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Asia-Pacific Journal of Operational Research
Journal Prestige (SJR): 0.477  Citation Impact (citeScore): 1 Number of Followers: 4  Hybrid journal (It can contain Open Access articles)ISSN (Print) 0217-5959 - ISSN (Online) 1793-7019 Published by World Scientific  [121 journals]
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- Sourcing Channel Decision in the Presence of Platform Encroachment with
Quantity Competition-
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Authors: Qinjia Zhong, Jianjun Wang, Jiaqian Xie, Xiaofan Lai
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The decision of sourcing channels is a critical strategy for online retail platforms (ORs) when they encroach into the product market of the third-party retailers (3PRs). Whether the products of the OR are provided by the common supplier or the one different from that of the 3PR leads to different supply chain structures. This paper focuses on the sourcing channel decision of an encroaching OR given the sourcing channel of a 3PR. The OR may source either from the common supplier, i.e., the same supplier as that of the 3PR, or from a different supplier, i.e., one different from that of the 3PR. This paper develops two game-theoretical models to examine the sourcing channel decision of an OR in quantity competition with a 3PR on the platform. The results show that the OR’s preference for the sourcing channel is significantly affected by the production cost, product substitutability and commission rate. By comparing the performances of the OR under these two sourcing channels, we find that when the production cost of the different supplier is very low, or when the product substitutability of the different supplier is low, or when the commission rate is either very low or very high, the OR prefers to source from the different supplier. Otherwise, the OR tends to source from the common supplier. We also find that the OR’s encroachment into the sales market of a 3PR by sourcing from the common supplier will always hurt the 3PR, but the platform encroachment by sourcing from the different supplier will not necessarily hurt, especially when the supplier selected by the OR has a significant disadvantage in production cost compared to those of the 3PR. Furthermore, when the OR encroaches by sourcing from the common supplier, it becomes the largest retailer in the market; otherwise, the 3PR is the largest retailer in that market. Our results also indicate that platform encroachment by different sourcing can not only expand the sales market, but also weaken the negative effects of platform encroachment on the profit of the 3PR.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-31T07:00:00Z
DOI: 10.1142/S0217595924400116
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- Could Regression of Stationary Series Be Spurious'
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Authors: Wing-Keung Wong, Yushan Cheng, Mu Yue
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Spurious regression, commonly associated with independent and (nearly) non-stationary time series, has been extensively studied. However, the potential for spurious outcomes in regression involving stationary time series remains largely unexplored, representing a gap in the literature. To address this gap, we propose that regression of stationary time series may yield spurious outcomes and conduct a comprehensive investigation to verify this conjecture. Additionally, we present a remedy algorithm to mitigate spurious effects and improve model interpretability. Through extensive simulations, we validate our conjecture and demonstrate the efficacy of the proposed remedy. A numerical analysis further illustrates the practical utility of our approach. This study offers a fresh perspective on spurious regression and provides a practical solution to enhance the reliability of regression analyses involving stationary time series data.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-31T07:00:00Z
DOI: 10.1142/S0217595924400177
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- Preface — Special Issue: Operational Research Models for E-Commerce
Platforms: Business Model Innovation-
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Authors: Bin Shen, Ciwei Dong, Hau-Ling Chan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
E-commerce platforms are digital marketplaces for selling goods and services online. Operational research (OR) models support business model innovation in e-commerce platforms. This issue covers eight papers that utilize OR models to examine business model innovation in e-commerce platforms. They all introduce new research problems and concepts, develop novel and rigorous OR methodologies to tackle them, and present innovative OR applications according to the real problems in e-commerce platforms. Based on the eight papers’ discussions, we propose future research directions.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-27T07:00:00Z
DOI: 10.1142/S0217595924020019
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- Multi-Period Profit-Oriented Location Routing Problem in Multi-User
Warehouses with Backordering and Order Acceptance: A Relax-Fix-Optimize
Heuristic-
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Authors: Mohammed Rida Ech-Charrat, Najlae Zhani, Kaoutar Chargui, Tarik Zouadi
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper addresses profit maximization in location routing problem (LRP) where backordering is allowed and serving all customers is not mandatory. The problem is motivated by the practical case of distribution companies using temporary multi-user warehouses to serve their customers. We propose a multi-period novel mixed integer linear programming (MILP) model for the problem where demand is price sensitive. The model seeks to find optimal locations of warehouses over the planning horizon, set of orders to serve, to backorder or to reject, and routing decisions giving optimal profit. The commercial solver (CPLEX) is used to solve the model on small instances. For medium and large instances, we propose a solution approach that incorporates the Relax and Fix (R&F), Fix and Optimize (F&O) procedures combined with local search heuristics. Computational experiments and economic analysis show the relevance of the proposed approach and the impact of parameters on generated profit.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-27T07:00:00Z
DOI: 10.1142/S0217595924500180
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- Coordination Mechanisms for Traceable Products in the Context of Live
Streaming E-Commerce from the Perspective of Platform, Anchor, and
Consumer-
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Authors: Ya-Jing Dong, Xuan Zhang, Jun-Zhen Zhou, Jie-Yu Song
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The meteoric rise of live streaming e-commerce, an emergent form of electronic commerce, is accompanied by frequent occurrences of “live streaming mishaps”. Live streaming’s visual nature offers a more varied and intuitive method for showcasing traceable products, thereby enhancing customer engagement and contracting customers. However, due to the diverging interests, conflicts and contradictions among the stakeholders in traceable products persist, introducing challenges and complexities to its management. We develop an evolutionary game-theoretical model involving three key entities: the platform, anchors (live stream hosts), and consumers. Through the establishment of a payment matrix, we analyze their interactive behaviors and equilibrium states. Additionally, we examine the impacts of key factors on the system and find that platforms and anchors play a pivotal “dual-core” role in traceable product management within the live streaming e-commerce landscape. Moreover, factors such as market attention, anchor characteristics, and consumer dissatisfaction emerge as critical determinants for effective traceable product management. This research contributes valuable insights into enhancing coordination of traceable products in live streaming e-commerce. It offers perspectives on the evolutionary dynamics and strategies for selecting traceable products by platforms, broadcasters, and consumers.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-25T07:00:00Z
