{"title":"Technology choice under the cap-and-trade policy: The impact of emission cap and technology efficiency","authors":"Shuhui Dong, Xiaole Wu","doi":"10.1016/j.ejor.2025.04.029","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.029","url":null,"abstract":"This paper studies how competing firms make technology choices and production decisions under the cap-and-trade policy when they engage in both product and emission trading markets. Using a two-stage game theoretical model, we analyze firms’ responses to stricter emission caps and efficiency improvements of clean technology. Interestingly, we identify a “reverse trading” phenomenon where the firm with traditional technology and hence higher emission intensity sells emission allowances to the firm with clean technology because the latter operates with a higher profit margin and can afford a higher premium for allowances. Furthermore, stricter regulations incentivize firms to adopt clean technology only if the technology efficiency exceeds a certain level. Otherwise, no matter how low the emission cap is, neither firm will adopt it due to higher production costs. Additionally, cleaner technology does not necessarily provide firms greater incentives to adopt it. The efficiency of clean technology has a non-monotonic effect on firms’ adoption incentives because it not only affects the adopting firm but also has a positive spillover effect on the firm using traditional technology through emission trading. From a regulatory perspective, we propose setting a moderate emission cap to maximize social welfare, and as clean technology becomes more efficient, the optimal cap should be further tightened when the technology is already highly efficient. These findings provide practical insights for policymakers in designing the cap-and-trade policy tailored to different levels of technology improvements.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"14 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal insurance design with Lambda-Value-at-Risk","authors":"Tim J. Boonen, Yuyu Chen, Xia Han, Qiuqi Wang","doi":"10.1016/j.ejor.2025.04.038","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.038","url":null,"abstract":"This paper explores optimal insurance solutions based on the Lambda-Value-at-Risk (<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mrow><mml:mi mathvariant=\"normal\">Λ</mml:mi><mml:mi mathvariant=\"normal\">VaR</mml:mi></mml:mrow></mml:math>). Using the expected value premium principle, we first analyze a stop-loss indemnity and provide a closed-form expression for the deductible parameter. A necessary and sufficient condition for the existence of a positive and finite deductible is also established. We then generalize the stop-loss indemnity and show that, akin to the VaR model, a limited stop-loss indemnity remains optimal within the <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mrow><mml:mi mathvariant=\"normal\">Λ</mml:mi><mml:mi mathvariant=\"normal\">VaR</mml:mi></mml:mrow></mml:math> framework. Further, we examine the use of <mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">Λ</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup><mml:mi mathvariant=\"normal\">VaR</mml:mi></mml:mrow></mml:math> as a premium principle and show that full or no insurance is optimal. We also identify that a limited loss indemnity is optimal when <mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">Λ</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup><mml:mi mathvariant=\"normal\">VaR</mml:mi></mml:mrow></mml:math> is solely used to determine the risk-loading in the premium principle. Additionally, we investigate the impact of model uncertainty, particularly in scenarios where the loss distribution is unknown but lies within a specified uncertainty set. Our findings suggest that a limited stop-loss indemnity is optimal when the uncertainty set is defined using a likelihood ratio. Meanwhile, when only the first two moments of the loss distribution are available, we provide a closed-form expression for the optimal deductible in a stop-loss indemnity.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Distributionally robust scheduling for the two-stage hybrid flowshop with uncertain processing time","authors":"Zhi Pei, Rong Dou, Jiayan Huang, Haimin Lu","doi":"10.1016/j.ejor.2025.04.037","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.037","url":null,"abstract":"In the present paper, we investigate the two-stage hybrid flowshop with uncertain processing time. The true probability distribution of the processing time is unknown, but the statistical features can be extracted from historical data, such as the mean, lower and upper bounds. To obtain the exact scheduling result, a distributionally robust optimization (DRO) model is built to minimize the worst-case expected makespan. Then the inner problem is further reformulated as a minimization problem with a fixed sequence based on duality theory and the totally unimodular property. In addition, valid lower and upper bounds are introduced to transform the DRO model into an equivalent mixed-integer linear programming (MILP) problem with McCormick inequalities, which can be handled directly with the off-the-shelf commercial solvers. The numerical analysis demonstrates the higher computational efficiency of the DRO-based model compared with its stochastic programming (SP) counterpart. In particular, the DRO model consistently outperforms the SP model in terms of worst-case indicators. And in most cases, the DRO model triumphs the SP model in terms of average, up-quartile and up-decile indicators. Moreover, the optimal schedule obtained by the DRO model demonstrates stronger stability compared with the deterministic model. These features shed light on the principles behind reliable schedules for the two-stage hybrid flowshop scheduling model, thereby enhancing the robustness of the manufacturing system in the face of process uncertainty.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"15 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuehui Wu, Hui Fang, Ali Gul Qureshi, Tadashi Yamada
