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Asia-Pacific Journal of Operational Research
Journal Prestige (SJR): 0.477  Citation Impact (citeScore): 1 Number of Followers: 3  Hybrid journal (It can contain Open Access articles)ISSN (Print) 0217-5959 - ISSN (Online) 1793-7019 Published by World Scientific  [120 journals]
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- Consequences of Trade Regulations on International Trade in
Remanufacturing-
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Authors: Feng Fu, Shuangying Chen, Wei Yan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The remanufacturing industry is experiencing a gradual increase in international trade. Accordingly, manufacturers are encountering a multitude of regulations in this cross-border trade of remanufactured products, such as import prohibitions/bans, environmental regulations, and tariff barriers. In this paper, we investigated the implications of exporting remanufactured products to the international market with or without trade regulations. Our analysis reveals that, although the international market for remanufacturing invariably benefits the manufacturer, trade regulations are a disadvantage to remanufactured exports. Thus, while the quality of remanufactured products increases, the adverse effect of trade regulations could be weakened. Additionally, we reveal that trade regulations may be detrimental to the environment with a higher rate of used core collection and disposal impact. Thus, policymakers should take care to regulate the international market for remanufactured goods rather than implementing a one-size-fits-all solution.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-05-04T07:00:00Z
DOI: 10.1142/S0217595922500166
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- Optimal Strategies for A Dual-Channel Farming Supply Chain with Horizontal
Competition and Cooperation-
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Authors: Hongjun Peng, Wenting Sun, Tao Pang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider a dual-channel farming supply chain with two farmers and one distributor, where agriculture products produced by farmers have different quality levels. Farmers sell high-quality products to supermarkets and normal-quality products to small retail markets, respectively. Three scenarios are investigated: decentralized selling through the distributor to supermarkets (the DD mode); centralized selling through the distributor to supermarkets (the CD mode); centralized selling directly to supermarkets (the CS mode). Under the CS mode, farmers need to bear some extra sale cost such as inventory and transportation cost. We derive farmers’ optimal strategies of production effort and quality investment. It turns out that as farming scale expands, farmers’ production effort decreases, while quality investment increases. Moreover, two farmers’ quality investments are the highest under the CS mode and the least under the DD mode. Further analysis indicates that farmers’ total profits are generally the highest under the CS mode, but farmers obtain the highest profits under the CD mode if farmers’ extra sale cost under the CS mode exceeds a certain level. Therefore, to improve farmers’ welfare and agriculture products’ quality simultaneously, the CS mode may be the best choice in most cases, and it leads to a “win–win” situation for farmers and consumers.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-04-26T07:00:00Z
DOI: 10.1142/S0217595922500154
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- Parallel Machines Scheduling with Deteriorating Maintenance Activities and
Job Rejection-
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Authors: Juan Zou, Yu-Kang Sui, Jie Gao, Xian-Zhao Zhang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We consider parallel identical machines scheduling problems with deteriorating maintenance activities and the option of job rejection. Each machine has at most one deteriorating maintenance activity. The length of each maintenance activity increases linearly with its starting time. The location of the maintenance activity on each machine needs to be determined. The goal is to find the sequence of jobs to minimize scheduling cost; we further the model by allowing job rejection. A job is either accepted and processed on one of machines, or rejected. The goal is to determine the sequence of the accepted jobs to minimize scheduling cost of the accepted jobs plus total rejection penalty of the rejected jobs. When the scheduling cost is the makespan, we design a pseudo-polynomial time algorithm, a 2-approximation algorithm and a fully polynomial time approximation scheme. When the scheduling cost is the total completion time, we provide a polynomial time algorithm for the problem. When the scheduling costs are the total weighted completion time under the agreeable ratio assumption and the maximum lateness, we present pseudo-polynomial time algorithms to solve these problems, respectively.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-04-18T07:00:00Z
DOI: 10.1142/S0217595922400139
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- Submodular Maximization Subject to a Knapsack Constraint Under Noise
Models-
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Authors: Dung T. K. Ha, Canh V. Pham, Huan X. Hoang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The field of Submodular Maximization subject to a Knapsack constraint has recently expanded to a variety of application domains, which is facing some challenges such as data explosions or additional conditions. There exist plenty of objective functions that cannot be evaluated exactly in many real cases unless they are estimated with errors. It leads to solving the problem under noise models. Somewhat surprisingly, Submodular Maximization subject to a Knapsack constraint under Noise models ([math]) has never been discussed a lot before. Hence, in this paper, we consider the problem with two kinds of noise models which are addition and multiplication. Inspired by the traditional Greedy algorithm, we first propose a Greedy algorithm under Noises with provable theoretical bounds. In order to find the solution when input data are extremely large, we then devise an efficient streaming algorithm that scans only a single pass over the data and guarantees theoretical approximations. Finally, we conduct some experiments on Influence Maximization problem under knapsack constraint, an instance of [math] to show the performances of the proposed algorithms.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-04-18T07:00:00Z
DOI: 10.1142/S0217595922500130
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- Maximum Entropy Bi-Objective Model and its Evolutionary Algorithm for
Portfolio Optimization-
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Authors: Chun-An Liu, Qian Lei, Huamin Jia
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Diversification of investment is a well-established practice for reducing the total risk of investing. Portfolio optimization is an effective way for investors to disperse investment risk and increase portfolio return. Under the assumption of no short selling, a bi-objective minimizing portfolio optimization model, in which the first objective is a semi-absolute deviation mean function used to measure the portfolio risk, and the second objective is a maximum entropy smooth function used to measure the portfolio return, is given in this paper. Also, a maximum entropy multi-objective evolutionary algorithm is designed to solve the bi-objective portfolio optimization model. In order to obtain a sufficient number of uniformly distributed portfolio Pareto optimal solutions located on the true Pareto frontier and fully exploit the useful asset combination modes which can lead the search process toward the frontier direction quickly in the objective space, a subspace multi-parent uniform crossover operator and a subspace decomposition mutation operator are given. Furthermore, a normalization method to deal with the tight constraint and the convergence analysis of the proposed algorithm are also discussed. Finally, the performance of the proposed algorithm is verified by five benchmark investment optimization problems. The performance evaluations and results analyses illustrate that the proposed algorithm is capable of identifying good Pareto solutions and maintaining adequate diversity of the evolution population. Also, the proposed algorithm can obtain faster and better convergence to the true portfolio Pareto frontier compared with the three state-of-the-art multi-objective evolutionary algorithms. The result can also provide optimal portfolio plan and investment strategy for investors to allocate and manage asset effectively.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-04-11T07:00:00Z
DOI: 10.1142/S0217595922500142
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- Scheduling High Multiplicity Jobs on Parallel Multi-Purpose Machines with
Setup Times and Machine Available Times-
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Authors: Caixia Jing, Wanzhen Huang, Lei Zhang, Heng Zhang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider the scheduling of high multiplicity jobs on parallel multi-purpose machines with setup times and machine available times, with the objective of minimizing makespan. High multiplicity means that jobs are partitioned into several groups and in each group all jobs are identical. Whenever there is a switch from processing a job of one group to a job of another group, a setup time is needed. Multi-purpose machine implies that each job can only be processed by a specific subset of all the machines, called processing set. A mixed integer programming is formulated for this NP-hard problem. A heuristic is proposed to solve the problem. Lower bounds are developed to evaluate the heuristic algorithm. Extensive numerical computations are performed and the results show that the heuristic generates solutions with makespan within 2% above the lower bounds in average, and outperforms CPLEX 12.6 for large scale and complex problems.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-04-05T07:00:00Z
DOI: 10.1142/S0217595922500129
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- An Output-Space Based Branch-and-Bound Algorithm for Sum-of-Linear-Ratios
Problem-
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Authors: Bo Zhang, Yuelin Gao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Founded on the idea of subdividing the [math]-dimensional output space, a branch-and-bound algorithm for solving the sum-of-linear-ratios(SLR) problem is proposed. First, a two-stage equivalent transformation method is adopted to obtain an equivalent problem(EP) for the problem SLR. Second, by dealing with all nonlinear constraints and bilinear terms in EP and its sub-problems, a corresponding convex relaxation subproblem is obtained. Third, all redundant constraints in each convex relaxation subproblem are eliminated, which leads to a linear programming problem with smaller scale and fewer constraints. Finally, the theoretical convergence and computational complexity of the algorithm are demonstrated, and a series of numerical experiments illustrate the effectiveness and feasibility of the proposed algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-03-24T07:00:00Z
DOI: 10.1142/S0217595922500105
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- Multiobjective Symmetric Duality in Higher-Order Fractional Variational
Programming-
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Authors: Arshpreet Kaur, Mahesh Kumar Sharma, Izhar Ahmad
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We introduce new classes of higher-order functional, termed higher-order [math]convex and higher-order [math]convex functionals. These classes are illustrated by nontrivial examples. A pair of higher-order multiobjective symmetric fractional variational programs with cone constraints and fixed boundary conditions is formulated. Appropriate duality results are discussed utilizing the aforementioned assumptions. The results in this paper are generalizations of the results already existing in literature.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-03-10T08:00:00Z
DOI: 10.1142/S0217595922500087
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- Online Batch Scheduling of Incompatible Job Families with Variable
Lookahead Interval-
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Authors: Wenhua Li, Libo Wang, Hang Yuan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We consider online parallel-batch scheduling of [math] incompatible unit-length job families with variable lookahead interval to minimize the maximum completion time, where [math] is the number of job families which is known in advance. Incompatible job family means that a batch only contains the jobs from the same job family. At any time [math], an online algorithm can foresee the information of the jobs arriving in the time interval [math], where [math] is variable. When the batch capacity [math] and [math], we provide a best possible online algorithm with competitive ratio [math] for [math] and [math], where [math] is a positive root of [math]. When the batch capacity [math] and [math], we give an online algorithm with competitive ratio [math] for [math] and [math], and prove that it is the best possible for [math] and [math].
