Annals of Operations Research最新文献

In recent years, production scheduling problems have increasingly incorporated real-world constraints. Within this domain, green scheduling has garnered significant attention among operational research practitioners. This class of problems typically involves various resource constraints, often pertaining to energy consumption or carbon emissions. This study addresses single-machine scheduling problems with controllable processing times and limited resource consumption. It considers distinct operation modes within the machine and non-renewable resources associated with job processing. We evaluate two performance measures: the minimization of the makespan and total tardiness, both belong to the NP-hard class of combinatorial optimization problems. Due to the complexity of the studied variants, this study develops two mixed-integer linear programming (MILP) models and three constraint programming (CP) models for the problems under study. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Average Relative Deviation Index (ARDI). Concerning the performance indicators, the third CP formulation achieved an ARPD of 10.63% for makespan minimization. In comparison, the second CP formulation achieved an ARDI of 2.51% for total tardiness minimization, highlighting the superiority of these solution approaches for each respective objective function.

We propose a new algorithm for minimizing a nonsmooth convex function f, applying one step of the fast gradient algorithm by Nesterov to the Moreau-Yosida regularization of the so-called Elzinga-Moore-Ouorou function, as first termed by Antonio Frangioni. In contrast with our previous fast proximal algorithms for nonsmooth convex optimization based on the Moreau-Yosida regularization of f, the proposed algorithm uses Chebychev centers as trial solutions, and has a descent property. Moreover, the generated approximate solutions are shown to converge to an optimal solution. The complexity bound has the accelerated convergence rate of the fast gradient algorithm, plus an error term that depends on the accuracy achieved at all subproblems solved in past iterations. From the theoretical development, we derive some practical choices for the proximity parameter. For numerical illustration, we consider the Held and Karp dual of a traveling salesman problem with datasets from TSPLIB, and some small-size academic test problems. The numerical experiments, although limited, indicate the relevance of the approach on large-scale problems in comparison with our previous proximal algorithm which is also based on Chebychev centers.

Sustainable Development Goals (SDGs) initiated by the United Nations focus on addressing various social, economic, and environmental challenges faced globally, such as sustainable production practices (Goal 12) and greenhouse gas emissions (Goal 13). In light of these objectives, we examine a dual-channel supply chain where a manufacturer offers green products via an online retailer (e-tailer) and an independent offline retailer. In this paper, we significantly contribute by investigating the manufacturer’s joint decisions on green product development, pricing, and the online selling format choice among direct selling, reselling, and agency selling. We make another contribution by exploring the role of consumer green awareness and channel competition. Our research results show that the optimal product greenness and selling format depends on the green cost factor, consumer green awareness, e-tailer agency fees, direct sales cost, and channel competition. In particular, the manufacturer will choose to open an online channel to sell green products if and only if consumer green awareness is high. Promoting consumer green awareness is also essential for green consumption in SDGs. Second, when the agency fee is lower, agency selling will lead to more environmentally friendly products, which benefits the manufacturer. Conversely, when the direct selling cost is lower and the agency fee is higher, direct selling allows the manufacturer to produce more environmentally friendly products and improve profitability. This helps the manufacturer produce green products to achieve Goal 13 in SDGs. Third, when consumer green awareness is low, and the manufacturer opens an online channel to sell green products, it will enhance consumer demand for offline non-green products, benefiting the traditional retailer. Finally, we conduct numerical studies to illustrate our analytical results and provide additional management insights.

This paper examines the interdependence of international success and budget concentration for sports clubs. More specifically, we apply the notion of the Herfindahl–Hirschman index to examine the effect of international rewards on distortion of budget concentration and financial opportunities for sports clubs. We establish a relationship between the size of rewards and its impact on the index, thus deriving conditions under which budget concentration increases or decreases. Our results yield interesting policy implications for the regulation of prize schemes in international competitions.

The rising popularity of e-commerce is attracting many manufacturers and their traditional offline retailers to online sales. However, as the entry rules highlight that downstream online selling requires upstream authorization, this study investigates a manufacturer’s decisions on channel selection and whether to authorize the retailer to sell online. We consider a multi-stage Stackelberg game that involves a hybrid e-commerce platform, a brand manufacturer, and a traditional retailer. The manufacturer determines (1) whether and how to sell online and (2) whether to authorize the retailer to sell online. The results show that the manufacturer with his dominance in authorization selects the reselling channel if the online selling cost is moderate, and the agency channel otherwise. However, the manufacturer does not allow the retailer to sell online, given the intense competition among online channels, if the selling cost is low and the referral fee rate is high, even though the retailer benefits from authorization in terms of reduced double marginalization. Moreover, considering the possibility that firms may have different strategic positions and the manufacturer may charge a licensing fee, this study is extended to identify the effects of (1) the platform’s dominance in determining whether to provide the manufacturer with a reselling channel, (2) the retailer’s dominance in determining whether to allow the manufacturer to sell online, and (3) the existence of a licensing fee. Our findings provide important practical implications for firms seeking to expand their presence online.