DOI: 10.1142/S021759592440013X
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- Long-Term Mine Planning: A Survey of Classical, Hybrid and Artificial
Intelligence-Based Methods-
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Authors: Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or (meta) heuristic methods — toward hybrid methods. They are compared through the scope of the problem formulation and discussed via solution quality, efficiency, and gaps. We finally conclude with opportunities to incorporate artificial intelligence (AI)-based methods due to paucity, multiple operational uncertainties simultaneously, sustainability indicator quantification, and benchmark instances.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-25T07:00:00Z
DOI: 10.1142/S0217595924400141
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- Container Pre-Marshaling Problem with Uncertain Intra-Group Retrievals
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Authors: Ning Wang, Jiahuan Chen, Zhihao Jiang, Lili Yu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In real-world terminals, containers are divided into multiple groups according to the reservation time. The container retrieval order between different groups is known, while the retrieval order within the same group is unknown. Previous research studies prioritize between groups but seldom prioritize within the same group, especially uncertain intra-group priorities. This paper studies the container pre-marshaling problem with uncertain intra-group retrievals (CPMP-UIR). Since the retrieval order of intra-group containers is uncertain, after the pre-marshaling, containers still need to be relocated during retrieval. CPMP-UIR conducts pre-marshaling, ensuring no relocations between groups and a minimum expected number of overstows during retrieval. In this paper, we first give the formula for calculating the expected number of overstows in a layout. The value obtained by the formula is a lower bound for the expected number of relocations. Second, we develop an algorithm called the Expected Overstow-based Heuristic (EOH) for solving CPMP-UIR. In addition, a hard-enough dataset is generated to evaluate the performance of EOH. Finally, our numerical experiments show that the expected number of overstows during the retrieval phase is reduced dramatically after the pre-marshaling.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-20T07:00:00Z
DOI: 10.1142/S0217595924500179
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- Methods to Enable Evolvable Digital Twins for Flexible Automated
Production Systems-
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Authors: Birgit Vogel-Heuser, Bjarne Lahrsen, Jan Wilch, Fan Ji, Mingxi Zhang, Eva-Maria Neumann
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Changing requirements cause flexible automated Production Systems (aPS) to evolve over decades. Digital Twins (DT) of the different hierarchy levels and design steps ease this evolution, e.g., by enabling requirement analysis and compatibility checks ahead of any physical changes. To ensure up-to-date models and integrate additional knowledge, information gained during operation is included in DTs. Consequently, evolvability, decomposability, control software modularity, and learning during operation are identified as four requirements to achieve such evolvable DTs. Concepts to realize every requirement are introduced and exemplarily validated using a demonstrator machine. AutomationML (AML), the XML-based vendor neutral language for information modeling and exchange in between different disciplines and their tools and product classification systems like ECLASS that specify components attributes vendor neutral enable evolvability during the design phase. Decomposability is achieved by assembling DTs of components according to ISA 88 levels from control unit to facility. A control primitive concept that realizes control software modularity is introduced and validated. Based on data analytics and operation data the DT can be updated by using the versioning mechanism of AML. Thereby, the DT for the next machine generation is improved with knowledge from operation and represents the already existing machine more precisely.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-13T07:00:00Z
DOI: 10.1142/S0217595924400153
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- Portfolio Selection Based on Mean-Generalized Variance Analysis: Evidence
from the G20 Stock Markets-
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Authors: Zongxin Li, Yongchang Hui, Wing-Keung Wong, Ruiyue Lin
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Modern finance theories have been increasingly paying attention to nonlinear and asymmetric features of stock returns. In this paper, we extend the concept of covariance to generalized covariance by using Generalized Measure of Correlation (GMC). Based on the generalized covariance which is capable of catching the nonlinearity and asymmetry in stock (index) returns, we propose a mean-generalized variance portfolio selection model which considers the gross-exposure constraint. Furthermore, we propose the corresponding nonparametric estimation approach and the global optimization algorithm to enhance the applicability of our new model. Empirical studies on G20 stock markets support that a portfolio considering nonlinear and asymmetric features among the international markets would outperform traditional ones based on mean-variance optimization and equal weighting strategy in terms of return and flexibility.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-10T07:00:00Z
DOI: 10.1142/S0217595924500167
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- Integrating Operations Research into Very Large-Scale Integrated Circuits
Placement Design: A Review-
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Authors: Binqi Zhang, Lu Zhen, Shuaian Wang, Fajun Yang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The placement stage of the physical design of very large-scale integrated circuits (VLSI) specifies the arrangement and order of standard cells and devices within an area, and the effectiveness of the placement result directly affects the chip’s performance. The advancement of process standards and reduction in the size of features size have dramatically increased the complexity of placement design. The realization of placement algorithms to deal with millions of cells has thus become an important issue in the automation of integrated circuit electronic design. In this paper, we first segment the digital integrated circuits into VLSI design styles and review them from the perspectives of practical placement problems, placement design ideas, and placement optimization methods. The discussion around traditional circuits focuses on the essence of the placement problem, placement steps, and classification of placement algorithms. In modern circuit placement, the discussion focuses on the impact of technical constraints on design. After identifying the essence of the placement problem, solutions to the packing problem are reviewed, and we then summarize our representative placement algorithms. Finally, based on the development of VLSI placement design, the optimization bottlenecks of existing placement design are summarized, and suggestions for future research are made based on the latest research topics and methodologies.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-07-06T07:00:00Z