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-03-08T08:00:00Z
DOI: 10.1142/S0217595922400127
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- Critical Interactive Risks in Project Portfolios from the Life Cycle
Perspective-
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Authors: Libiao Bai, Jiale Liu, Ning Huang, Kanyin Zheng, Tingting Hao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-03-08T08:00:00Z
DOI: 10.1142/S0217595922500075
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- Scalarization and Optimality Conditions of [math]-Globally Proper
Efficient Solution for Set-Valued Equilibrium Problems-
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Authors: Zhi-Ang Zhou, Min Kuang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, our purpose is to use the improvement set to investigate the scalarization and optimality conditions of [math]-globally proper efficient solution for the set-valued equilibrium problems with constraints. First, the notion of [math]-globally proper efficient solution for set-valued equilibrium problems with constraints is introduced in locally convex Hausdorff topological spaces. Second, the linear scalarization theorems of [math]-globally proper efficient solution are derived. Finally, under the assumption of nearly [math]-subconvexlikeness, the Kuhn–Tucker and Lagrange optimality conditions for set-valued equilibrium problems with constraints are obtained in the sense of [math]-globally proper efficiency. Meanwhile, we give some examples to illustrate our results. The results obtained in this paper improve and generalize some known results in the literature.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-02-28T08:00:00Z
DOI: 10.1142/S0217595922500099
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- Least-Distance Range Adjusted Measure in DEA: Efficiency Evaluation and
Benchmarking for Japanese Banks-
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Authors: Xu Wang, Takashi Hasuike
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This study aims to formulate the least-distance range adjusted measure (LRAM) in data envelopment analysis (DEA) and apply it to evaluate the relative efficiency and provide the benchmarking information for Japanese banks. In DEA, the conventional range adjusted measure (RAM) acts as a well-defined model that satisfies a set of desirable properties. However, because of the practicality of the least-distance measure, we formulate the LRAM and propose the use of an effective mixed integer programming (MIP) approach to compute it in this study. The formulated LRAM (1) satisfies the same desirable properties as the conventional RAM, (2) provides the least-distance benchmarking information for inefficient decision-making units (DMUs), and (3) can be computed easily by using the proposed MIP approach. Here, we apply the LRAM to a Japanese banking data set corresponding to the period 2017–2019. Based on the results, the LRAM generates higher efficiency scores and allows inefficient banks to improve their efficiency with a smaller extent of input–output modification than that required by the RAM, thereby indicating that the LRAM can provide more easy-to-achieve benchmarking information for inefficient banks. Therefore, from the perspective of the managers of DMUs, this study provides a valuable LRAM for efficiency evaluation and benchmarking analysis.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-02-22T08:00:00Z
DOI: 10.1142/S0217595922500063
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- Parallel-Machine Scheduling with Step-Deteriorating Jobs to Minimize the
Total (Weighted) Completion Time-
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Authors: Cuixia Miao, Fanyu Kong, Juan Zou, Ran Ma, Yujia Huo
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider the parallel-machine scheduling with step-deteriorating jobs. The actual processing time of each job deteriorates as a step function if its starting time is beyond a given deteriorating date. We focus on the case of the common job deteriorating date. For the minimization problem of total completion time, we first show that the problem is NP-hard in the strong sense. Then we propose one property of any optimal schedule. Furthermore, we prove that two special cases of common normal processing time or common penalty are polynomially solvable. For the minimization problem of total weighted completion time, we analyze the NP-hardness and present a polynomial time optimal algorithm for the case of common normal processing time and common penalty.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-02-14T08:00:00Z
DOI: 10.1142/S0217595922400115
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- Effective Heuristic Techniques for Combined Robust Clustering Problem
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Authors: Yunhe Xu, Chenchen Wu, Ling Gai, Lu Han
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Clustering is one of the most important problems in the fields of data mining, machine learning, and biological population division, etc. Moreover, robust variant for [math]-means problem, which includes [math]-means with penalties and [math]-means with outliers, is also an active research branch. Most of these problems are NP-hard even the most classical problem, [math]-means problem. For the NP-hard problems, the heuristic algorithm is a powerful method. When the quality of the output can be guaranteed, the algorithm is called an approximation algorithm. In this paper, combining two types of robust settings, we consider [math]-means problem with penalties and outliers ([math]-MPO). In the [math]-MPO, we are given an [math]-point set [math], a penalty cost [math] for each [math], an integer [math], and an integer [math]. The target is to find a center subset [math] with [math], a penalty subset [math] and an outlier subset [math] with [math], such that the sum of the total costs, including the connection cost and the penalty cost, is minimized. We offer an approximation algorithm using a heuristic local search scheme. Based on a single-swap manipulation, we obtain [math]-approximation algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-02-07T08:00:00Z
DOI: 10.1142/S0217595922400097
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- Approximation Algorithms for Matroid and Knapsack Means Problems
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Authors: Ao Zhao, Qian Liu, Yang Zhou, Min Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we concentrate on studying the [math]-means problem with a matroid or a knapsack constraint. In the matroid means problem, given an observation set and a matroid, the goal is to find a center set from the independent sets to minimize the cost. By using the linear programming [math]-rounding technology, we obtain a constant approximation guarantee. For the knapsack means problem, we adopt a similar strategy to that of matroid means problem, whereas the difference is that we add a knapsack covering inequality to the relaxed LP in order to decrease the unbounded integrality gap.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-01-28T08:00:00Z
DOI: 10.1142/S0217595922400073
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- A Variational Inequality-Based Location-Allocation Algorithm for Locating
Multiple Interactive Facilities-
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Authors: Yan Gu, Jianlin Jiang, Liyun Ling, Yibing Lv, Su Zhang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Multi-source Weber problem (MWP) is an important model in facility location, which has wide applications in various areas such as health service management, transportation system management, urban planning, etc. The location-allocation algorithm is a well-known method for solving MWP, which consists of a location phase and an allocation phase at each iteration. In this paper, we consider more general and practical case of MWP–the constrained multi-source location problem (CMSLP), i.e., the location of multiple facilities with considering interactive transportation between facilities, locational constraints on facilities and the gauge for measuring distances. A variational inequality approach is contributed to solving the location subproblem called the constrained multi-facility location problem (CMFLP) in location phase, which leads to an efficient projection-type method. Then a new location-allocation algorithm is developed for CMSLP. Global convergence of the projection-type method as well as local convergence of new location-allocation algorithm are proved. The efficiency of proposed methods is verified by some preliminary numerical results.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-01-15T08:00:00Z
DOI: 10.1142/S0217595922400103
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- Workload Balancing Among Heathcare Workers Under Uncertain Service Time
Using Distributionally Robust Optimization-
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Authors: Nguyen Duy Anh
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Healthcare systems are facing serious challenges in balancing their human resources to cope with volatile service demand, while at the same time providing necessary job satisfaction to the healthcare workers. In this paper, we propose a distributionally robust optimization formulation to generate a task assignment plan that promotes the fairness in allocation, attained by reducing the difference in the total working time among workers, under uncertain service time. The proposed joint chance constraint model is conservatively approximated by a worst-case Conditional Value-at-Risk, and we devise a sequential algorithm to solve the finite-dimensional reformulations which are linear (mixed-binary) optimization problems. We also provide explicit formula in the situation where the support set of the random vectors is a hyperrectangle. The experiment with both synthetic and real data indicates promising results for our distributionally robust optimization approach.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-01-12T08:00:00Z
DOI: 10.1142/S0217595921500457
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- Approximation Algorithms for the Capacitated Min–Max Correlation
Clustering Problem-
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Authors: Sai Ji, Jun Li, Zijun Wu, Yicheng Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we propose a so-called capacitated min–max correlation clustering model, a natural variant of the min–max correlation clustering problem. As our main contribution, we present an integer programming and its integrality gap analysis for the proposed model. Furthermore, we provide two approximation algorithms for the model, one of which is a bi-criteria approximation algorithm and the other is based on LP-rounding technique.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-01-04T08:00:00Z
DOI: 10.1142/S0217595922400085
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- Slack Due-Window Assignment Scheduling Problem with Deterioration Effects
and a Deteriorating Maintenance Activity-
