Pub Date : 2026-07-01Epub Date: 2026-02-03DOI: 10.1016/j.omega.2026.103531
Zhanxin Ma, Jie Yin
The DEA cross-projection method effectively enhances the objectivity and feasibility of improvement targets by introducing a mutual evaluation system. However, existing methods have not fully addressed the projection points that may exceed the production possibility set. Additionally, improvement targets derived from cross-weights may lack rationality and objectivity, posing a risk of deviating from group consensus. To address these concerns, this paper proposes a novel DEA cross-projection method to rectify the feasibility issues in the original approach. Secondly, this paper presents a cross-improvement target setting method based on group consensus by increasing the collective consensus degree in determining cross-weights. Finally, the proposed method was applied to analyze the energy utilization efficiency of Chinese steel enterprises in 2020. The results indicate that the DEA cross-projection method presented in this paper not only resolves the issue of projection points potentially exceeding the production possibility set but also offers more rational improvement strategies than existing DEA cross-projections by considering weight consensus.
{"title":"DEA cross-projection method and the correction of the cross-improvement targets","authors":"Zhanxin Ma, Jie Yin","doi":"10.1016/j.omega.2026.103531","DOIUrl":"10.1016/j.omega.2026.103531","url":null,"abstract":"<div><div>The DEA cross-projection method effectively enhances the objectivity and feasibility of improvement targets by introducing a mutual evaluation system. However, existing methods have not fully addressed the projection points that may exceed the production possibility set. Additionally, improvement targets derived from cross-weights may lack rationality and objectivity, posing a risk of deviating from group consensus. To address these concerns, this paper proposes a novel DEA cross-projection method to rectify the feasibility issues in the original approach. Secondly, this paper presents a cross-improvement target setting method based on group consensus by increasing the collective consensus degree in determining cross-weights. Finally, the proposed method was applied to analyze the energy utilization efficiency of Chinese steel enterprises in 2020. The results indicate that the DEA cross-projection method presented in this paper not only resolves the issue of projection points potentially exceeding the production possibility set but also offers more rational improvement strategies than existing DEA cross-projections by considering weight consensus.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"142 ","pages":"Article 103531"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189745","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}
Nowadays, e-commerce is associated with many returns due to emotional consumption, information asymmetry, factory defects, or, more generally, customer dissatisfaction. However, little attention has been paid to reverse logistics in the e-commerce industry, although it has been proven crucial to improving the perceived quality of service and profit revenue. Depending on the nature of the goods, one successful option is to design combined forward-and-reverse logistics systems, where the collection of returns is ensured along with the traditional distribution of products, together with hub-and-spoke networks in which both distribution and collection demand from many spokes are aggregated into a few hubs. In this context, we study a variant of the vehicle routing problem with divisible deliveries and pickups, in which each hub may be associated with a mandatory delivery demand and a mandatory return pickup demand, and it may be visited more than once within the same or different routes. To address realistic scenarios, and given the large fluctuation of demand within the aggregating hubs, we also assume that an uncertain optional pickup quantity may arise and formulate the problem through two-stage Stochastic Programming, proposing and modeling ad-hoc recourse actions. Moreover, an integer L-shaped method enhanced with ad-hoc valid inequalities is developed for solving the resulting problem. Managerial insights on the underlying tactical and operational policies are inferred from extensive computational experiments on a case study and on realistic artificial instances.