{"title":"Capacitated hub location routing problem with time windows and stochastic demands for the design of intra-city express systems","authors":"Yuehui Wu, Hui Fang, Ali Gul Qureshi, Tadashi Yamada","doi":"10.1016/j.ejor.2025.05.006","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.05.006","url":null,"abstract":"This work focuses on planning an intra-city express system in a practical environment. Various operation characteristics, such as vehicle capacity, hub capacity, time windows, and stochastic demands, have been considered. Therefore, we introduce a capacitated hub location routing problem with time windows and stochastic demand and formulate it using a multi-stage recourse model. In this model, long-term decisions (hub location and client-to-hub allocation) are made first, and short-term decisions (vehicle routing) are determined after revealing stochastic variables. To solve the problem, we propose a hybrid stochastic variable neighbourhood search (HSVNS) algorithm, which integrates an adaptive large neighbourhood search (ALNS) algorithm within a stochastic variable neighbourhood search (SVNS) framework. Numerical experiments and case studies indicate that the HSVNS algorithm can provide high-quality solutions within a reasonable computation time for instances with up to 70 clients and that considering stochastic factors can efficiently reduce operation costs, especially for instances with tight vehicle capacity and loose time windows.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"47 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Infeasibility conditions and resolution strategies for super-efficiency models under weak disposability and null-jointness: A directional distance function approach with endogenous directions","authors":"Ruiyue Lin, Zongxin Li","doi":"10.1016/j.ejor.2025.04.039","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.039","url":null,"abstract":"Existing studies have not focused on the infeasibility of super-efficiency models under the weak disposability and null-jointness (WDJ) assumptions, despite the wide adoption of these two conditions in fields where undesirable outputs exist, like the evaluation of energy and environmental efficiency. This paper employs a directional distance function (DDF) approach to investigate super-efficiency feasibility under these assumptions. We note that DDF-based super-efficiency models using frequently-used exogenous directions may encounter infeasibility under the WDJ assumptions, even when constant returns to scale (CRS) are assumed. We present the specific conditions that lead to this infeasibility. By utilizing endogenous directions, we construct a feasible DDF-based CRS super-efficiency model under the WDJ assumptions, ensuring that the DDF super-efficiency scores remain within a maximum of 1. We also find that the DDF-based super-efficiency model under the WDJ assumptions is infeasible in certain cases when variable returns to scale (VRS) are assumed, regardless of whether directions are endogenous or exogenous. To address this issue, we propose a modified DDF-based VRS super-efficiency model that aims to maintain the WDJ assumptions as much as possible. This VRS model ensures feasibility and generates DDF super-efficiency scores below 1. Some properties of the models and their relationships are discussed. Finally, several examples and a real case from the literature validate the feasibility and practical applicability of the proposed models.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"17 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Branch-and-cut-and-price for agile earth observation satellite scheduling","authors":"Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen","doi":"10.1016/j.ejor.2025.04.014","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.014","url":null,"abstract":"The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth’s surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera’s look angles. Second, the time windows of each target vary during different observation cycles, called “orbits”. Some targets are invisible during certain orbits. We call this variant the Time-dependent Team Orienteering Problem with Variable Time Windows. In this paper, we present an efficient branch-and-cut-and-price (BCP) algorithm that exploits the problem’s characteristics to solve it to optimality. Some algorithmic enhancements have been implemented, such as a Lagrangian bound, an ng-path relaxation, a primal heuristic, and subset-row inequalities. Extensive experiments on different configurations of benchmark instances demonstrate the superior performance of the proposed BCP algorithm and its algorithmic enhancements. Moreover, the primal heuristic yields a high-quality lower bound and outperforms state-of-the-art heuristics. Finally, we adopt our framework to solve the well-known TOPTW, and our algorithm is much faster than state-of-the-art exact algorithms.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"19 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A novel centralized cross-efficiency evaluation via explainable artificial intelligence in the context of big data","authors":"Min Yang, Zixuan Wang, Liang Liang","doi":"10.1016/j.ejor.2025.05.012","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.05.012","url":null,"abstract":"Cross-efficiency evaluation in data envelopment analysis (DEA) assumes that decision making units (DMUs) have full flexibility in choosing weights according to their individual preferences. However, this total autonomy may be inapplicable in some centralized organizational scenarios. To address this problem, this paper introduces a novel centralized cross-efficiency evaluation which considers both individual and organizational preferences with assistance of explainable artificial intelligence (XAI) in the context of big data. Specifically, XAI is first applied to approach the organizational efficiency function and then calculate the marginal contribution of each variable as the variable importance, which represents the organizational preference. Furthermore, we propose a centralized secondary goal model to select the <ce:italic>unique</ce:italic> optimal weight profile from the candidate weights that remain self-efficiency as Pareto-optimal, such that the deviation between individual and organizational preferences is minimized. In addition, a centralization factor is introduced to ensure that the model's centralization degree corresponds to the actual centralization level in organizational management. Finally, the proposed method is applied to evaluate the efficiency of DMUs within three different centralized organizations sequentially. The results verify that the proposed method yields more discriminative and robust efficiency scores within organizations compared to several previous cross-efficiency evaluation methods.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"74 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Lim Kwan Hui, Pieter Vansteenwegen