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Authors: Xue Jia, Dan-Yang Lv, Yang Hu, Ji-Bo Wang, Zhi Wang, Ershen Wang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper studies the slack due-window assignment scheduling problem with deterioration effects and a deterioration maintenance activity on a single-machine. The machine deteriorates during the machining process, and at a certain moment performs a deterioration maintenance activity, that is, the duration time of the maintenance activity is a linear function of the maintenance starting time. It is needed to make a decision on when to schedule the deteriorating maintenance activity, the optimal common flow allowances and the sequence of jobs to minimize the weighted penalties for the sum of earliness and tardiness, weighted number of early and delayed, and weighted due-window starting time and size. This paper proposes a polynomial time algorithm to solve this problem.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2022-01-04T08:00:00Z
DOI: 10.1142/S0217595922500051
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- Does Gold Still Shelter Inflation, and, if so, When' Evidence From
Four Countries-
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Authors: Mohd Fahmi Ghazali, Nurul Fasyah Mohd Ussdek, Hooi Hooi Lean, Jude W. Taunson
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This study investigates gold as a hedge or a safe haven against inflation in four countries. We propose two standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. Gold exhibits considerable evidence of a strong hedge in the US and China. Nevertheless, gold provides shelter at different times and not consistently across countries. With regards to be a safe haven, gold retains its status as a key investment in China. On the other hand, gold only plays a minor role in the UK and India. These findings indicate that gold can secure Chinese investment during the high inflationary periods, while gold is a profitable asset to hold over a long period of time in the US. In contrast, UK and Indian investors should hold a well-diversified portfolio for sustainable return and protection from purchasing power loss.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-23T08:00:00Z
DOI: 10.1142/S0217595922400036
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- Online-Retail Supply Chain Optimization with Credit Period and Selling
Price-Dependent Demand-
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Authors: Xu Chunming, Wang Changlong, Ren Jie, Kang Linyao, Du Donglei
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Credit payment strategies have been implemented widely in the online retail industry. This work studies an online-retail supply chain involving credit period and selling price-dependent demands. The participants of the supply chain form a Stackelberg game where the supplier as a follower sells products to the customers through an online platform provider, who as a leader provides a credit period to customers and charges the supplier based on the quantity of goods sold. We study and compare the supply chains when the online platform provider adopts the cash payment and credit payment strategies, respectively, to investigate the effects of the credit period, the selling price and the default risk on supply chain system performance. We also investigate these supply chains under both the centralized and decentralized settings and provide an example to illustrate a simple allocation mechanism to coordinate the decentralized supply chain. Finally, an extension of the supply chain with credit payment is given.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-23T08:00:00Z
DOI: 10.1142/S0217595922400048
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- Nonsubmodular Constrained Profit Maximization in Attribute Networks
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Authors: Liman Du, Wenguo Yang, Suixiang Gao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-23T08:00:00Z
DOI: 10.1142/S0217595922400061
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- Distributionally Robust Joint Chance Constrained Vessel Fleet Deployment
Problem-
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Authors: Feifeng Zheng, Zhaojie Wang, E. Zhang, Ming Liu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This work investigates the problem of vessel fleet deployment for liner shipping. The objective is to minimize the total cost, i.e., the sum of vessel chartering cost and vessel-route operating cost. In the considered problem, the shipment demand for each route is uncertain and its distribution is unknown. Due to lacking historical data, we use the moment-based ambiguity set to characterize the unknown distributions of demands. We then introduce a distributionally robust model and propose a new approximation approach to solve this problem. Finally, numerical experiments are conducted to demonstrate the performance of our approximation approach.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-20T08:00:00Z
DOI: 10.1142/S021759592250004X
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- Heuristics for Finding Sparse Solutions of Linear Inequalities
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Authors: Yichen Yang, Zhaohui Liu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider the problem of finding a sparse solution, with a minimal number of nonzero components, for a set of linear inequalities. This optimization problem is combinatorial and arises in various fields such as machine learning and compressed sensing. We present three new heuristics for the problem. The first two are greedy algorithms minimizing the sum of infeasibilities in the primal and dual spaces with different selection rules. The third heuristic is a combination of the greedy heuristic in the dual space and a local search algorithm. In numerical experiments, our proposed heuristics are compared with the weighted-[math] algorithm and DCA programming with three different non-convex approximations of the zero norm. The computational results demonstrate the efficiency of our methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-15T08:00:00Z
DOI: 10.1142/S021759592240005X
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- Majorized iPADMM for Nonseparable Convex Minimization Models with
Quadratic Coupling Terms-
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Authors: Yumin Ma, Ting Li, Yongzhong Song, Xingju Cai
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider nonseparable convex minimization models with quadratic coupling terms arised in many practical applications. We use a majorized indefinite proximal alternating direction method of multipliers (iPADMM) to solve this model. The indefiniteness of proximal matrices allows the function we actually solved to be no longer the majorization of the original function in each subproblem. While the convergence still can be guaranteed and larger stepsize is permitted which can speed up convergence. For this model, we analyze the global convergence of majorized iPADMM with two different techniques and the sublinear convergence rate in the nonergodic sense. Numerical experiments illustrate the advantages of the indefinite proximal matrices over the positive definite or the semi-definite proximal matrices.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-12-13T08:00:00Z
DOI: 10.1142/S0217595922400024
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- A Novel Two-Stage Game Model for Pricing Cloud/Fog Computing Resource in
Blockchain Systems-
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Authors: Jinmian Chen, Yukun Cheng, Zhiqi Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Cloud/fog computing resource pricing is a new paradigm in the blockchain mining scheme, as the participants would like to purchase the cloud/fog computing resource to speed up their mining processes. In this paper, we propose a novel two-stage game to study the optimal price-based cloud/fog computing resource management, in which the cloud/fog computing resource provider (CFP) is the leader, setting the resource price in Stage I, and the mining pools act as the followers to decide their demands of the resource in Stage II. Since mining pools are bounded rational in practice, we model the dynamic interactions among them by an evolutionary game in Stage II, in which each pool pursues its evolutionary stable demand based on the observed price, through continuous learning and adjustments. Backward induction method is applied to analyze the sub-game equilibrium in each stage. Specifically in Stage II, we first build a general study framework for the evolutionary game model, and then provide a detailed theoretical analysis for a two-pool case to characterize the conditions for the existence of different evolutionary stable solutions. Referring to the real world, we conduct a series of numerical experiments, whose results validate our theoretical findings for the case of two mining pools. Additionally, the impacts from the size of mining block, the unit transaction fee and the price of token on the decision makings of participants are also discussed.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-20T08:00:00Z
DOI: 10.1142/S0217595922400012
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- A Global Convergence Analysis for Computing a Symmetric Low-Rank
Orthogonal Approximation-
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Authors: Wenxin Du, Shenglong Hu, Youyicun Lin, Jie Wang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we present a refined convergence analysis for a simple yet powerful method for computing a symmetric low-rank orthogonal approximation of a symmetric tensor proposed in the literature. The significance is that the assumption guaranteeing the global convergence is vastly relaxed to only on an input parameter of this algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-20T08:00:00Z
DOI: 10.1142/S0217595922500038
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- Bicriteria Common Flow Allowance Scheduling with Aging Effect, Convex
Resource Allocation, and a Rate-Modifying Activity on a Single Machine-
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Authors: Xiaoli Zhao, Jian Xu, Ji-Bo Wang, Lin Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We consider a single machine scheduling problem with slack due date assignment in which the actual processing time of a job is determined by its position in a sequence, its resource allocation function, and a rate-modifying activity simultaneously. The problem is to determine the optimal job sequence, the optimal common flow allowance, the optimal amount of the resource allocation, and the position of the rate-modifying activity such that the two constrained optimization objective cost functions are minimized. One is minimizing the total penalty cost containing the earliness, tardiness, common flow allowance subject to an upper bound on the total resource cost, the other is minimizing the total resource cost subject to an upper bound on the total penalty cost. For two optimization problems, we show that they can be solved in optimal time, respectively.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-09T08:00:00Z
DOI: 10.1142/S0217595921500469
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- Fast Algorithms for LS and LAD-Collaborative Regression
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Authors: Jun Sun, Lingchen Kong, Mei Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss–Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-07T02:52:45Z
DOI: 10.1142/S0217595922500014
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- Asymptotic Analysis for a Stochastic Second-Order Cone Programming and