{"title":"Incorporating stochastic optional pickup demand in routing operations with divisible services for hub-and-spoke e-commerce returns management systems","authors":"Alessandro Gobbi , Daniele Manerba , Francesca Vocaturo","doi":"10.1016/j.omega.2025.103510","DOIUrl":"10.1016/j.omega.2025.103510","url":null,"abstract":"<div><div>Nowadays, e-commerce is associated with many returns due to emotional consumption, information asymmetry, factory defects, or, more generally, customer dissatisfaction. However, little attention has been paid to reverse logistics in the e-commerce industry, although it has been proven crucial to improving the perceived quality of service and profit revenue. Depending on the nature of the goods, one successful option is to design combined <em>forward-and-reverse</em> logistics systems, where the collection of returns is ensured along with the traditional distribution of products, together with <em>hub-and-spoke</em> networks in which both distribution and collection demand from many spokes are aggregated into a few hubs. In this context, we study a variant of the vehicle routing problem with divisible deliveries and pickups, in which each hub may be associated with a mandatory delivery demand and a mandatory return pickup demand, and it may be visited more than once within the same or different routes. To address realistic scenarios, and given the large fluctuation of demand within the aggregating hubs, we also assume that an uncertain optional pickup quantity may arise and formulate the problem through two-stage Stochastic Programming, proposing and modeling ad-hoc recourse actions. Moreover, an integer L-shaped method enhanced with ad-hoc valid inequalities is developed for solving the resulting problem. Managerial insights on the underlying tactical and operational policies are inferred from extensive computational experiments on a case study and on realistic artificial instances.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103510"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925461","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}
Pub Date : 2026-06-01Epub Date: 2025-12-20DOI: 10.1016/j.omega.2025.103505
Bo Feng, Qingchun Meng, Guodong Yu
In disaster response, logistics management requires efficient matching among workforce, materials and tasks. Uncertain and non-stationary task arrivals, heterogeneity in workforce skill levels and uncertainty in task execution times, together with the need to coordinate materials with workforce deployment, jointly render the disaster response process highly stochastic; meanwhile, multi-organizational participation further introduces cross-organizational resource coordination challenges. Existing approaches struggle to address these jointly—stochastic demand, cross-organizational coordination, and inefficiencies from decoupled workforce–material scheduling—and often require frequent manual retuning that limits responsiveness and scale. We develop an adaptive, cross-organizational decision system that co-optimizes volunteer assignment, material allocation, and replenishment in real time. System evolution is governed by task arrivals, service completions, and inventory decay, while assignment, material allocation, and replenishment act directly on these drivers. We adopt a Markov Decision Process (MDP) framework to integrate multi-organizational collaboration, real-time resource management and task allocation, and implement an end-to-end controller via hierarchical deep reinforcement learning(HDRL) that jointly optimizes volunteer assignment, material allocation, and replenishment. Across varied demand regimes, scales, and perishability levels, the proposed joint controller consistently outperforms common queueing heuristics: task backlogs decrease by about 30–85% and personnel costs by 16–42%, while logistics and resource-usage costs remain broadly comparable, with occasional modest logistics increases that relieve congestion. Relative to short-horizon rolling dynamic programming, it achieves lower backlog and total cost with less manual re-tuning, millisecond-level inference latency, and smooth scaling.
{"title":"Joint scheduling policy for volunteers and materials in multi-organizational disaster response","authors":"Bo Feng, Qingchun Meng, Guodong Yu","doi":"10.1016/j.omega.2025.103505","DOIUrl":"10.1016/j.omega.2025.103505","url":null,"abstract":"<div><div>In disaster response, logistics management requires efficient matching among workforce, materials and tasks. Uncertain and non-stationary task arrivals, heterogeneity in workforce skill levels and uncertainty in task execution times, together with the need to coordinate materials with workforce deployment, jointly render the disaster response process highly stochastic; meanwhile, multi-organizational participation further introduces cross-organizational resource coordination challenges. Existing approaches struggle to address these jointly—stochastic demand, cross-organizational coordination, and inefficiencies from decoupled workforce–material scheduling—and often require frequent manual retuning that limits responsiveness and scale. We develop an adaptive, cross-organizational decision system that co-optimizes volunteer assignment, material allocation, and replenishment in real time. System evolution is governed by task arrivals, service completions, and inventory decay, while assignment, material allocation, and replenishment act directly on these drivers. We adopt a Markov Decision Process (MDP) framework to integrate multi-organizational collaboration, real-time resource management and task allocation, and implement an end-to-end controller via hierarchical deep reinforcement learning(HDRL) that jointly optimizes volunteer assignment, material allocation, and replenishment. Across varied demand regimes, scales, and perishability levels, the proposed joint controller consistently outperforms common queueing heuristics: task backlogs decrease by about 30–85% and personnel costs by 16–42%, while logistics and resource-usage costs remain broadly comparable, with occasional modest logistics increases that relieve congestion. Relative to short-horizon rolling dynamic programming, it achieves lower backlog and total cost with less manual re-tuning, millisecond-level inference latency, and smooth scaling.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103505"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884132","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}
Pub Date : 2026-06-01Epub Date: 2025-12-11DOI: 10.1016/j.omega.2025.103499
Chandra Shekhar, Vijender Yadav, Ankur Saurav
Retailers managing perishable and defective items under credit-based trade environments and stringent carbon regulations face multifaceted challenges that affect profitability, inventory reliability, and environmental compliance. This study develops a comprehensive inventory model relevant to industries such as pharmaceuticals, food distribution, and cold-chain retailing, where product deterioration, inspection for defectiveness, and sustainability pressures are critical. The proposed model integrates several interdependent real-world factors: two-level trade credit, inflationary effects, preservation investment, time-dependent deterioration, carbon emissions, and operational learning. The objective is to jointly optimize replenishment cycle length, order size, green technology investment, and inspection strategies to maximize total profit while ensuring regulatory adherence and inventory availability. Novel contributions include the incorporation of a power-law learning curve in cost dynamics, a quadratic-time-dependent demand reflecting promotional or seasonal effects, and a non-linear carbon emission penalty function governed by green investment. The model is solved numerically using the Butterfly Optimization Algorithm due to its proven convergence efficiency in non-convex environments. Numerical results demonstrate that the integrated strategy improves profit by approximately 12.6% and reduces carbon emissions by 17.9% compared to traditional models that exclude sustainability and financing considerations. The sensitivity analysis further reveals practical decision-making insights under varying economic, operational, and regulatory conditions. This framework provides a realistic and adaptable decision-support tool for modern inventory systems committed to balancing economic viability with environmental responsibility.