DOI: 10.1142/S0217595924500076
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- Multiple Objective Simulation Optimization for Emergency Department
Resource Allocation-
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Authors: Hao Huang, Chih-Wei Cheng
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Emergency departments (EDs) are among the most intensive and dynamic departments in hospital systems. This study considers the resource allocation problem at the ED of a hospital with two objective functions: the length of stay (LS) and wasted resources. The numbers of doctors, nurses, lab technicians, and other medical equipment must be constantly determined to effectively operate EDs with the two objective functions, representing the satisfaction of patients and unnecessary costs, respectively. The ED system is evaluated with a discrete event simulation (DES) model. Furthermore, a Pareto optimal set of solutions is approximated using the quantile-relaxed multiple objective probabilistic branch and bound (QR-MOPBnB), which was developed as an extension algorithm of MOPBnB to improve efficiency. QR-MOPBnB iteratively branches the solution space into subregions and identifies the subregions that are the most promising to be close to the Pareto optimal set. Using QR-MOPBnB, the trade-off between the LS and wasted resources is illustrated for decision makers to appropriately determine the ideal design based on their preferences, and the relaxed Pareto optimal set with statistical quality assurance provides further insights similar to those obtained through sensitivity analysis.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-28T07:00:00Z
DOI: 10.1142/S0217595924400074
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- Product Introduction with Parallel Importation
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Authors: Fengmei Xu, Feng Yang, Feifei Shan, Ting Chen
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The aim of this paper is to investigate the manufacturer’s product introduction decision when entering a foreign market by considering parallel importation. This paper considers a situation where a manufacturer already sells an original product directly in the domestic market and decides to introduce an original product, a new product, or both through a retailer in a foreign market, and a parallel importer may divert the original product across markets due to price gap (we call it parallel importation). This paper shows that when consumers perceive gray products as perfect substitutes for authorized products, the manufacturer switches from introducing two products to introducing a new product and still maintains the quality of the new product in the presence of parallel importation. Furthermore, in response to parallel importation, the manufacturer not only narrows the price gap for original products between two markets but also reduces the wholesale price of new products in the foreign market. In addition, parallel importation always hurts the retailer but may benefit the manufacturer. Counterintuitively, parallel importation always improves social welfare in the domestic market but may damage social welfare in the foreign market.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-26T07:00:00Z
DOI: 10.1142/S0217595924500131
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- Joint Channel and Logistics Selection Strategies in an E-Commerce Supply
Chain with a Capital-Constrained Supplier-
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Authors: Lipan Feng, Qi Kou, Yuqian Dai, Xiaoli Wu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Some prominent e-commerce giants, such as Amazon and JD.com, have established their own logistics service and shared it with other merchants. In this paper, we study the channel (reselling channel or agency channel) and logistics service (platform’s logistics or third-party logistics) selection strategies in an e-commerce supply chain composed of a limited capital supplier and a platform within the consideration of the supply chain finance. We show that the supplier’s channel and logistics service selection strategies depend on the cost coefficient of the platform’s logistics service and the production cost. Interestingly, the supplier prefers the reselling channel rather than the agency channel with the platform’s logistics service. This explains why the merchants in JD.com rarely use JD.com logistics in the agency channel. From the perspective of the platform, the platform may prefer the supplier to use third-party logistics service to deliver goods in the agency channel particularly when the cost coefficient of the platform’s logistics service is relatively large. Furthermore, the platform will put in more effort level aimed at enhancing the logistics service in the reselling channel.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-25T07:00:00Z
DOI: 10.1142/S0217595924400104
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- Improving Instant Delivery Efficiency: Integrating Learning Effects into
Strategic Rider Assignment Models-
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Authors: Tijun Fan, Ming Yang, Jingyi Chen, Qiuchen Gu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
High volatility in customer demand orders during peak and off-peak periods is a great challenge for instant delivery. In this paper, considering the rider familiarity with different areas and the learning effect, we establish two models for different rider assignment strategies: Maximum efficiency model during the peak period and Training familiarity model during the off-peak period. Meanwhile, a hybrid algorithm parallel genetic algorithm and a large-scale neighborhood search (PGA-LNS) is designed to solve the models. The results of two comparative experiments and 50-cycle peak and off-peak alternating experiments show that adopting the Maximum efficiency model in the peak period and the Training familiarity model in the off-peak period is beneficial for instant delivery to achieve overall flexibility, stability, and delivery efficiency.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-19T07:00:00Z
DOI: 10.1142/S0217595924400128
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- Parameter Estimation and Testing for the Doubly Geometric Process with
Lognormal Distribution: Application to Bladder Cancer Patients’ Data-
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Authors: Mustafa Hilmi Pekalp, Gültaç Eroğlu İnan, Halil Aydoğdu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The geometric process (GP) has been widely utilized as a stochastic monotone model in the fields of probability and statistics. However, its practical application is often limited by certain assumptions. To address this, [Wu (2018). Doubly geometric process and applications. Journal of the Operational Research Society, 69(1), 66–67] introduced the doubly geometric process (DGP) as an extension of the GP model, relaxing some of its assumptions. Due to its ability to overcome the limitations of the GP model, the DGP has gained significant popularity in recent times. This study focuses on the parameter estimation problem for the DGP when the distribution of the first interarrival time follows a lognormal distribution with parameters [math] and [math]. We employ the maximum likelihood method to obtain estimates for both the model parameters and the distribution parameters. Additionally, we investigate the asymptotic joint distribution and statistical properties such as asymptotic unbiasedness and consistency of the estimators. Furthermore, we propose a novel test procedure to distinguish between the GP and DGP models. To assess the performance of the estimators and the proposed test procedure, we conduct a simulation study involving various sample sizes and parameter values. Finally, we present an application of the developed methods in fitting data from bladder cancer patients.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-17T07:00:00Z