Applications-
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Authors: Jie Zhang, Yue Shi, Mengmeng Tong, Siying Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Stochastic second-order cone programming (SSOCP) is an extension of deterministic second-order cone programming, which demonstrates underlying uncertainties in practical problems arising in economics engineering and operations management. In this paper, asymptotic analysis of sample average approximation estimator for SSOCP is established. Conditions ensuring the asymptotic normality of sample average approximation estimators for SSOCP are obtained and the corresponding covariance matrix is described in a closed form. Based on the analysis, the method to estimate the confidence region of a stationary point of SSOCP is provided and three examples are illustrated to show the applications of the method.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-06T07:00:00Z
DOI: 10.1142/S0217595922500026
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- Optimal Sequential Investment Decision-Making with Jump Risk
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Authors: I-Ming Jiang, Yu-Hong Liu, Sutee Pakavaleetorn
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper uses the real-option approach to find the optimal amount and time of investment in a real project. This approach is better than the net present value approach because it captures the uncertainty in the future expected cash flow. The real option has been applied to many industries; for example, oil and gas, telecommunications, and large-scale energy projects. The model introduced in this paper adds two features to the standard real options. The first feature is the two-stage investment. In reality, it is common for the project manager to not invest all budget at once. The second feature is the early termination event. The finding is that such a sudden death event causes the waiting period to be longer for both one-stage and two-stage investments. With low volatility, the lower optimal investment proportion of the total project budget is expected, when the mean arrival rate of the jump downside risk is higher. With medium or higher volatility, the higher optimal proportion is associated with a higher probability of jump risk. When the rate of one-time event is constant, volatility increases the optimal investment amount and increases the waiting time. The project has more value when the investment is divided into two stages rather than one stage. All other things being equal, the waiting time of one-stage is shorter than the waiting time of the first stage but longer than that of the second stage.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-03T07:00:00Z
DOI: 10.1142/S0217595921400352
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- Optimality of Approximate Quasi-Weakly Efficient Solutions for Vector
Equilibrium Problems via Convexificators-
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Authors: Guolin Yu, Siqi Li, Xiao Pan, Wenyan Han
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper is devoted to the investigation of optimality conditions for approximate quasi-weakly efficient solutions to a class of nonsmooth Vector Equilibrium Problem (VEP) via convexificators. First, a necessary optimality condition for approximate quasi-weakly efficient solutions to problem (VEP) is presented by making use of the properties of convexificators. Second, the notion of approximate pseudoconvex function in the form of convexificators is introduced, and its existence is verified by a concrete example. Under the introduced generalized convexity assumption, a sufficient optimality condition for approximate quasi-weakly efficient solutions to problem (VEP) is also established. Finally, a scalar characterization for approximate quasi-weakly efficient solutions to problem (VEP) is obtained by taking advantage of Tammer’s function.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-11-03T07:00:00Z
DOI: 10.1142/S0217595921500470
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- General Inexact Primal-Dual Hybrid Gradient Methods for Saddle-Point
Problems and Convergence Analysis-
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Authors: Zhongming Wu, Min Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we focus on the primal-dual hybrid gradient (PDHG) method, which is being widely used to solve a broad spectrum of saddle-point problems. Despite of its wide applications in different areas, the study of inexact versions of PDHG still seems to be in its infancy. We investigate how to design implementable inexactness criteria for solving the subproblems in PDHG scheme so that the convergence of an inexact PDHG can be guaranteed. We propose two specific inexactness criteria and accordingly some inexact PDHG methods for saddle-point problems. The convergence of both inexact PDHG methods is rigorously proved, and their convergence rates are estimated under different scenarios. Moreover, some numerical results on image restoration problems are reported to illustrate the efficiency of the proposed methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-10-25T07:00:00Z
DOI: 10.1142/S0217595921500445
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- Copula Approach to Multivariate Energy Efficiency Analysis
-
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Authors: Mervenur Sözen, Mehmet Ali Cengiz
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-10-02T07:00:00Z
DOI: 10.1142/S0217595921500421
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- On Exchange Methods for Nonlinear Semi-Infinite Programs
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Authors: Liping Zhang, Shouqiang Du
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
A new exchange method is presented for semi-infinite optimization problems with polyhedron constraints. The basic idea is to use an active set strategy as exchange rule to construct an approximate problem with finitely many constraints at each iteration. Under mild conditions, we prove that the proposed algorithm terminates in a finite number of iterations and guarantees that the solution of the resulting approximate problem at final iteration converges to the solution of the original problem within arbitrarily given tolerance. Numerical results indicate that the proposed algorithm is efficient and promising.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-09-28T07:00:00Z
DOI: 10.1142/S0217595921500433
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- Single-Machine Due-Window Assignment Scheduling with Resource Allocation
and Generalized Earliness/Tardiness Penalties-
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Authors: Yu Tian
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this study, the due-window assignment single-machine scheduling problem with resource allocation is considered, where the processing time of a job is controllable as a linear or convex function of amount of resource allocated to the job. Under common due-window and slack due-window assignments, our goal is to determine the optimal sequence of all jobs, the due-window start time, due-window size, and optimal resource allocation such that a sum of the scheduling cost (including weighted earliness/tardiness penalty, weighted number of early and tardy job, weighted due-window start time, and due-window size) and resource consumption cost is minimized. We analyze the optimality properties, and provide polynomial time solutions to solve the problem under four versions of due-window assignment and resource allocation function.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-09-08T07:00:00Z
DOI: 10.1142/S021759592150041X
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- A Computational Approach to Optimal Control Problems with Almost Smooth
Controls-
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Authors: Ying Zhang, Zhao Zhang, Yingtao Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we consider a class of optimal control problems involving continuous control and state inequality constraints where the control is almost smooth. We first employ the control parametrization technique via approximating the control signal by a piecewise linear function. Then, we develop a time scaling transformation procedure for transforming the approximate problem into an equivalent problem that can be solved readily using conventional methods. On this basis, a novel exact penalty function method is constructed by appending penalized constraint violations to the cost function. The gradient formulas and convergent properties ensure that the transformed unconstrained optimal parameter selection problems can be solved by existing optimization algorithms or software packages. Finally, an example is solved showing the effectiveness and applicability of the approach proposed.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S0217595921400340
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- Demand Information Sharing in the Presence of B2B Spot Market
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Authors: Shanshan Ma, Liyan Wang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Companies have started to use a business-to-business (B2B) spot market in combination with their traditional long-term procurement contracts to procure intermediate goods. This study investigates whether or not the market players should share their demand forecast information with one another in a supplier — manufacturer supply chain and assess the benefits of sharing information in the presence of the B2B spot market. First, the supplier and manufacturer make forecast on the demand, and during this period they select an information-sharing arrangement, that is, whether to share information or not. Then, the supplier sets the wholesale price and the manufacturer submits an order after observing the wholesale price. Both the supplier and manufacturer can trade their intermediate goods in a B2B spot market. We find that the manufacturer can infer the supplier’s demand forecast from the wholesale price in the non-information-sharing case, but the supplier cannot enjoy such an advantage. We also find that information sharing benefits both the supplier and the manufacturer, if and only if demand and spot price are positively correlated and the supplier’s expectation of the manufacturer’s forecast is medium. By contrast, obtaining more demand forecast information can hurt supply chain players. Information sharing benefits the manufacturer but hurts the supplier when the supplier’s expectation of the manufacturer’s forecast is high. However, when the supplier’s expectation of the manufacturer’s forecast is low, information sharing benefits the supplier but hurts the manufacturer.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S0217595921500329
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- Two-Stage Heuristic Algorithm Proposal for Urban E-Commerce Deliveries
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Authors: Mehmet Karaoğlu, Gökhan Kara
