Authors:Zhusong Liu, Zhenyou Wang, Yuan-Yuan Lu Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. This paper considers the single machine scheduling with learning effect, resource allocation and deteriorating maintenance activity simultaneously. For the convex resource allocation consumption function, we provide a bicriteria analysis where the first (schedule) criterion is to minimize the total weighted sum of makespan, total completion time and total absolute differences in completion times, and the second (resource) criterion is to minimize the total weighted resource consumption. Our aim is to find the optimal resource allocations and job sequence that minimize the three different models of considering the two criterion. We show that these three models are polynomially solvable respectively. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:10Z DOI: 10.1142/S0217595917500117

Authors:Byung-Gyoo Kim, Byung-Cheon Choi, Myoung-Ju Park Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. We consider a two-agent scheduling problem in a two-machine flow shop environment where each agent is responsible for his own set of jobs and wishes to minimize the makespan. The objective is to minimize one agent’s makespan, subject to the other’s objective of not exceeding a given threshold. It is known that the problem is NP-hard. Thus, we consider special cases such that the processing times of each agent have a special structure, and analyze their computational complexity. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:08Z DOI: 10.1142/S0217595917500178

Authors:Li-Ching Ma Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. The case-based distance (CBD) methods for screening are helpful for assisting decision makers in filtering out alternatives that are unlikely to be chosen. However, most of these methods based on selected cases and distance measurements from a chosen target point can only solve screening problems with positive criteria weights. This study proposes a two-phase approach based on mixed integer programming to integrate the concept of Data Envelopment Analysis-Discriminant Analysis (DEA-DA) and the extended case-based distance (ECBD) method for screening problems involving uncertain signs of criteria weights and different target points. The results show that the proposed approach can reduce the misclassification rate and address multiple solution problems. In addition, because the proposed approach can solve problems involving negative weights directly, the influences of different target points can be reduced. Therefore, it is helpful for decision makers to conduct scenario analysis based on different chosen target points. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:06Z DOI: 10.1142/S0217595917500130

Authors:Shi-Sheng Li, De-Liang Qian, Ren-Xia Chen Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. We consider the problem of scheduling [math] jobs with rejection on a set of [math] machines in a proportionate flow shop system where the job processing times are machine-independent. The goal is to find a schedule to minimize the scheduling cost of all accepted jobs plus the total penalty of all rejected jobs. Two variations of the scheduling cost are considered. The first is the maximum tardiness and the second is the total weighted completion time. For the first problem, we first show that it is [math]-hard, then we construct a pseudo-polynomial time algorithm to solve it and an [math] time for the case where the jobs have the same processing time. For the second problem, we first show that it is [math]-hard, then we design [math] time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:05Z DOI: 10.1142/S0217595917500154

Authors:Moawia Alghalith, Xu Guo, Cuizhen Niu, Wing-Keung Wong Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. In this paper, we analyze the impacts of joint energy and output prices uncertainties on the input demands in a mean–variance framework. We find that an increase in expected output price will surely cause the risk-averse firm to increase the input demand, while an increase in expected energy price will surely cause the risk-averse firm to decrease the demand for energy, but increase the demand for the non-risky inputs. Furthermore, we investigate the two cases with only uncertain energy price and only uncertain output price. In the case with only uncertain energy price, we find that the uncertain energy price has no impact on the demands for the non-risky inputs. We also show that the concepts of elasticity and decreasing absolute risk aversion (DARA) play an important role in the comparative statics analysis. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:02Z DOI: 10.1142/S021759591750018X

Authors:Jiawen Hu, Zuhua Jiang, Hong Wang Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. This paper considers the situation where the production planning sector has decision making priority over the maintenance sector. The production rate of each production period (PP) varies with different lot size of each PP. An extended imperfect preventive maintenance (PM) model is proposed for a system running with time-varying production rate. The Stackelberg game theory model is adopted to handle the interactive effect between production plan and PM schedule; meanwhile, it embodies the decision priority of the production planning sector. In the Stackelberg game theory model, the production planning sector is assigned to be the leader with the decision variable of production rate of each PP, and the maintenance sector to be the follower with the decision variable of PM schedule. Firstly, the optimal lot size of each item for each PP is obtained for the production planning sector. Then, the optimal production rate of each PP and PM sequences are obtained simultaneously by regarding the total lot size of each PP as the objective. The availability loss caused by maintenance operations and effect of production rate on hazard rate function of system have been considered. An example is studied to illustrate the effectiveness of the model, also the results are fully analyzed. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:14:01Z DOI: 10.1142/S0217595917500129