DOI: 10.1142/S0217595924500143
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- Reduced Outer Space Algorithm for Globally Computing Affine Sum-of-Ratios
Problems-
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Authors: Hongwei Jiao, Junqiao Ma, Youlin Shang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper investigates the affine sum-of-ratios problem (SRP) which has numerous applications in many fields of economy and engineering. For globally computing the affine SRP, based on equivalent transformation and new affine relaxation problem, a reduced outer space branch-and-bound algorithm is designed. The designed algorithm has been proven to converge to a global optimal solution of the problem (SRP) eventually. Meanwhile, the maximum iteration time for the algorithm in the worst case is estimated by analyzing its computational complexity. Finally, numerical experimental results indicate that the presented algorithm is robust and effective.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-17T07:00:00Z
DOI: 10.1142/S0217595924500155
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- A New Cost-Sharing Scheme for Cleaning Up Polluted Line-Structure River
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Authors: Dongshuang Hou, Weibin Han, Loyimee Gogoi
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Cleaning up a polluted river requires coordinated actions among different firms along the river. The key to promoting and enriching the firms’ cooperation is to share the pollution cleanup cost among these firms fairly and reasonably. In this paper, we propose a new cost-sharing scheme, called the entire upstream agents compensation method (the EUAC method for short), for a line-structure river cleanup cost-sharing problem by considering the waste transfer rate for each river segment. On the one hand, we offer a re-allocation scenario in order to implement the EUAC method. On the other hand, to reveal the rationality and stability of this proposed method, we formulate a cooperative cost game for the line-structure river cleanup cost-sharing problem, and show that the EUAC method coincides with the Shapley value of this game and belongs to the core of this game.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-15T07:00:00Z
DOI: 10.1142/S021759592450012X
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- Advance Selling Strategy with Return Service: The Impacts of Information
Disclosure and Disclosure Methods-
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Authors: Yajing Wang, Jian Li, Shichao Zhu, Pei Quan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The return cost burdens both consumers and merchants, leading merchants to emphasize information disclosure during pre-sales of new products. This study analyzes the effects of information disclosure methods on consumer valuation and merchants’ pre-sale strategies across four scenarios: no advance selling, no information disclosure, traditional information disclosure, and blockchain-enabled information disclosure. We examine optimal pricing and inventory strategies under each scenario, assessing the value of information disclosure and the merchant’s optimal advance selling strategy. We find that traditional information disclosure could lead to various pre-sale pricing outcomes based on consumer valuation distribution bounds, trust levels, and merchant return losses. Blockchain-based disclosure influences merchants to implement premium pricing, which may render blockchain unnecessary. Offering pre-orders is beneficial with any information disclosure method. If consumers have varied product understandings, merchants benefit more from using an information disclosure strategy rather than just offering returns; otherwise, the reverse is true. The paper provides theoretical insights into optimal pre-sale information disclosure practices.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-08T07:00:00Z
DOI: 10.1142/S0217595924400098
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- Price of Anarchy of Scheduling Games on Hierarchical Machines with
Quadratic Social Cost-
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Authors: Ling Lin, Zhiyi Tan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper studies the inefficiency of Nash equilibria (NE) for scheduling games on hierarchical machines with quadratic social cost. There is a set of hierarchical machines and a set of jobs. Each job can choose one machine from the set of machines that are permitted to process it, and the cost associated with the job is equal to the load of the selected machine. A schedule is an NE if no job can reduce its cost by ultimately moving to a different eligible machine. The social cost is the sum of squares of the loads of all the machines. We show that the Price of Anarchy for two, three and four machines is [math], [math] and [math], respectively.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-04T07:00:00Z
DOI: 10.1142/S0217595924500106
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- Efficient Parallel Algorithm for Minimum Cost Submodular Cover Problem
with Lower Adaptive Complexity-
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Authors: Hue T. Nguyen, Dung T. K. Ha, Canh V. Pham
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we study the Minimum Cost Submodular Cover (MCSC) problem over the ground set of size [math], which aims at finding a subset with the minimal cost required so that the utility submodular function exceeds a given threshold. The problem has recently attracted a lot of attention due to its applications in various domains of operations research and artificial intelligence. However, the existing algorithms for this problem may not be effectively parallelized because of their costly adaptive complexity. This paper proposes an efficient parallel algorithm that returns a [math]-bicriteria approximation solution within [math] adaptive complexity, where [math] are fixed parameters. Our algorithm requires [math] query complexity, however, it can reduce to [math] instead while retaining a low adaptive complexity of [math]. Therefore, our algorithm not only achieves the same approximation guarantees as the state of the art but also significantly improves the best-known low adaptive complexity algorithm for the above problem.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-06-01T07:00:00Z
DOI: 10.1142/S0217595924500052
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- A Dynamic and Stochastic Cumulative Capacitated Vehicle Routing Problem