{"title":"Deep reinforcement learning for solving the stochastic e-waste collection problem","authors":"Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Lim Kwan Hui, Pieter Vansteenwegen","doi":"10.1016/j.ejor.2025.04.033","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.04.033","url":null,"abstract":"With the growing influence of the internet and information technology, Electrical and Electronic Equipment (EEE) has become a gateway to technological innovations. However, discarded devices, also called e-waste, pose a significant threat to the environment and human health if not properly treated, disposed of, or recycled. In this study, we extend a novel model for the e-waste collection in an urban context: the Heterogeneous VRP with Multiple Time Windows and Stochastic Travel Times (HVRP-MTWSTT). We propose a solution method that employs deep reinforcement learning to guide local search heuristics (DRL-LSH). The contributions of this paper are as follows: (1) HVRP-MTWSTT represents the first stochastic VRP in the context of the e-waste collection problem, incorporating complex constraints such as multiple time windows across a multi-period horizon with a heterogeneous vehicle fleet, (2) The DRL-LSH model uses deep reinforcement learning to provide an online adaptive operator selection layer, selecting the appropriate heuristic based on the search state. The computational experiments demonstrate that DRL-LSH outperforms the state-of-the-art hyperheuristic method by 24.26% on large-scale benchmark instances, with the performance gap increasing as the problem size grows. Additionally, to demonstrate the capability of DRL-LSH in addressing real-world problems, we tested and compared it with reference metaheuristic and hyperheuristic algorithms using a real-world e-waste collection case study in Singapore. The results showed that DRL-LSH significantly outperformed the reference algorithms on a real-world instance in terms of operating profit.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"12 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Circular economy application in pharmaceutical supply chains in the UK: a holistic evolutionary game approach","authors":"Nazanin Nami, Grigory Pishchulov, Joao Quariguasi Frota Neto","doi":"10.1016/j.ejor.2025.05.009","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.05.009","url":null,"abstract":"The environmental hazards of improperly managed waste have gained universal recognition among scholars and stakeholders. These hazards are especially critical in the pharmaceutical sector since leftover medications contain active chemicals that threaten the environment and human health. Nonetheless, implementation of adequate measures to ensure proper collection and treatment of pharmaceutical leftovers remains insufficient, and tons of unwanted medications are discarded in landfills and wastewater annually. Such outcomes are due to lack of coordination between the parties involved and poor incentive systems in place. To address this issue, we study coordination in pharmaceutical reverse supply chains and government incentive strategies. We employ the evolutionary game methodology to evaluate strategic behaviour of pharmacies and a waste recycler under different incentive plans. We are focusing on both reward- and awareness-driven customer segments to boost the return volume of unwanted medications. Moreover, supply chain coordination is investigated as a tool to enhance the economic viability of the system. We compare the incentive plans based on return volume, participation rate, budget spend, and implementation time, to recommend the most effective plan. An extensive numerical study provides insights into the performance of the incentive plans in different conditions. The results reveal that a plan that provides proper incentives to pharmacies for targeting both, reward- and awareness-driven customers, coupled with contract-based coordination, outperforms other plans, and does not necessarily require a budget allocation. Our study is motivated by the UK’s National Health System but it is generalisable to pharmaceutical reverse supply chains in other countries as well.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"127 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Moral hazard in data envelopment analysis benchmarking","authors":"Xiangyang Tao, Qiaoyu Peng","doi":"10.1016/j.ejor.2025.05.001","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.05.001","url":null,"abstract":"This paper delves into the concept of moral hazard in data envelopment analysis (DEA) benchmarking. The moral hazard issue emerges when decision-making units (DMUs) conceal their actions in the application of best practices, driven by the costs involved and the possibility of incomplete reimbursement. This issue remains unexplored in DEA benchmarking because previous studies assume that applying best practices is straightforward once these practices have been identified. Therefore, we postulate the presence of information asymmetry pertaining to the optimal production possibilities of DMUs, and regard applying best practices in benchmarking as a moral hazard issue. To address this issue, we formulate an incentive game and propose efficient contracts, where DEA Russell-like measures are first employed to describe DMUs’ effort levels. We prove applying best practices is the dominate strategy equilibrium of the incentive game with the implementation of efficient contracts. By exploring moral hazard in DEA benchmarking, this paper recommends the managers to incorporate considerations of information asymmetry when embarking on benchmarking activities.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"140 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}