This study aims to solve the problem of the site selection and distribution of emergency stockpiles during an epidemic outbreak. To this end, this study takes the demand prediction result of the Susceptible-Exposed-Infectious-Recovered-Dead epidemic model as the basis for site selection and constructs a double-layer model of site selection and the distribution of emergency tertiary logistics, with “strategic national stockpile–regional distribution center–demand point" as the main line. Considering the capacity limitation, coverage radius, and dispatch scheduling of different models of vehicles, the upper layer aims to minimize the sum of various types of costs, while the lower layer aims to minimize the average distribution time. Focusing on the NP-hard characteristics of the problem, based on the basic life choice–based optimizer, a nonlinear convergence factor and an enhanced search levy flight strategy are introduced to design a novel life choice–based optimizer to solve the upper layer model; the lower layer is solved using the Floyd’s algorithm. Real data for Changchun City during the Coronavirus disease 2019 epidemic in 2022 are used for empirical research. The joint optimization algorithm of the novel life choice–based optimizer and Floyd’s algorithm is compared with other algorithms. The experimental results show the feasibility of the new model and the effectiveness of the new algorithm.

High-volume containers, which arise from extensive trade activities, often share identical attributes, thereby enabling their substitutability. Such substitutability enhances the optimization potential of scheduling yard cranes (YCs) and automated guided vehicles (AGVs) by allowing for the efficient handling of containers with the same size, weight, and destination. This study proposes a novel scheduling method to minimize handling time and reduce delays by optimizing the sequence of containers with identical attributes. Mixed-integer programming models are formulated to address the scheduling problem of non-cantilevered YCs and multi-load AGVs in a perpendicular automated container terminal. Subsequently, we designed a hybrid precise-heuristic algorithm. The YC scheduling and slot allocation models are solved by commercial software to obtain the optimal solution. Additionally, an adaptive variable neighborhood multi-population genetic algorithm is developed to generate high-quality satisfactory solutions for AGV scheduling within a feasible time frame. The results indicate that leveraging container substitutability during loading and unloading processes can effectively reduce the total handling cost by up to 8.9%.

The fierce market competition leads firms that attempt to enter a market to carefully consider their timing of releasing a new product. As rivals to new entrants, incumbents need to continuously upgrade the technology level of their future products. However, extant studies have failed to reveal the interaction between an entrant’s decision regarding product release timing and an incumbent’s technology-upgrading strategy (reducing cost or improving quality). Therefore, our paper develops a duopolistic game model to address this issue and provides related managerial insights. Our paper shows that the incumbent’s different strategies could lead to the differentiated impact on the entrant’s timing decision for product release. While in certain condition, regardless of the incumbent’s technology upgrade plan, the entrant firm might not postpone product release even if he could produce with low cost in the future. Furthermore, an interesting result is that the incumbent can also benefit from the cost-reducing strategy even if the scale parameter for cost-reduction R&D increases to be very large, i.e., the incumbent also obtains higher profit than that under quality- improving strategy. Finally, we extend a model where the entrant can engage in technological upgrade to compete against incumbent in future and find that adopting a technology upgrade strategy different with the incumbent firm is more likely to benefit the entrant.

This special issue of Annals of Operations Research presents a collection of 31 papers that emerged from the 18th European Meeting on Game Theory (SING 18), held in Messina, Italy, in June 2023. The contributions span the full breadth of game-theoretical research, from foundational developments in cooperative and non-cooperative game theory to innovative applications in supply chain management, healthcare, finance, environmental policy, and emerging technologies. This collection demonstrates the vitality and relevance of game theory in addressing contemporary challenges across diverse domains, highlighting the synergy between theoretical advances and practical problem-solving. The papers showcase the evolution of game-theoretical methods, incorporating modern computational techniques including artificial intelligence, deep learning, and quantum computing, while maintaining strong connections to classical game-theoretic principles.

Remanufacturing-as-a-Service promises scalable, lower-cost repair of metal components by coordinating distributed capabilities through a cloud platform. This paper formulates a quantitative, supply-chain optimization of cloud-enabled remanufacturing built on a hybrid additive–subtractive (ASM) repair cost model and evaluates three delivery scenarios: (i) centralized national hubs with hybrid ASM; (ii) collaborations with tier-one local facilities that own separate AM and SM equipment; and (iii) collaborations with tier-two local facilities that provide only partial capability, requiring multi-facility routing. Using a U.S. tooling and machinery case study, this paper quantitatively fuses stepwise ASM repair economics with strategic facility location under an explicit quality–cost trade-off, closing a gap between process modeling and supply-chain design in remanufacturing. Results show that across plausible freights and capital costs, decentralized collaborations remain within 5% of the cost optimum while improving regional responsiveness; longer planning horizons shift the near-optimal set toward 5–8 hubs, rather than a single national site. Sensitivity tests confirm that (a) higher transportation costs and longer horizons favor additional hubs, while (b) tighter accuracy thresholds steer volume to hybrid hubs or vetted tier-one partners. The model provides clear trigger points—based on accuracy targets, capital expenditures, and freight conditions—for deciding when to build new hubs versus contracting with local partners. It offers managers a robust tool to navigate the critical trade-offs between cost efficiency, service reach, and quality.