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In today’s intensive competition conditions, it is an inevitable necessity for the companies operating in the e-commerce sector to manage their physical distribution processes efficiently and effectively. The most important step in e-commerce logistics processes is the last step delivery operations in the city. In order for these operations to be carried out quickly and efficiently, delivery vehicles and personnel must be optimally led. These problems, referred to as vehicle routing problem (VRP) in the literature, include determining the least cost routes that vehicles will cover to meet customer needs. In this study, the urban delivery problems of the enterprises operating in the online retail sector are examined. In line with these problems, VRP for urban e-commerce deliveries has been modified; open, multi-depot, distribution aggregation, time-window VRP is discussed. A mathematical model for the modified VRP has been developed and a new heuristic algorithm consisting of two stages has been developed for large-scale problems. The developed algorithm was tested on three different cases. The results are compared with the solution of the nearest neighbor method and the performance of the proposed algorithm is presented.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S0217595921500342
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- An Iterated Local Search Heuristic for the Staff Scheduling Problem for
Part-Time Employees in Japan-
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Authors: Wei Wu, Naoaki Katoh, Atsuko Ikegami
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we introduce a mathematical programming model for solving a staff scheduling problem based on one-day duties (task patterns) of individual staff members. The model can accommodate various service types, management policies, and staff preferences. We first enumerate all feasible one-day duties and propose an iterated local search approach that incorporates various methodologies, including a size-reduction method and a very large-scale neighborhood search. For the very large-scale neighborhood search, we design a dynamic programming method that aims to find the most improved schedule and can be used in the rescheduling stage. Computational results show that the model and the proposed algorithm perform well for real-world instances in Japan.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S0217595921500378
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- Approximation Algorithms for Non-Submodular Optimization Over Sliding
Windows-
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Authors: Yunxin Luo, Chenchen Wu, Chunming Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, the problem we study is how to maximize a monotone non-submodular function with cardinality constraint. Different from the previous streaming algorithms, this paper mainly considers the sliding window model. Based on the concept of diminishing-return ratio [math], we propose a [math]-approximation algorithm with the memory [math], where [math] is the ratio between maximum and minimum values of any singleton element of function [math]. Then, we improve the approximation ratio to [math] through the sub-windows at the expense of losing some memory. Our results generalize the corresponding results for the submodular case.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S021759592150038X
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- Cross-Efficiency Evaluation Method Taking Management Objectives as
Reference Points from Peer Perspective-
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Authors: Hai-Liu Shi, Sheng-Qun Chen, Ying-Ming Wang, Yan Huang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
According to the management by objectives (MBO) theory, performance evaluations should take into account the achievement of management objectives (MOs). The most commonly used performance evaluation method — the cross-efficiency evaluation method — seldom considers the role of MOs as a reference point. According to the prospect theory, decision-makers underestimate the benefits that exceed the reference point; they also exaggerate the losses that fall below the reference point. This irrational psychology is more obvious when evaluating the performance evaluation of peers. As such, this paper proposes a cross-efficiency evaluation method based on prospect theory, which takes MOs as a reference point from a peer perspective. First, taking MOs as reference points, a decision-making unit (DMU) chooses a set of weights for each peer, in order to maximize or minimize the prospects of the peer, according to the benevolent or aggressive attitude of the DMU. In order to improve the adaptability of the method, the precise number of MOs is further extended to be an interval number. Finally, the relationship between models, which are based on precise MOs and interval MOs, is illustrated by propositions. Finally, numerical examples are provided to illustrate the applications of the proposed cross-efficiency evaluation method.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-31T07:00:00Z
DOI: 10.1142/S0217595921500408
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- A Replenishment Inventory Model with a Stock-Dependent Demand and
Age–Stock-Dependent Cost Functions in a Random Environment-
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Authors: Yonit Barron
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper investigates an [math] continuous-review perishable inventory model with a stock-dependent Poisson demand process, full backordering (with an extension for lost sales) and uncertainty in lead time and shelf life. Four types of costs are considered: a fixed cost of an order and each outdated item; age-dependent costs of an item (i.e., holding and salvage costs), given by a function of its remaining shelf life; and a delay cost of a backlogged demand unit, which is a function of its delay duration. Applying the supplementary variable technique, we obtain the joint probability-density function of the number of items in the system and the remaining time and thereby obtain the optimal parameters minimizing the long-run average total cost. Numerical experiments show that supply chain profits are enhanced by integrating the age components into replenishment decisions, and ignoring the shelf age- and delay-dependent costs may result in a substantial loss (up to 25%). It further appeared that estimating the lead-time distribution by an exponential one is significantly more costly, in particular as the c.v. differs from 1. In contrast, an exponential shelf life may provide a good heuristic for other shelf-life distributions.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-20T07:00:00Z
DOI: 10.1142/S0217595921500354
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- Iterative Multi-Attribute Procurement Auction with Decision Support for
Bid Formulation-
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Authors: T. G. Chetan, Mamata Jenamani, S. P. Sarmah
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-16T07:00:00Z
DOI: 10.1142/S0217595921500366
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- An Ordinal Weighted EDM Model for Nonmetric Multidimensional Scaling
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Authors: Qing-Na Li, Chi Zhang, Mengzhi Cao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Multidimensional scaling (MDS) is to recover a set of points by making use of noised pairwise Euclidean distances. In some situations, the observed Euclidean distances may contain large errors or even missing values. In such cases, the order of the distances is far more important than their magnitude. Non-metric multidimensional scaling (NMDS) is then to deal with this problem by taking use of the ordinal information. The challenge of NMDS is to tackle the large number of ordinal constraints on distances (for [math] points, this will be of [math]), which will slow down existing numerical algorithms. In this paper, we propose an ordinal weighted Euclidean distance matrix model for NMDS. By designing an ordinal weighted matrix, we get rid of the large number of ordinal constraints and tackle the ordinal constraints in a soft way. We then apply our model to image ranking. The key insight is to view the image ranking problem as NMDS in the kernel space. We conduct extensive numerical test on two state-of-the-art datasets: FG-NET aging dataset and MSRA-MM dataset. The results show the improvement of the proposed approach over the existing methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-06T07:00:00Z
DOI: 10.1142/S0217595921500330
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- Proximal Gradient-Type Algorithms for a Class of Bilevel Programming
Problems-
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Authors: Dan Li, Shuang Chen, Li-Ping Pang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
A class of proximal gradient-type algorithm for bilevel nonlinear nondifferentiable programming problems with smooth substructure is developed in this paper. The original problem is approximately reformulated by explicit slow control technique to a parameterized family function which makes full use of the information of smoothness. At each iteration, we only need to calculate one proximal point analytically or with low computational cost. We prove that the accumulation iterations generated by the algorithms are solutions of the original problem. Moreover, some results of complexity of the algorithms are presented in convergence analysis. Numerical experiments are implemented to verify the efficiency of the proximal gradient algorithms for solving this kind of bilevel programming problems.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-08-06T07:00:00Z
DOI: 10.1142/S0217595921500391
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- Study on Agent Incentives for Resource Sharing on P2P Networks
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Authors: Yukun Cheng, Xiaotie Deng, Yuhao Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
There have recently been extensive studies on proportional response protocol, which is motivated by the successful BitTorrent system for file sharing over a P2P network. The proportional response protocol has been proved to be strategy-proof against weight cheating attacks and edge cheating attacks, in order to allocate a single type of resource on P2P networks. This strategy-proof property holds due to an elegant combinatorial structure: the bottleneck decomposition of the underlying network structure, and the utility function, defined as the total resources that one agent receives from its neighbors. However, Sybil attacks, under which an agent may form several fictitious players and split its resource among them, have been shown as a more difficult attack to defend against, and thus a strategic agent playing Sybil attacks may result in personal gain. Previous efforts have been made to show that an agent may generate a gain, but with limited gains by Sybil attacks on several special networks, including trees, cliques, and rings. This paper is the first to study the agent’s incentives by adopting a Sybil attack on general networks. The main contribution is to prove that any agent cannot obtain more than three times as much as the revenue when it plays honestly.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-07-16T07:00:00Z
DOI: 10.1142/S0217595921500317
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- Parallel Machine Scheduling with Due Date-to-Deadline Window, Order
Sharing and Time Value of Money-