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Authors: Yu Wu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The cumulative capacitated vehicle routing problem (CCVRP) is a variant of the classical capacitated vehicle routing problem in which the objective is the minimization of customers’ total waiting time prior to service. Inspired by the first-mile pick-up service within 3rd-Party Logistics operations, we study a dynamic and stochastic variant of the CCVRP. In this variant, a capacitated vehicle undertakes a single tour to collect customers’ parcels, taking into account stochastic service requests, stochastic service cancellations, and stochastic demand (i.e., changes in parcel size) simultaneously. Conditional on maximizing the usage of the vehicle’s capacity, we minimize the total waiting time of both customers who are served and those who request services but are not served due to cancellation or capacity violation. We formulate this problem as a Markov decision process and solve it using a hybrid approach that combines approximate dynamic programming and rollout algorithm. Our main contributions include ([math]) the construction of the [math] [math] that leverages a property of the Poisson process, and ([math]) consideration of service fairness-based acceptance/rejection rule in response to excessive stochastic service requests. In the numerical study, our approach is examined to outperform an 2-Opt-based re-optimization policy in terms of both customers’ waiting time and service-request satisfying rate. More importantly, computational results heuristically show that there is no cost to adopting the service fairness-based rule, which is obviously a valuable managerial insight.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-31T07:00:00Z
DOI: 10.1142/S0217595924500088
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- Earliness-Tardiness Scheduling with Delivery Times and Deteriorating Jobs
-
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Authors: Yuan-Yuan Lu, Shuang Zhang, Jia-Yi Tao
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper addresses single-machine scheduling problems with past-sequence-dependent (denoted by [math]) delivery times, due-window assignments and deteriorating jobs simultaneously. The [math] delivery time of a job is proportional to the job’s waiting time, and the deteriorating jobs mean that the processing time of a job is a proportional increasing function of its starting time. For common, slack and different due-window assignments, the goal is to minimize the weighted sum of earliness, tardiness, due-window starting time and size. Some optimal propositions of the problems are given, and by these propositions, polynomial time algorithms are proposed to optimally solve the above problems.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-29T07:00:00Z
DOI: 10.1142/S021759592450009X
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- Proportionate Flow Shop Scheduling with Job-dependent Due Windows and
Position-dependent Weights-
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Authors: Ji-Bo Wang, Dan-Yang Lv, Congying Wan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we deal with the different due windows assignment proportionate flow shop problem with position-dependent weights, where the sequence is a permutation. The objective is to determine an optimal job sequence and due windows of all jobs such that the weighted sum of earliness–tardiness, the starting time and size of all due windows is to be minimized, where the weight is not related to the job but to the position in which some job is scheduled, i.e., position-dependent weights. According to a series of optimal properties, we prove that the problem can be solved in polynomial time [math], where [math] is the number of jobs.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-29T07:00:00Z
DOI: 10.1142/S0217595924500118
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- Higher-Order Radial Hadamard Directional Derivatives and Applications to
Set-Valued Equilibrium Problems-
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Authors: Tian Tang, Guolin Yu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we first introduce the higher-order lower and upper radial Hadamard directional derivatives for set-valued maps, which can capture the global information of concerning maps, and discuss the relationships with other existed higher-order derivatives. Second, based on the introduced derivatives, we establish optimality conditions for Henig efficient solutions of a set-valued equilibrium problem with constraints. Particularly, the optimality conditions hold in the case where the derivatives of objective and constraint functions are separated. Finally, we give some duality theorems for a mixed type of primal-dual set-valued equilibrium problem. The main results of this paper are illustrated by several concrete examples.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-23T07:00:00Z
DOI: 10.1142/S0217595924500064
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- Simulation Learning and Optimization: Methodology and Applications
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Authors: Kuo-Hao Chang, Tzu-Li Chen
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-21T07:00:00Z
DOI: 10.1142/S0217595924400086
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- Order-Fulfillment Coalitions in a Hybrid E-Commerce Platform: Myopic
versus Farsighted Stability-
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Authors: Qingying Li, Shuo Zhu, Hongqiao Chen
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper considers the order-fulfillment coalitions among an e-tailer and two third-party sellers in a hybrid e-commerce platform. The e-tailer uses a self-supported logistics system to fulfill the orders, and the seller can either use the former or turn to third-party logistics systems. By investigating the equilibrium order quantities, the strong preference relations, and the possible deviations for all the possible coalition structures, we discuss both myopic stability and farsighted stability for all the coalition structures. We find that if economies of scale are absent, fulfilling the orders independently is unique Nash stable and farsightedly stable; however, if alliances generate economies of scale, there may not exist Nash stable, but the farsightedly stable outcome always exists, which further depends on the interaction between the e-tailer’s order-fulfillment cost advantage and economies of scale. Further discussions on extremely high economies of scale and the proportional commission fee are also conducted.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-14T07:00:00Z
DOI: 10.1142/S0217595924400037
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- The Incentive to Join in Customer-to-Manufacturer (C2M) Mode: Competition
and Quality Differentiation-
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Authors: Liu Yang, Baozhen Shi, Yinan Qi
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Customer-to-Manufacturer (C2M) is an emerging business mode in which the platform analyzes customers’ information and provides suggestions to manufacturers so that the manufacturers can understand customers’ needs much better. This paper investigates C2M by considering two competing firms that sell products to customers through an e-commerce platform. One produces high-quality products (firm H), and the other produces low-quality products (firm L). The platform sells a product improvement report to the firms, each firm decides whether or not to buy the report and join in C2M mode. The study results show that buying the product improvement report gives each firm the privilege of setting a higher product price. However, such privileges are weakened when both firms buy the reports. When the report price is small, the Nash equilibrium solution is that both firms buy the product improvement report, although it is not individually optimal for each firm. It indicates that the report price influences the firms’ profits and incentive to engage in C2M mode and one firm’s purchasing decision affects the other. Interestingly, we find that a moderate report price may make firm H worse off but benefit firm L. It indicates that the C2M mode is not always appealing to firms.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-14T07:00:00Z
