Annals of Operations Research最新文献

Environmental regulations have led to firms considering offshoring their production to avoid compliance costs. However, overseas production comes with yield uncertainty. This study examines optimal environmental regulations and firms’ responses under three production scenarios. The government’s objective is to maximize social welfare by selecting the type and intensity of regulatory instruments, while firms seek to optimize profits by adjusting their optimal production quantities. Our analysis finds that price and quantity regulation instruments have similar impacts on social welfare, but firms prefer quantity instruments despite price instruments being more beneficial for consumers. Furthermore, both pollution damage coefficient and yield uncertainty are important factors affecting social welfare and firms’ location choice and there exist win–win situations that benefit the government, firms, and consumers. Additionally, offshoring tends to be more environmentally friendly to the local environment. To further validate the main model, we examine various extensions. Results show that the hybrid instrument does not significantly enhance social welfare, but it does offer flexibility in adjusting firms’ production transfer motivations. Additionally, factors such as positive production and fixed costs, as well as multi-market issues, do not alter the government’s preference for regulatory instruments.

Multi-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

Matheuristics are problem independent frameworks that use mathematical programming tools to obtain high quality heuristic solutions. They are structurally general enough to be applied to different problems with little adaptation to their abstract structure, so they can be considered as new or hybrid metaheuristics based on components derived from the mathematical model of the problems of interest. In this survey, we emphasize the mathematical tools and describe how they can be used to design heuristics. We focus on mixed-integer linear programming and report representative examples from the literature of how it has been used for effective heuristic optimization. References to contributions to matheuristics deriving from neighboring research areas such as Artificial Intelligence or Quantum Computing are also included. We conclude with some ideas for possible future developments. This paper extends an original version published in 4OR with new sections on CMSA, Incremental Core, AI hybrids and Quantum Heuristics, and includes references to several recent publications.

Small and medium enterprises (SMEs) in supply chains often struggle to obtain external financial resources from banks, due to their poor supply chain visibility. Prior studies suggest that adopting blockchain technology can enhance supply chain visibility, thus facilitating access to supply chain finance (SCF). In this study, we compare the traditional SCF mode with the blockchain-enabled SCF mode within the framework of a dual-channel closed-loop supply chain (CLSC). Our analysis incorporates both prepayment financing and accounts receivable financing or purchase order financing into the analytical model. Our main findings are as follows. Firstly, adopting blockchain technology leads to a reduction in the interest rate, enabling the manufacturer and the retailer to raise their buy-back prices. This increase in buy-back prices subsequently encourages greater recycling of used products. Secondly, in a context marked by high cross-price elasticity, we observe abrupt pricing fluctuations in both online and offline sales channels. However, adopting blockchain technology can effectively mitigates these fluctuations, thus ensuring the sustained stability of demand in both sales channels. Thirdly, in the blockchain-enabled mode, equilibrium wholesale price, offline sales price, manufacturer’s buy-back price, and retailer’s buy-back price are higher compared to those in the traditional mode. Conversely, the equilibrium direct sales price is lower in the blockchain-enabled mode than in the traditional mode. Fourthly, changes in interest rates and cross-price elasticity have analogous impacts on pricing dynamics in both the traditional and blockchain-enabled modes. Fifthly, adopting blockchain technology leads to an all-win outcome for participants in SCF arrangements only when the benefits of blockchain-enabled traceability and reduced interest rates outweigh the costs associated with hosting and access.

Disasters cause huge economic losses, affect the lives of many people, and severely damage the environment. Effective resource management during disaster preparedness and response phases improves distribution efforts and service levels and, hence, accelerates the disaster relief operations. Resource management in response to catastrophe has received increasing research attention in recent years, but no review paper focuses on this specific topic. Thus, the main purpose of this paper is to review the existing literature on resource management for disaster relief published in English in peer-reviewed journals in order to fill the gap. We apply bibliometric, network, and content analyses in our review to identify popular research topics, classify the literature into research clusters, and analyze the interrelationships between these research clusters. The second purpose of this paper is to identify gaps and trends in existing research. Finally, we propose six future research directions that provide a roadmap for resource management research for disaster relief.

The Colored bin packing problem (CBPP) is a generalization of the Bin packing problem (BPP). The CBPP consists of packing a set of items, each with a weight and a color, in bins of limited capacity, minimizing the number of used bins and satisfying the constraint that two items of the same color cannot be packed side by side in the same bin. In this article, we proposed an adaptation of BPP heuristics and new heuristics for the CBPP. Moreover, we propose a set of fast neighborhood search algorithms for CBPP. These neighborhoods are applied in a meta-heuristic approach based on the Variable neighborhood search (VNS) and a matheuristic approach that combines linear programming with the meta-heuristics VNS and Greedy randomized adaptive search (GRASP). The results indicate that our matheuristic is superior to VNS and that both approaches can find near-optimal solutions for a large number of instances, even for those with many items.

We study a general optimization problem in which coefficients in the objective are uncertain. We use capacities (lower probabilities) to model such uncertainty. Two popular criteria in imprecise probability, namely maximality and E-admissibility, are employed to compare solutions. We characterize non-dominated solutions with respect to these criteria in terms of well-known notions in multi-objective optimization. These characterizations are novel and make it possible to derive several interesting results. Specially, for convex problems, maximality and E-admissibility are equivalent for any capacities even though the set of associated acts is not convex, and in case of 2-monotone capacities, finding an arbitrary non-dominated solution and checking if a given solution is non-dominated are both tractable. For combinatorial problems, we show a general result: in case of 2-monotone capacities, if the deterministic version of the problem can be solved in polynomial time, checking E-admissibility can also be done in polynomial time. Lastly, for the matroid optimization problem, more refined results are also obtained thanks to these characterizations, namely the connectedness of E-admissible solutions and an outer approximation based on the greedy algorithm for non-dominated solutions with respect to maximality.

This paper mainly deals with the reliability modeling and optimal preventive replacement policies for a stochastically deteriorating system with random shocks. Specifically, the system is subject to stochastic performance deterioration, in which it is described with a Gamma process and degrades after a random degradation latency period. At the same time, a random shock process with a non-homogenous Poisson process is incorporated into system degradation modeling, where two kinds of shock effectiveness are formed upon arrival. The dependence between the degradation-induced failure and the shock-induced failure is bidirectional in this research. Based on system survival function, a periodic replacement policy and an inspection replacement policy are respectively investigated. The optimal solutions to the two preventive replacement policies are sought analytically and their resulting long-run average cost rates are compared to decide which one is more profitable. Finally, an illustrative example of the gas insulated transmission line is surveyed to validate the theoretical results, demonstrating that the random degradation onset time and two kinds of shocks are significant factors to system reliability evaluation and maintenance decisions.

Many packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.