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Authors: Yinfeng Xu, Rongteng Zhi, Feifeng Zheng, Ming Liu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Motivated by a variety of applications in sharing economy, we study an identical parallel machine scheduling problem with due date-to-deadline window by jointly considering machine sharing and the time value of money. A factory owns a set of parallel identical machines and processes a set of production orders within a finite time period. In the sharing setting, the factory may also rent external machines to handle a part of orders by paying some extra cost. The factory aims to determine the sharing policy of the production orders and the scheduling rule of machines, to maximize its total future value of profits by satisfying the orders. To the best of our knowledge, there are no previous results for this problem. In this work, a mathematical programming model is derived, and a problem-specific genetic algorithm and a heuristic are proposed to solve large-scale instances. Numerical experiments using randomly generated instances are carried out to evaluate the effectiveness and efficiency of the proposed solution methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-28T07:00:00Z
DOI: 10.1142/S021759592150024X
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- A Bicriteria Approach for Saving a Path Maximizing Dynamic Contraflow
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Authors: Hari Nandan Nath, Stephan Dempe, Tanka Nath Dhamala
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The maximum dynamic contraflow problem in transportation networks seeks to maximize the flow from a source to a sink within a given time horizon with a possibility of arc reversals. This may result into blockage of paths of desired length from some node of the network towards the source. In some cases such as the evacuation planning, we may require a path towards the source to move some facilities, for example, emergency vehicles. In this work, we model the problem of saving such a path as a bicriteria optimization problem which minimizes the length of the path and maximizes the dynamic flow with arc reversals. We use the [math]-constraint approach to solve the problem and propose a procedure that gives the set of all Pareto optimal solutions in a single-source-single-sink network with integer inputs. We also present computational performance of the algorithm on a road network of Kathmandu city, and on randomly generated networks. The results are of both theoretical and practical importance.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-28T07:00:00Z
DOI: 10.1142/S0217595921500275
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- Efficiency Loss and Coordination in the Online Shopping Supply Chain with
Competitive Shipping Companies-
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Authors: Yihong Hu, Qiang Qiang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper studies efficiency loss and coordination mechanism in a supply chain with one online retailer and multiple competitive shipping companies in the presence of congestion effects. We build a three-level game between customers, shipping companies and the retailer. The equilibrium market structure is determined. The optimal volume–investment ratio for each shipping company is the same for both centralized and decentralized supply chains, and it is dependent on the delivery time function, independent of competitors’ decisions. The efficiency loss of the decentralized supply chain with one retailer and one shipping company is found to be 1/4, independent of the delivery time function and the demand function. The loss is reduced when competition is introduced into shipping companies and an upper bound of efficiency loss with multiple homogeneous shipping companies is derived. Revenue-sharing contracts may be designed to allow the decentralized supply chain to perform as well as a centralized one. The necessity of coordination is reduced when competition is introduced and the number of competitive companies increases. Finally, we extend the model to consider heterogeneous shipping companies and find that the disparity between shipping companies increases the efficiency loss because in the decentralized supply chain less efficient shipping companies also provide service. This research explicates the relationship options between e-commerce retailers and shipping companies, providing managerial insights for industry practitioners.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-21T07:00:00Z
DOI: 10.1142/S0217595921500251
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- On the Quasiconcave Multilevel Programming Problems
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Authors: H. Sadeghi, M. Esmaeili
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Multilevel programming appears in many decision-making situations. Investigation of the main properties of quasiconcave multilevel programming (QCMP) problems, to date, is limited to bilevel programming (only two levels). In this paper, first, we present an extension of the properties of quasiconcave bilevel programming (QCBP) problems for the case when three levels exist. Then, by induction on [math] (the number of levels), we prove the existence of an extreme point of the polyhedral constraint region that solves the QCMP problem under given conditions. Ultimately, a number of numerical examples are illustrated to verify the results.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-21T07:00:00Z
DOI: 10.1142/S0217595921500263
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- An Efficient Elite-Based Simulation–Optimization Approach for Stochastic
Resource Allocation Problems in Manufacturing and Service Systems-
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Authors: Chun-Chih Chiu, James T. Lin
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Stochastic resource allocation problems (SRAPs) involve determining the optimal configuration of a limited resource to achieve an objective function under given constraints and random effects in manufacturing systems (MSs) and service systems (SSs). The problems are traditionally solved by determining the optimal solution. It is generally preferable to determine as many global optima as possible, or at least a small set of diverse but good candidates, to help the decision-maker rapidly adopt alternative solutions from the set if one solution is unsuitable. However, many local or global optima occur in SRAPs in MSs and SSs due to the interaction between random system factors, such as processing time uncertainty and machine failure rates. Thus, enhancing the searching efficiency of algorithms for SRAPs is a challenge. This study proposes an efficient simulation–optimization approach, called elite-based particle swarm optimization (EPSO), using an optimal replication allocation strategy (ORAS) (i.e., EPSO[math], to address three types of SRAPs from the literature. Three simulation models were constructed to evaluate the system performance under random factors. We developed a novel EPSO to explore and exploit the solution space. We created an elite group (EG) that includes multiple solutions, and each solution of the EG has a statistically nonsignificant difference from the current optimal solution. The new feature of EPSO updates the velocity and position of the particles in the design space based on multiple global optima from the EG to enhance diversity and prevent premature convergence. We propose an ORAS to allocate a limited number of replications to each solution. Three numerical experiments were performed to verify the effectiveness and efficiency of EPSO[math] compared with other simulation–optimization approaches, namely particle swarm optimization (PSO) and the genetic algorithm (GA) with both optimal computing budget allocation (OCBA) and the ORAS. The experimental results reveal that the solution quality of EPSO improved compared with that of PSO and GA, and the ORAS provides a more efficient allocation of the number of replications compared with the OCBA in the three experiments. Finally, the proposed approach also provides an elite set at the end of the algorithm, instead of a single optimal solution, to support decision-making.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-21T07:00:00Z
DOI: 10.1142/S0217595921500305
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- Fritz John Optimality Conditions for Interval-Valued Multi-Objective
Functions Using gH-Symmetrical Derivative-
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Authors: Sachin Rastogi, Akhlad Iqbal, Sanjeev Rajan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we introduce the concept and applications of gH-symmetrical derivative for interval-valued multi-objective functions, which is the generalization of generalized Hukuhara derivative (gH-derivative). By a suitable example it has been shown that gH-symmetrically derivative is an extension of gH-derivative. Furthermore, we apply this new derivative to investigate the Fritz John type optimality conditions for interval-valued multiobjective programming problems. We use LR type of order relation in this context.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-16T07:00:00Z
DOI: 10.1142/S0217595921500299
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- Tensor Manifold with Tucker Rank Constraints
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Authors: Shih Yu Chang, Ziyan Luo, Liqun Qi
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Low-rank tensor approximation plays a crucial role in various tensor analysis tasks ranging from science to engineering applications. There are several important problems facing low-rank tensor approximation. First, the rank of an approximating tensor is given without checking feasibility. Second, even such approximating tensors exist, however, current proposed algorithms cannot provide global optimality guarantees. In this work, we define the low-rank tensor set (LRTS) for Tucker rank which is a union of manifolds of tensors with specific Tucker rank. We propose a procedure to describe LRTS semi-algebraically and characterize the properties of this LRTS, e.g., feasibility of tensors manifold, the equations/inequations size of LRTS, algebraic dimensions, etc. Furthermore, if the cost function for tensor approximation is polynomial type, e.g., Frobenius norm, we propose an algorithm to approximate a given tensor with Tucker rank constraints and prove the global optimality of the proposed algorithm through critical sets determined by the semi-algebraic characterization of LRTS.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-11T07:00:00Z
DOI: 10.1142/S0217595921500226
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- Min–Max Scheduling of Batch or Drop-Line Jobs Under Agreeable
Release and Processing Times-
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Authors: Yuan Gao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We study the Pareto optimization scheduling on an unbounded parallel-batch machine with jobs having agreeable release dates and processing times for minimizing makespan and maximum cost simultaneously. The jobs considered in this paper are of two types: batch jobs and drop-line jobs. For batch jobs, the completion time of a job is given by the completion time of the batch containing this job. For drop-line jobs, the completion time of a job is given by the starting time of the batch containing this job plus the processing time of this job. For both of batch jobs and drop-line jobs, we present polynomial-time algorithms for finding all Pareto optimal points.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-11T07:00:00Z