Authors:Ata Allah Taleizadeh Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. This study proposes a model for a multi-objective, multi-buyer, multi-vendor, multi-product and multi-constraint supply chain. The buyers’ demand rates are stochastic variables with known probability distribution functions. The classical ([math]) inventory control system is used to manage the inventories of all buyers where the lead times are production rate dependent. Shortage is permitted and is partially backordered where the partial back ordering rate is forecasted. The model is considered as a multi-objective integer nonlinear programming problem including cost, service level and lead time objectives and using a novel hybrid method, a hybrid of Meta Goal Programming (MGP) and Firefly Algorithm (FA) are solved. Numerical examples are given to illustrate the proposed method in the study. The results of the study are compared to other hybrid methods of Meta Goal Programming with other evolutionary algorithms such as Bees Algorithms (BA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:13:59Z DOI: 10.1142/S021759591750021X

Authors:Amal Abdel Razzac, Linda Salahaldin, Salah Eddine Elayoubi, Yezekael Hayel, Tijani Chahed Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. This work presents a strategic investment framework for mobile TV infrastructure. We address the question of whether an operator should enter the mobile TV market and, if yes, how and when to do so. We perform a capacity and QoS analysis that shows that Mobile TV is not a simple added-on service but requires new planning strategies that need heavy investments. We consider two possible settings: a competitive mobile TV network where a digital video broadcasting (DVB) operator and a cellular network operator build their independent networks and a cooperative setting where the TV network is mainly relying on a DVB infrastructure whose coverage can be complemented by a cellular network. Two main issues alter the investment decision of the stakeholders, namely, the market uncertainty and the competition or the willingness of cooperation between the main technological players. We define a game theoretical real options framework and propose a novel bi-level dynamic programming algorithm that solves the underlying profit maximization problem. Our numerical results illustrate the optimal investment decisions of both actors and the impact of the system parameters as well as the degree of uncertainty on the investment dates. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:13:58Z DOI: 10.1142/S0217595917500142

Authors:Changkyu Park Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 04, August 2017. Drop-shipping is a commonly adopted online-order fulfillment strategy in the Internet age. In this practice, online retailers leverage the fulfillment capabilities of suppliers to fulfill orders. On the other hand, purchase dependence is a frequent phenomenon and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g., market basket analysis), it has largely remained unaddressed in operations management. This paper develops an [math] model to address an environment in which unmet demand orders are partially lost and partially backordered when purchase dependence exists. The partial backorders are fulfilled by a drop-shipping option. Through computational analyses, this paper demonstrates the effect of both drop-shipping on a partial backordering and purchase dependence. The results show that more profit can be realized by utilizing a drop-shipping option under purchase dependence. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-08-02T07:13:56Z DOI: 10.1142/S0217595917500166

Authors:Yu Zhao, Xi Zhang, Zhongshun Shi, Lei He Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-19T06:39:27Z DOI: 10.1142/S0217595917500208

Authors:Soheila Seyedboveir, Sohrab Kordrostami, Behrouz Daneshian, Alireza Amirteimoori Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. The “Dynamic-network” version of cost efficiency measurement in Data Envelopment Analysis (DEA) is proposed in this paper. The classical DEA models ignore operations of individual processes within a system; moreover, they compute efficiency at the same time. Therefore, we suggest a relational model to estimate cost efficiency in static network structures. Also, we incorporate the dynamic effect in network structures. The proposed models here evaluate the overall efficiency over the whole periods and indicate it as a weighted average of period efficiencies. The main advantage revealed in this study is recognition of: which divisions at what periods caused the inefficiency of the system, the internal activities of the system over time, considered; moreover, the results obtained here is applicable in, improving the performance of the system. A case study of Iranian banking industry is used to show the applicability of the approach. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-15T05:57:20Z DOI: 10.1142/S0217595917500233

Authors:Jun Tong, Jian-Qiang Hu, Jiaqiao Hu Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. We propose an efficient algorithm for computing the equilibrium of a capital asset pricing model with heterogeneous investors and short-sale constraints. We show that the equilibrium prices of the risky assets in the model are proportional to the Lagrangian multipliers of an equivalent dual formulation of the problem. Based on this observation, we derive sufficient conditions to guarantee the existence and uniqueness of equilibrium and prove the convergence of the algorithm. Numerical examples are also provided to illustrate the algorithm. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-15T05:57:19Z DOI: 10.1142/S0217595917500257