DOI: 10.1142/S0217595924400050
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- Semi-Additive Integer-Valued Production Technology for Analyzing Public
Hospitals in Mashhad-
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Authors: Mojtaba Ghiyasi, Wade D. Cook
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [math] rather than [math]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-14T07:00:00Z
DOI: 10.1142/S0217595924500040
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- A Drone-Driven Delivery Network Design for an On-Demand O2O Platform
Considering Hazard Risks and Customer Heterogeneity-
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Authors: Xuting Sun, Xinhang Li
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Nowadays, the online-to-offline (O2O) retailers provide on-demand delivery service for online orders by their own fleets and riders. An intelligent delivery network lays an important foundation to support cost-effective delivery service in the long run. Drones have great potential to revolutionize the instant delivery industry regarding cost and timeliness, while the hazard risks to humans and the environment should be seriously considered through sophisticated network design. In this paper, we propose a framework for a drone-driven intelligent delivery network design problem with the consideration of the multi-dimensional risk map, which needs to determine store location, drone fleet size and allocation, customer assignment, customer delivery mode selection, and delivery routing. A bi-objective non-linear programming model is formulated to maximize profit and minimize integrated risks as well. To tackle large instances, a modified NSGA-III algorithm is developed, which is incorporated with problem-specific search operators and Pareto local search to obtain Pareto solutions efficiently. Real-world data-based numerical experiments are conducted to verify the performance of the modified NSGA-III algorithm compared to the modified NSGA-II. A case study based on the geographical information in Shanghai is analyzed to validate the effectiveness of the proposed model. Moreover, sensitivity analysis is presented to evaluate the effects of multiple parameters on the drone delivery service network design. Some managerial insights are obtained for the O2O retailer who offers on-demand delivery service through online platform.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-05-10T07:00:00Z
DOI: 10.1142/S0217595924400049
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- How Does Queueing Information in Pre-Sales Call Centers Affect Customer
Repurchase Behavior-
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Authors: Tao Dai, Yidan Lu, Nikola Zivlak, Danijela Ciric Lalic, Bojan Lalic
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
With the growth of mobile e-business, it has become common for customers to experience an explosion of instant desire to consume in a short period of time. Companies are harnessing this momentum, hoping to better capture, retain and convert these consumer desires into actual sales orders. Consequently, companies also need to use pre-sales call centers to provide the satisfied service and motivate more customers to repurchase, thereby increasing the revenue. The function of the call centers is updated from providing post-sales service to providing pre-sales consultation. In this regard, this paper examines the impact of delayed announcements consisting of queue information on customer repurchase behavior in pre-sales call centers. We classify the queuing information into no-information, part-information and full-information according to the level of information by constructing a simulation model. In the simulation experiments, some application scenarios are set up to describe the load of the call center. In all the given scenarios, we find out the full-information, which provides the delay time, is always the best. Moreover, the different methods for estimating the delay time to provide the full-information, namely LES, EA, EA2 and WA–LES, are compared. We find optimal announcements for the application scenarios set out in the time-varying scenario as well. Consequently, considering there exists certain cost for the company to make WA–LES announcements, while some call centers can only provide part-information instead, this paper investigates the impact of estimate bias on customer repurchase behavior and company revenue. Subsequently, the company revenue is further investigated in two extended experiments, where repurchase influence power and repurchase number are limited. The analysis ideas and simulation logic of this paper have good reference significance for e-commerce pre-sales call centers, and the suggestions for setting information levels in practice while considering customer repurchase behavior are also clearly given in the conclusion of the paper.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-04-22T07:00:00Z
DOI: 10.1142/S0217595924400062
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- An Optimal Computing Allocation Strategy for Swarm-Based Algorithm in
Simulation-Based Optimization Problem with Stochastic Constraint-
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Authors: Chun-Chih Chiu, Chyh-Ming Lai, Yu-Chi Liu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
For realizing smart manufacturing, recent studies have been focusing on the capabilities of digital twins (DTs) that offer a virtual portrayal of a process or system and thereby facilitate real-time monitoring, analysis, and optimization. DTs construct a “digital model” of factories by using advanced technologies such as smart sensors, Internet of Things (IoT), artificial intelligence (AI) techniques, and optimization processes for executing critical decisions for enhancing productivity and predicting future events. Therefore, simulation-based optimization techniques play key supporting roles in the decision-making process of the DT shop. Thus, to enhance competitiveness in industrial companies, discourse on properly designing simulation-based optimization techniques begets significant study. This study aims to address a simulation-based optimization problem with stochastic constraint (SO[math] using a hybrid particle swarm optimization (PSO). Given the large solution space, the PSO supports design space exploration. However, since all solutions must be verified for feasibility, the optimal computing allocation strategy (OCAS) strategy for SO[math] is proposed to allocate the computing budget efficiently. OCAS is an improved version of optimal computing budget allocation for constrained optimization (OCBA-CO), specifically designed for complex systems with an expansive design space that possesses multiple local optimal solutions. During a typical PSO algorithm searching process, OCBA-CO is applied separately for each generation, resulting in a significant number of replications merely distinguishing similar solutions. To overcome this problem, our study proposes the PSO[math], which combines PSO with OCAS to avoid wasting simulation runs on similar solutions. Two quantitative experiments are performed to assess the efficacy of PSO[math] compared to other competitors. The simulation results indicate that the PSO[math] algorithm presents a competitive and superior solution in terms of both quality and searching efficiency.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-03-22T07:00:00Z