DOI: 10.1142/S0217595921500238
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- Herding Behavior and Liquidity in the Cryptocurrency Market
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Authors: Sonia Arsi, Khaled Guesmi, Elie Bouri
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-08T07:00:00Z
DOI: 10.1142/S0217595921400212
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- Self-Adaptive Inertial Projection and Contraction Algorithm for Monotone
Variational Inequality-
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Authors: Xue Gao, Xingju Cai, Xueye Wang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we propose a self-adaptive inertial projection and contraction algorithm, by combining backtracking search with the inertial projection and contraction algorithm, for solving monotone variational inequality in Hilbert space. This algorithm not only circumvents the restrictive assumption of Lipschitz continuity of the operator, but also gives more suitable and feasible parameters. Under the assumption that the operator is continuous and monotone, we establish weak convergence for proposed algorithm. Finally, we report some preliminary computational results to show the efficiency and advantage of the algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-08T07:00:00Z
DOI: 10.1142/S0217595921500214
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- Berth Allocation in Transshipment Ports by Considering Quay Crane Coverage
and Ship Fuel Consumption-
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Authors: Shucheng Yu, Bochen Wang, Si Zhang, Lu Zhen
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This study investigates an integrated model for the continuous berth allocation and quay crane scheduling problem by considering quay crane coverage range, ship fuel consumption, and transshipment costs. A nonlinear mixed-integer programming model is proposed. Some nonlinear parts in this model are linearized by approximation approaches. While the objective function aims to minimize waiting costs, it also seeks to minimize fuel consumption costs from the current port to the next port and housekeeping costs generated by transshipment between vessels. A local branching-based solution algorithm is designed to solve the proposed model. Computational experiments are conducted to validate the effectiveness of the proposed scientific programming model and efficiency of the algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-06-08T07:00:00Z
DOI: 10.1142/S0217595921500287
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- Merging Decision-Making Units with Fuzzy Data
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Authors: Saeid Ghobadi, Khosro Soleimani-Chamkhorami
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper studies the problem of target setting for a generated entity from a merger among two or more decision making units. Identification of the inherited input/output levels from merging decision-making units is an important issue. In this study, a novel inverse data envelopment analysis model is introduced for target setting of a merger in the presence of fuzzy data. This model enables the merged unit to recognize the required input/output levels from merging units to achieve a predefined efficiency target. Moreover, a fuzzy linear programming model is presented for estimating the minimum attainable efficiency score through a given merging. Then, the performance of the proposed method is examined through a banking application.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-05-11T07:00:00Z
DOI: 10.1142/S0217595921400121
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- Variable Neighborhood Descent for Multi-Compartment and Multi-Objective
Vehicle Routing Problem in Refined Product Distribution-
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Authors: Xuping Wang, Wenping Fan, Hongxin Zhan, Zilai Sun
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Refined product distribution is an application of the Multi-compartment Vehicle Routing Problem (MCVRP), which simultaneously considers the vehicle routing, the assignment of heterogonous vehicles and loading policies of multi-compartment. First, we develop an optimization model with the objective of delivering on-time and minimizing transportation cost. Then we propose a Multi-objective Variable Neighborhood Descent Algorithm (MOVND), where the [math]-constraint method transforms the original problem into a series of sub-problems of single objective with constraints. Finally, the efficiency of the proposed algorithm is verified by conducting a large number of small and large instances. Mainly including (i) compared with the classical NSGA-2 algorithm for multi-objective VRP, MOVND provides better performance in terms of convergence, spread and distribution; (ii) the multi-compartment vehicles are able to carry a variety of products simultaneously, which can improve the effective utilization of vehicle space and meet the needs of different customers in a single transportation. In addition, heterogonous vehicles that take full advantage of the characteristics of different vehicles are superior to homogeneous ones in terms of operating cost in the practice of the refined product distribution.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-05-11T07:00:00Z
DOI: 10.1142/S0217595921500196
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- B-Subdifferentials of the Projection onto the Generalized Simplex
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Authors: Youyicun Lin, Shenglong Hu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, a complete characterization of the B-subdifferential of the projection onto the generalized simplex is given. This work is accomplished with the help of Han–Sun Jacobian, whose full characterization is also given in this case. The characterizations are given with simple and explicit formulae in terms of the active index set of the projection.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-05-11T07:00:00Z
DOI: 10.1142/S0217595921500202
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- Feature Transformation for Corporate Tax Default Prediction: Application
of Machine Learning Approaches-
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Authors: Mohammad Zoynul Abedin, M. Kabir Hassan, Imran Khan, Ivan F. Julio
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-05-05T07:00:00Z
DOI: 10.1142/S0217595921400170
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- Applying Simulation Optimization for Agile Vehicle Fleet Sizing of
Automated Material Handling Systems in Semiconductor Manufacturing-
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Authors: Kuo-Hao Chang, Robert Cuckler
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Automated material handling systems (AMHS) have been widely used in semiconductor manufacturing. However, the performance of AMHS heavily hinges on vehicle fleet sizing, which is a complex yet crucial problem. For example, a small fleet size may increase the average wait time, but a large fleet size can also result in traffic congestion. This tradeoff is difficult and can be further exacerbated by profound uncertainty in the manufacturing process. In the literature, the existing models are focused on improving the mean-based performance of AMHS, where the resulting optimal vehicle fleet size is fixed, lacking the ability and flexibility to respond to the changes and/or special requirements that suddenly come up in the manufacturing process. Another drawback with the existing models is that they are not able to characterize the upside/downside risks associated with the resulting vehicle fleet size. This paper, motivated by a real project, presents a novel quantile-based decision model to fill the gap. The adjustment of [math] values in the proposed decision model allows for agile vehicle fleet sizing according to the production situations, resulting in the satisfactory performance of AMHS. We develop a simulation optimization solution method, called ES-AMHS in short, to enable the efficient derivation of the optimal vehicle fleet size. A comprehensive numerical analysis is conducted to evaluate the efficiency and efficacy of the solution method. Finally, an empirical study in cooperation with a wafer fab in Taiwan is presented to show the practical usefulness of this methodology in a real-world setting.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-05-05T07:00:00Z
DOI: 10.1142/S0217595921500184
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- Portfolio Selection with Regularization
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Authors: Ning Zhang, Jingnan Chen, Gengling Dai
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We study the Markowitz mean-variance portfolio selection model under three types of regularizations: single-norm regularizations on individual stocks, mixed-norm regularizations on stock groups, and composite regularizations that combine the single-norm and mixed-norm regularizations. With mixed-norm regularizations incorporated, our model can accomplish group and stock selections simultaneously. Our empirical results using both US and global equity market data show that compared to the classical mean-variance portfolio, almost all regularized portfolios have better out-of-sample risk-adjusted performance measured by Sharpe ratio. In addition, stock selection and group screening accomplished by adding [math] and [math] regularizations respectively can lead to decreased volatility, turnover rate, and leverage ratio. Yet there are instances in which diversifying across different groups is more favorable, depending on the grouping methods. Moreover, we find a positive correlation between portfolio turnover and leverage. Heavily leveraged portfolios also have high turnover rates and thus high transaction costs.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-04-26T07:00:00Z
DOI: 10.1142/S0217595921500160
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- Single-Machine Scheduling Problems with Variable Processing Times and
Past-Sequence-Dependent Delivery Times-
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Authors: Ji-Bo Wang, Jing Xue, Bo Cui, Ming Gao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Scheduling problems with variable processing times and past-sequence-dependent delivery times are considered on a single-machine. The delivery times of jobs depend on their waiting times of processing. A job’s actual processing time depends on its position in a sequence, its starting time and its allocation of non-renewable resources. Under the linear resource consumption function, the goal (version) is to determine the optimal sequence and optimal resource allocation such that the sum of scheduling cost and total resource consumption cost is minimized. Under the convex resource consumption function, three versions of the scheduling cost and total resource consumption cost are discussed. We prove that these four versions can be solved in polynomial time, respectively. Some applications are also given by using the scheduling cost, which involve the makespan, total completion time, total absolute differences in completion times (TADC), and total absolute differences in waiting times (TADW).