{"title":"Two-tier trade credit inventory system for defective and deteriorating items incorporating preservation technology and learning effect with carbon emission in an inflationary environment","authors":"Chandra Shekhar, Vijender Yadav, Ankur Saurav","doi":"10.1016/j.omega.2025.103499","DOIUrl":"10.1016/j.omega.2025.103499","url":null,"abstract":"<div><div>Retailers managing perishable and defective items under credit-based trade environments and stringent carbon regulations face multifaceted challenges that affect profitability, inventory reliability, and environmental compliance. This study develops a comprehensive inventory model relevant to industries such as pharmaceuticals, food distribution, and cold-chain retailing, where product deterioration, inspection for defectiveness, and sustainability pressures are critical. The proposed model integrates several interdependent real-world factors: two-level trade credit, inflationary effects, preservation investment, time-dependent deterioration, carbon emissions, and operational learning. The objective is to jointly optimize replenishment cycle length, order size, green technology investment, and inspection strategies to maximize total profit while ensuring regulatory adherence and inventory availability. Novel contributions include the incorporation of a power-law learning curve in cost dynamics, a quadratic-time-dependent demand reflecting promotional or seasonal effects, and a non-linear carbon emission penalty function governed by green investment. The model is solved numerically using the Butterfly Optimization Algorithm due to its proven convergence efficiency in non-convex environments. Numerical results demonstrate that the integrated strategy improves profit by approximately 12.6% and reduces carbon emissions by 17.9% compared to traditional models that exclude sustainability and financing considerations. The sensitivity analysis further reveals practical decision-making insights under varying economic, operational, and regulatory conditions. This framework provides a realistic and adaptable decision-support tool for modern inventory systems committed to balancing economic viability with environmental responsibility.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103499"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788001","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}
Pub Date : 2026-06-01Epub Date: 2025-12-12DOI: 10.1016/j.omega.2025.103501
Mouad Sidki , Tchernev Nikolay , Pierre Féniès , Libo Ren , Selwa El Firdoussi
This paper addresses an integrated, real-world detailed scheduling problem of OCP Group operations, in the context of the phosphate mining industry. The studied problem incorporates a production scheduling problem in multiple washing stations, a multiproduct pipeline network scheduling problem, and a multiproduct inventory management problem, where tanks are shared for storing different products at different periods. We propose a discrete-time, product-centric MILP framework based on a rolling-horizon decomposition with two components. First, a MILP model generates scheduling solutions for each one of the sub-horizons while incorporating maintenance windows and minimizing product shortages and the total volume of injected water into pipelines. Each sub-problem is solved with a total discharge of batches still in the pipelines at the end of the scheduling horizon, ensuring that downstream inventory levels and demands are accurately updated. Additionally, a fixed portion of each sub-solution is discarded to mitigate edge effects and preserve solution quality across sub-horizons. Second, once all the sub-solutions are assembled, a post-processing MILP model refines the final solutions by minimizing unnecessary state changes across multiple units, resulting in good-quality, directly implementable solutions. The developed approach provides a holistic framework that addresses the limitations of previous attempts to solve the studied problem. To evaluate its performance, it was tested on ten challenging high-demand industrial instances over a seven-day rolling horizon. The experimental results demonstrate its effectiveness in providing high-quality solutions with high demand satisfaction rates and low water consumption in a reasonable CPU time.