DOI: 10.1142/S0217595924400025
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- An Orthonormalization-Free and Inversion-Free Algorithm for Online
Estimation of Principal Eigenspace-
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Authors: Siyun Zhou, Liwei Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
In this paper, we study the problem of estimating the principal eigenspace over an online data stream. First-order optimization methods are appealing choices for this problem thanks to their high efficiency and easy implementation. The existing first-order solvers, however, require either per-step orthonormalization or matrix inversion, which empirically puts pressure to the parameter tuning, and also incurs extra costs of rank augmentation. To get around these limitations, we introduce a penalty-like term controlling the distance from the Stiefel manifold into matrix Krasulina’s method, and propose the first orthonormalization- and inversion-free incremental PCA scheme (Domino). The Domino is shown to achieve the computational speed-up, and own the ability of automatic correction on the numerical rank. It also maintains the advantage of Krasulina’s method, e.g., variance reduction on low-rank data. Moreover, both of the asymptotic and non-asymptotic convergence guarantees are established for the proposed algorithm.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-03-07T08:00:00Z
DOI: 10.1142/S0217595924500015
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- Strategic Technology Innovation of a Basic Product in an E-Commerce
Platform Supply Chain with a Potential Entrant in a Complementary Goods
Market-
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Authors: Beibei Shang, Jiajia Nie, Qiang Guo, Xiaoxuan Xu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper examines the impact of a potential entrant’s entry into the complementary goods market on the technology innovation of the basic product. We focus on an e-commerce platform supply chain comprising a manufacturer, an e-commerce platform, and a potential entrant. The manufacturer distributes a basic product and a complementary product through the e-commerce platform. The manufacturer has the option to improve the quality of the basic product through technology innovation. The determination of whether the potential entrant enters the market for complementary goods is contingent upon its observation of the manufacturer’s technology innovation strategy. Upon the potential entrant’s entry into this market, competition ensues between the potential entrant and the manufacturer in the realm of the complementary goods market. Our findings reveal a trade-off: on the one hand, the manufacturer’s decision to forgo technology innovation results in reduced profit, while, on the other hand, technology innovation attracts the potential competitor to enter the complementary goods market, which also leads to decreased profit. Under certain circumstances, the loss incurred by the former is less than that incurred by the latter. Consequently, the manufacturer may choose to forego technology innovation to prevent the entry of the potential entrant in the complementary goods market. Furthermore, our study demonstrates that the entry of the potential entrant can yield a mutually beneficial outcome for the manufacturer, the platform, and the potential entrant, creating a win–win–win scenario. In addition, our results suggest that as the production cost of innovative products rises, the manufacturer may have a stronger incentive to intensify its innovation efforts in response to the potential entrant’s exit from the market. Finally, we establish that our main results remain robust even when some of our initial assumptions are relaxed.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-02-29T08:00:00Z
DOI: 10.1142/S0217595924400013
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- The Minmax Regret Scheduling-Location Problem on Trees with Interval-Data
Edge Lengths-
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Authors: Huy Minh Le, Kien Trung Nguyen, Liem Dinh Tien
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
We address in this paper a variant of the scheduling-location (ScheLoc) problem on tree networks with interval edge lengths where the total deviation of the uncertain data cannot exceed a threshold. We further use the minmax regret concept to deal with the corresponding uncertainty. In order to solve the problem, we investigate the structure of the schedule which leads to the maximum regret value at a fixed point. Then we consider the machine location belonging to a specific edge of the tree and partition the underlying edge into regions with linear maximum regret function. Finally, we develop a combinatorial algorithm that solves the minmax regret ScheLoc problem in polynomial time based on a finite dominating set approach.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-02-29T08:00:00Z
DOI: 10.1142/S0217595924500027
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- No-Idle Flow Shop Scheduling with Deteriorating Jobs and Common Due Date
Under Dominating Machines-
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Authors: Dan-Yang Lv, Ji-Bo Wang
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
The phenomenon of deterioration effects can reduce production efficiency caused by extended start time or other factors. Based on it, this paper considers the scheduling situations of the common due date assignment in a no-idle flow shop scheduling environment. Under the given four dominating relationships (i.e., increasing, decreasing, increasing-decreasing and decreasing-increasing dominating machines, correspondingly expressed as idm, ddm, idm–ddm and ddm–idm) between machines, the objective functions are, respectively, to minimize (1) the weighted sum of common due date and total earliness; and (2) the weighted sum of common due date, total earliness and total penalty factors of tardy jobs. It is verified that these problems can be solved in polynomial time under four dominating relationships.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-02-29T08:00:00Z
DOI: 10.1142/S0217595924500039
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- Unification of Higher-Order Dual Programs Over Cones
-
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Authors: S. Saini, N. Kailey
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper unified Wolfe and Mond–Weir-type higher-order symmetric dual multiobjective programs over arbitrary cones. The usual duality theorems are proved for unified dual programs under the assumptions of higher-order [math]-[math]-convexity. Several known results are obtained as special cases. An example is given to show how higher-order Wolfe and Mond–Weir-type symmetric duals can be extracted from our model. An example is presented to show the existence of higher-order [math]-[math]-convex functions.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-02-27T08:00:00Z
DOI: 10.1142/S0217595923500422
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- Online Portfolio Selection Strategy with Side Information Based on
Learning with Expert Advice-
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Authors: Xingyu Yang, Xiaoteng Zheng, Lina Zheng