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-04-16T07:00:00Z
DOI: 10.1142/S0217595921500135
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- A Proximal Bundle Method with Exact Penalty Technique and Bundle
Modification Strategy for Nonconvex Nonsmooth Constrained Optimization-
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Authors: Xiaoliang Wang, Liping Pang, Qi Wu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The bundle modification strategy for the convex unconstrained problems was proposed by Alexey et al. [[2007] European Journal of Operation Research, 180(1), 38–47.] whose most interesting feature was the reduction of the calls for the quadratic programming solver. In this paper, we extend the bundle modification strategy to a class of nonconvex nonsmooth constraint problems. Concretely, we adopt the convexification technique to the objective function and constraint function, take the penalty strategy to transfer the modified model into an unconstrained optimization and focus on the unconstrained problem with proximal bundle method and the bundle modification strategies. The global convergence of the corresponding algorithm is proved. The primal numerical results show that the proposed algorithms are promising and effective.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-04-16T07:00:00Z
DOI: 10.1142/S0217595921500159
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- An Integrated Response-Surface-Based Method for Simulation Optimization
with Correlated Outputs-
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Authors: Kuo-Hao Chang, Hui-Yu Yang, Robert Cuckler
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
While nearly all previous algorithms designed to solve simulation optimization problems have treated the outputs of simulation systems at a given design point (input parameter) as being independent of each other, this premise is flawed in that simulated outputs are generally correlated. We propose a decorrelation (DC) procedure that can effectively evaluate and remove the correlation of outputs of a simulation system. The proposed DC procedure is further integrated with STRONG, an improved framework of the well-known Response Surface Methodology (RSM), for tackling the simulation optimization problems with correlated outputs. This integration is particularly synergistic due to the fact that STRONG is a fully automated, response-surface-based procedure possessing appealing convergence properties and DC can take advantage of the concept of trust region as in STRONG to enable the removal of the correlation of outputs at the design points within the same trust region all at once. This is more efficient compared to the traditional approaches where a substantial number of observations are typically required for dealing with correlations. The resulting integrated method, which we call STRONG-DC, requires various adaptations so as to ensure the efficacy and efficiency of the overall framework. STRONG-DC preserves the desirable automation and convergence as STRONG, namely, it does not require human involvements and can be proved to achieve the truly optimal solution(s) with probability one (w.p.1) under reasonable conditions. Moreover, the effectiveness and efficiency of STRONG-DC are evaluated through extensive numerical analyses, along with a case study involving the well-known newsvendor problem.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-04-09T07:00:00Z
DOI: 10.1142/S0217595921500147
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- Error Bounds for Inverse Mixed Quasi-Variational Inequality via
Generalized Residual Gap Functions-
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Authors: Yinfeng Zhang, Guolin Yu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we investigate error bounds of an inverse mixed quasi variational inequality problem in Hilbert spaces. Under the assumptions of strong monotonicity of function couple, we obtain some results related to error bounds using generalized residual gap functions. Each presented error bound is an effective estimation of the distance between a feasible solution and the exact solution. Because the inverse mixed quasi-variational inequality covers several kinds of variational inequalities, such as quasi-variational inequality, inverse mixed variational inequality and inverse quasi-variational inequality, the results obtained in this paper can be viewed as an extension of the corresponding results in the related literature.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-04-09T07:00:00Z
DOI: 10.1142/S0217595921500172
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- New Tests for Richness and Poorness: A Stochastic Dominance Analysis of
Income Distributions in Hong Kong-
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Authors: Nikolai Sheung-Chi Chow, Maria Rebecca Valenzuela, Wing-Keung Wong
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper applies stochastic dominance techniques for income distribution analysis and develops tests of richness and poorness to achieve more accurate characterizations of relative welfare in populations than was previously possible. Results from our empirical application, using Hong Kong data, are consistent with predictions of the life-cycle theory of income and savings. Among other things, we find high concentrations of poor individuals among the younger cohorts, and at the same time, there are high concentrations of rich individuals amongst the oldest cohorts. Our results help to explain Hong Kong’s persistently high levels of income inequality in the population.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-03-09T08:00:00Z
DOI: 10.1142/S0217595920400254
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- The Benefits of Diversification Between Bitcoin, Bonds, Equities and the
US Dollar: A Matter of Portfolio Construction-
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Authors: Abdulnasser Hatemi-J, Mohamed A. Hajji, Elie Bouri, Rangan Gupta
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper investigates the potential portfolio diversification between Bitcoin, bonds, equities, and the US dollar. We make use of two approaches for constructing the portfolio. The first is the standard minimum variance approach, and the alternative is based on combining risk and return when the portfolio is constructed. The portfolio based on the minimum variance approach does not result in increasing the return per unit of risk compared to the corresponding value for the best single asset, in this case, Bitcoin. However, the portfolio based on the approach that combines risk and return in the optimization problem does show a return per unit risk higher than the corresponding value for any of the four assets. Thus, the portfolio diversification benefit with respect to these four assets, in terms of return per unit risk, exists only if the portfolio is constructed via the new approach.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-02-27T08:00:00Z
DOI: 10.1142/S0217595920400242
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- A Risk Measurement Model of China’s Non-Ferrous Metal Futures Market
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Authors: Hong Shen, Jinling Zhang, Xu Li, Pin T. Ng
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
With the impact of foreign exchange markets, risks in financial markets are becoming more complex and diversified, which underlines the importance of risk management in financial supervision. In this paper, China’s non-ferrous metal futures market is selected as the research object, and Shanghai Futures Exchange’s Industrial Metal Commodity Index (IMCI) data are used to measure risk using the conditional autoregressive value at risk (CAViaR) model. The US dollar index (USDX) is incorporated into the CAViaR model to study its impact on the risk. Through empirical analysis, we arrive at the following conclusions: First, the asymmetric slope CAViaR model (AS-CAViaR) is more suitable for measuring the risk in China’s non-ferrous metal futures market. Second, the risk is positively impacted by the lagged risk. Moreover, the impacts of positive and negative returns on the risk are asymmetric, with a negative return having a greater impact. Third, the positive and negative shock of USDX has significant and different impacts on the risk. These impacts can be caused by global capital flows. In addition, the impact of the vector of explanatory variables on the IMCI at different quantile levels is discussed based on the CAViaR-USDX model, which reflects the comprehensive advantages of the quantile regression method and the model’s applicability. The above conclusions verify the impact of USDX on China’s non-ferrous metal futures market and provide a theoretical basis and direction for risk monitoring.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-02-27T08:00:00Z
DOI: 10.1142/S0217595920400266
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- The Impact of Government Subsidies and Retailer Contracts on Product
Recovery-
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Authors: Pan Zhang, Chien-Chiang Lee, Yongqi Wu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Conducting product recovery and remanufacturing not only help manufacturers decrease the unit cost of production, but also benefit the environment. However, most manufacturers are hampered by the huge initial investment of related operations. In order to alleviate the manufacturers’ financial pressure of product recovery and remanufacturing, some governments implement the production subsidy (subsidy [math]) and recycling subsidy (subsidy [math]). Meanwhile, retailers can provide the revenue-sharing contract (contract [math]) and cost-sharing contract (contract [math]). Hence, this paper mainly studies the incentive designs of the government and retailer, and the effects of these incentives on the closed-loop supply chain. We first establish a Stackelberg game model consisting of a government, a manufacturer and a retailer, then investigate and compare the optimal decisions and payoffs of each member under each incentive combination of the government and retailer. Our results first show that, on the other hand, the government’s subsidy type cannot affect the retailer’s design of contract [math], but subsidy [math] can induce the retailer to share a higher rate of sale revenue, comparing to subsidy [math]. On the other hand, the retailer’s contract [math] could induce the government to increase subsidy rate in most cases, comparing to contract [math]. Second, the subsidy [math] can always lead to a higher collection rate, lower wholesale and retail prices, and higher payoffs for the government, manufacturer and retailer, comparing to subsidy [math]. Besides, under subsidy [math], contract [math] always leads to a higher collection rate, lower wholesale and retail prices, and higher payoffs for the government, manufacturer and retailer, comparing to contract [math]. However, under subsidy [math], contract [math] can lead to a higher collection rate, a lower wholesale price, and higher payoffs for the manufacturer and retailer, comparing to contract [math] only when the manufacturer’s recovery efficiency is high. Moreover, the retail price is always higher and the government payoffs is always lower under contract [math]. Third, the government prefers to implement the subsidy [math] and then which contract is chosen by the retailer depends on the collection efficiency of the manufacturer. Therefore, subsidy [math] combining with contract [math] or [math] is the equilibrium incentive combination.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2021-02-25T08:00:00Z
DOI: 10.1142/S0217595920400230
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