{"title":"A novel rolling horizon product-centric MILP based approach for a real-world integrated production and distribution scheduling problem","authors":"Mouad Sidki , Tchernev Nikolay , Pierre Féniès , Libo Ren , Selwa El Firdoussi","doi":"10.1016/j.omega.2025.103501","DOIUrl":"10.1016/j.omega.2025.103501","url":null,"abstract":"<div><div>This paper addresses an integrated, real-world detailed scheduling problem of OCP Group operations, in the context of the phosphate mining industry. The studied problem incorporates a production scheduling problem in multiple washing stations, a multiproduct pipeline network scheduling problem, and a multiproduct inventory management problem, where tanks are shared for storing different products at different periods. We propose a discrete-time, product-centric MILP framework based on a rolling-horizon decomposition with two components. First, a MILP model generates scheduling solutions for each one of the sub-horizons while incorporating maintenance windows and minimizing product shortages and the total volume of injected water into pipelines. Each sub-problem is solved with a total discharge of batches still in the pipelines at the end of the scheduling horizon, ensuring that downstream inventory levels and demands are accurately updated. Additionally, a fixed portion of each sub-solution is discarded to mitigate edge effects and preserve solution quality across sub-horizons. Second, once all the sub-solutions are assembled, a post-processing MILP model refines the final solutions by minimizing unnecessary state changes across multiple units, resulting in good-quality, directly implementable solutions. The developed approach provides a holistic framework that addresses the limitations of previous attempts to solve the studied problem. To evaluate its performance, it was tested on ten challenging high-demand industrial instances over a seven-day rolling horizon. The experimental results demonstrate its effectiveness in providing high-quality solutions with high demand satisfaction rates and low water consumption in a reasonable CPU time.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103501"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788041","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}
Pub Date : 2026-06-01Epub Date: 2025-12-23DOI: 10.1016/j.omega.2025.103508
Yuying Zhang, Shiming Deng, Wanpeng Wang
We study how firms selling newsvendor-type products determine order quantities to maximize the probability of achieving a target profit, referred to as profitability. Unlike existing literature, we assume decision-makers have access to historical demand data and related feature data. To integrate feature information into the optimization model, we propose a weighted sample average approximation method that resolves the inherent inconsistency of traditional SAA approaches. This feature-based model is reformulated as a mixed integer programming for efficient solution. We further prove the consistency and asymptotic optimality of the ordering policy derived from our method. For high-dimensional feature settings with irrelevant features, we develop a decision-based feature selection method within the nonparametric optimization framework. Additionally, we introduce a nonparametric bootstrap method to estimate conservative profitability, mitigating overestimation risks caused by sampling errors. Numerical experiments using both synthetic and real data are conducted to demonstrate the effectiveness of our proposed methods. Notably, as the sample size increases, our feature selection method consistently identifies all relevant features, meaning the probability of correctly selecting the model approaches 1. Furthermore, in real-data experiments, our feature-based method improves profitability by more than 50% compared to the SAA method.
{"title":"Feature-based profitability evaluation for newsvendor-type products","authors":"Yuying Zhang, Shiming Deng, Wanpeng Wang","doi":"10.1016/j.omega.2025.103508","DOIUrl":"10.1016/j.omega.2025.103508","url":null,"abstract":"<div><div>We study how firms selling newsvendor-type products determine order quantities to maximize the probability of achieving a target profit, referred to as profitability. Unlike existing literature, we assume decision-makers have access to historical demand data and related feature data. To integrate feature information into the optimization model, we propose a weighted sample average approximation method that resolves the inherent inconsistency of traditional SAA approaches. This feature-based model is reformulated as a mixed integer programming for efficient solution. We further prove the consistency and asymptotic optimality of the ordering policy derived from our method. For high-dimensional feature settings with irrelevant features, we develop a decision-based feature selection method within the nonparametric optimization framework. Additionally, we introduce a nonparametric bootstrap method to estimate conservative profitability, mitigating overestimation risks caused by sampling errors. Numerical experiments using both synthetic and real data are conducted to demonstrate the effectiveness of our proposed methods. Notably, as the sample size increases, our feature selection method consistently identifies all relevant features, meaning the probability of correctly selecting the model approaches 1. Furthermore, in real-data experiments, our feature-based method improves profitability by more than 50% compared to the SAA method.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103508"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884131","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}
Pub Date : 2026-06-01Epub Date: 2025-12-05DOI: 10.1016/j.omega.2025.103489
Nuobu Liang, Qingyu Zhang