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Online portfolio selection is a decision-making problem of how to dynamically adjust asset positions based on historical price sequences. The existing works on online portfolio strategy are based on integrating static experts whose advice does not change with market states, which is inconsistent with the advice from dynamic experts in reality. In this paper, we propose a new online portfolio strategy by using side information states to characterize market states, getting advice from dynamic experts who dynamically adjust the strategy based on side information states and integrating their advice. To do so, we first regard all the state constant rebalanced portfolio strategies, which make portfolios dynamically according to side information states, as expert advice. Second, we apply the exponentially weighted average (EWA) algorithm to integrate the advice from the experts and propose a new strategy. Then, we prove that the strategy has the same asymptotic average logarithmic return rate as the best state constant rebalanced portfolio (BSCRP) strategy, i.e., it is universal. Finally, we conduct numerical experiments by using actual financial data from Chinese and American markets. The results indicate that our proposed strategy has good competitive performance.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-02-05T08:00:00Z
DOI: 10.1142/S0217595923500410
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- Research on Multiple Slack Due-Date Assignments Scheduling with
Position-Dependent Weights-
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Authors: Ji-Bo Wang, Han Bao, Congying Wan
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper considers a single-machine scheduling problem where jobs have multiple slack due-date assignments, i.e., some jobs have a common flow allowance and all jobs have [math] ([math] is a given constant) common flow allowances. The objective is to determine the multiple common flow allowances and optimal job sequence that minimizes the weighted sum of earliness-tardiness penalty and slack due-date assignment cost, where the weights only depend on their positions in a sequence, i.e., the position-dependent weights. Optimal properties are given for this problem, and then the problem is shown to be polynomially solvable when the number of multiple slack due-date assignments is a given constant. The model with multiple slack due-date assignments can also be extended to position (time)-dependent scheduling.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-01-27T08:00:00Z
DOI: 10.1142/S0217595923500392
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- Mathematical Models and Optimal Algorithms for Lot Scheduling Considering
Job Splitting and Due Dates in Green Logistics-
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Authors: Ming Liu, Zhongzheng Liu, Feifeng Zheng, Chengbin Chu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Lot scheduling is a promising manufacturing mode in green logistics that can efficiently save energy and reduce production costs. It has been widely applied to integrate circuit tests in semiconductor factories, textile processing in garment workshops, etc. Each processing lot is of a fixed capacity and identical processing time, and completes more than one job simultaneously. Jobs with sizes and due dates are allowed to be arbitrarily split and processed in consecutive lots. They are delivered immediately upon completion. To the best of our knowledge, in the domain of lot scheduling, there exist no mathematical programming models that describe the above features simultaneously. In this work, we focus on the single machine environment and mainly consider two lot scheduling problems with the objectives of minimizing the maximum lateness and the total tardiness, respectively. For the problems, we first propose new mixed integer linear programming models (solved by commercial solvers), which enable a systematic understanding of the studied problems and serve as a mathematical programming basis for more complicated problems. We then prove that the Earliest Due-Date (EDD) first rule and the Shortest Processing Time (SPT) first rule can optimally solve the two problems, respectively, provided that the due dates and job sizes are agreeable, i.e., a later due date indicates a larger size of job. Experimental results show the efficiency of our methods and managerial insights are drawn.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-01-19T08:00:00Z
DOI: 10.1142/S0217595923500409
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- Global Robust Newsvendor Operation Strategy for a Two-Market Stochastic
Inventory System-
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Authors: Xiaoli Yan, Hui Yu
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
Global markets increase sales and profitability opportunities for enterprises, but more environmental uncertainty poses new challenges for operational planning. This paper attempts to introduce the idea of distributionally robust optimization into the global operation problem of a two-market stochastic inventory system, providing theoretical guidance and reference decision-making for enterprises to optimize and configure in a global market with non-overlapping geographic locations and sales seasons. We find that the demand correlation and the lack of demand information will not substantially affect the operation strategy, and the enterprise’s industrial chain and supply chain remain stable. However, reducing inter-market tariffs or logistics costs will lead to a change in strategy, and the existence of the secondary market will lead to more capacity planning in the primary market. In addition, we find that enterprises’ transshipment strategies rely significantly on exchange rate volatility. Numerical experiments were conducted to demonstrate our theoretical results.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-01-10T08:00:00Z
DOI: 10.1142/S0217595923500380
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- Logistics Service Openness Strategy of Online Platforms with Vertical
Differentiation and Endogenous Service Level-
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Authors: Yihong Hu, Yongrui Duan, Shengnan Qu, Jiazhen Huo
Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print.
This paper explores the strategic motivation for a platform to open its superior logistics service to a third-party seller with an endogenous service level. We consider a Stackelberg game between the platform and the seller who sells products to consumers who perceive the platform’s product as having a higher value than the seller’s product. We characterize the equilibrium results in two schemes regarding opening or not opening the service and present conditions for the platform to open the service and the seller to accept the service. In equilibrium, the platform’s logistics service remains at the same level before and after opening. Particularly, we demonstrate that the motivation for the platform to open the service is not simply collecting extra revenue from the service but can be understood from mitigating price competition and securing its demand and price. We find that the platform is always willing to open the logistics system because it provides the platform an additional tool to manipulate the seller’s pricing behavior and therefore improves its own profit. With a high commission rate, the platform is even willing to subsidize the seller for using the logistics service. A Pareto improvement can be realized for two firms when consumers are highly sensitive to the service level. Consumers are worse off after service opening in most cases. Our analysis offers insights into the incentives of one retailer providing high-quality service for its rival when retailers differentiate in price and service.
Citation: Asia-Pacific Journal of Operational Research
PubDate: 2024-01-03T08:00:00Z
DOI: 10.1142/S0217595923400225
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