Authors:Yuxiang Yang, Zuqing Huang, Qiang Patrick Qiang, Gengui Zhou Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. A firm sets up his facilities including manufacturing/remanufacturing plants and distribution/collection centers, incorporating an existing closed-loop supply chain (CLSC) network. The entering firm has to compete with the existing firms in the existing network. The entering firm behaves as the leader of a Stackelberg game while the existing firms in the existing network are followers. We assume that the entering firm can anticipate the existing firms’ reaction to his potential location decision before choosing his optimal policy. We use a CLSC network equilibrium model in which the decision makers are faced with multiple objectives to capture the existing firms’ reaction. A mathematical programming model with equilibrium constraints is developed for this competitive CLSC network design problem by taking into account the market competition existing in the decentralized CLSC network. A solution method is developed by integrating Genetic algorithm with an inexact logarithmic-quadratic proximal augmented Lagrangian method. Finally, numerical examples and the related results are studied for illustration purpose. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-15T05:57:19Z DOI: 10.1142/S0217595917500269

Authors:Lingfa Lu, Liqi Zhang Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we consider the online single machine scheduling problem to minimize the maximum starting time of the jobs. For the non-preemptive model, we show that there is no determined or randomized online algorithm with a bounded competitive ratio. For the preemption-resume model, we show that the well-known SRPT rule yields an optimal schedule. For the preemption-restart model, we show that any determined online algorithm has a competitive ratio of at least [math] and present an online algorithm with the best-possible competitive ratio of [math]. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-05T01:25:57Z DOI: 10.1142/S0217595917500221

Authors:Qiulan Zhao, Jinjiang Yuan Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. We introduce and study the rescheduling on a single machine to minimize the maximum lateness under the sequence disruptions of original jobs. In the problem, each original job [math] has a constraint disruption on its sequence respect to an original optimal schedule [math], i.e., [math]. That is, if [math] is the [math]th job in [math], then it is required that the position index [math] of [math] in a schedule for all jobs satisfies [math]. By introducing the positive sequence disruption [math] and the negative sequence disruption [math], three problems are considered in this paper: problem (P1) is [math], problem (P2) is [math], and problem (P3) is [math]. We show that the three problems are equivalent and can be solved in [math] time. Then we study an extension of problem (P2): [math]. We show that the extended problem can be solved in [math] time. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-05T01:25:57Z DOI: 10.1142/S0217595917500245

Authors:Qing Wang, Zhaojun Liu, Yang Zhang Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In the traditional DEA model, each DMU maximizes its efficiency with the most favorable weights. This leads to flexibility and unreality of input and output weights. Subsequently, it is unfair to compare and rank the efficiencies of different DMUs obtained on the basis of these weights. In this paper, we propose a novel approach to determine a common set of weights with more consensus to evaluate and rank the performance of all DMUs by weighting the rescaled weights based on the degree of consensus, where the weights obtained from DEA are rescaled for comparison among DMUs. Moreover, to overcome the non-uniqueness of the weights, a novel secondary goal is developed based on the agreement between self-evaluation and peer-evaluation. In addition, the restriction of weights is taken into account to avoid trivial weights. Finally, an example of 14 international passenger airlines is used to illustrate the performance and credibility of our proposed method. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-09-05T01:25:57Z DOI: 10.1142/S0217595917500270

Authors:Andrea Raiconi, Julia Pahl, Monica Gentili, Stefan Voß, Raffaele Cerulli Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this work, we face a variant of the capacitated lot sizing problem. This is a classical problem addressing the issue of aggregating lot sizes for a finite number of discrete periodic demands that need to be satisfied, thus setting up production resources and eventually creating inventories, while minimizing the overall cost. In the proposed variant we take into account lifetime constraints, which model products with maximum fixed shelflives due to several possible reasons, including regulations or technical obsolescence. We propose four formulations, derived from the literature on the classical version of the problem and adapted to the proposed variant. An extensive experimental phase on two datasets from the literature is used to test and compare the performance of the proposed formulations. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:23Z DOI: 10.1142/S0217595917500191