Many companies, such as Hermes and Apple or Dell and Intel, engage in ingredient branding to form strategic partnerships. A distinctive feature of these collaborations is the visible presence of the ingredient brand on the end product, creating “brand halo” effects where one brand’s reputation influences the other as a cumulative and long-term process. However, when partners contribute and benefit unequally, fairness concerns about “free-riding” arise. While ingredient branding promotes mutual gain, fairness concerns may lead to distributional conflict. Yet how this tension shapes supply chain performance remains unclear. To address this issue, we develop a cooperative supply chain framework involving a component supplier and an end-product manufacturer, incorporating Nash bargaining fairness concerns and the Nerlove–Arrow dynamic goodwill model. Our findings reveal that while fairness concerns tend to reduce market demand and brand goodwill in an ingredient branding supply chain, they also help offset the disadvantages of being a Stackelberg follower. In a profit–cost sharing supply chain, higher supplier fairness concerns degree requires greater subsidy for coordination in the supplier-dominated setting, while higher manufacturer fairness concerns degree increases the likelihood for Pareto improvement in the manufacturer-dominated setting. In the extended model with advertising and quality improvement, we find that suppliers should invest in quality improvement initially, while advertising is preferred later only if its efficiency is sufficiently high. Consumers benefit from lower prices when the Stackelberg leader exhibits a low degree of fairness concern. Finally, we use numerical analysis to examine the robustness of our model by considering nonlinear fairness concerns and budget constraints. Our results show that, despite minor fluctuations in equilibrium decisions under the nonlinear fairness-minded supply chain, the overall trend is consistent with that in the linear case. Furthermore, imposing budget constraints on the Stackelberg leader leads to greater fluctuation in overall supply chain utility than imposing them on the followers.
{"title":"A differential game of ingredient branding supply chain with Nash bargaining fairness concern","authors":"Nuobu Liang, Qingyu Zhang","doi":"10.1016/j.omega.2025.103489","DOIUrl":"10.1016/j.omega.2025.103489","url":null,"abstract":"<div><div>Many companies, such as Hermes and Apple or Dell and Intel, engage in ingredient branding to form strategic partnerships. A distinctive feature of these collaborations is the visible presence of the ingredient brand on the end product, creating “brand halo” effects where one brand’s reputation influences the other as a cumulative and long-term process. However, when partners contribute and benefit unequally, fairness concerns about “free-riding” arise. While ingredient branding promotes mutual gain, fairness concerns may lead to distributional conflict. Yet how this tension shapes supply chain performance remains unclear. To address this issue, we develop a cooperative supply chain framework involving a component supplier and an end-product manufacturer, incorporating Nash bargaining fairness concerns and the Nerlove–Arrow dynamic goodwill model. Our findings reveal that while fairness concerns tend to reduce market demand and brand goodwill in an ingredient branding supply chain, they also help offset the disadvantages of being a Stackelberg follower. In a profit–cost sharing supply chain, higher supplier fairness concerns degree requires greater subsidy for coordination in the supplier-dominated setting, while higher manufacturer fairness concerns degree increases the likelihood for Pareto improvement in the manufacturer-dominated setting. In the extended model with advertising and quality improvement, we find that suppliers should invest in quality improvement initially, while advertising is preferred later only if its efficiency is sufficiently high. Consumers benefit from lower prices when the Stackelberg leader exhibits a low degree of fairness concern. Finally, we use numerical analysis to examine the robustness of our model by considering nonlinear fairness concerns and budget constraints. Our results show that, despite minor fluctuations in equilibrium decisions under the nonlinear fairness-minded supply chain, the overall trend is consistent with that in the linear case. Furthermore, imposing budget constraints on the Stackelberg leader leads to greater fluctuation in overall supply chain utility than imposing them on the followers.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103489"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683751","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}
The ongoing electrification of the transport sector, driven by the numerous advantages of electric vehicles (EVs), introduces new challenges related to charging logistics, particularly due to long charging durations and uncertain conditions, posing significant negative impacts on grid stability and user satisfaction. While existing literature on EV charging scheduling often assumes deterministic charging durations, real-world conditions introduce randomness due to uncontrollable factors such as battery state-of-charge (SoC), fluctuating grid demand, and ambient temperature. In this paper, we address the Electric Vehicle Charging Scheduling Problem (EVCSP) under uncertain charging durations. First, we introduce a novel, flexible multi-objective scheduling model operating on a continuous time horizon, considering stochastic charging durations and incorporating controlled preemptions during charging, where the non-preemptive mode is a particular case. Then, we prove that finding a feasible assignment of EVs to chargers is strongly NP-hard under this uncertainty, even assuming identical chargers. Our model accounts for realistic constraints, including heterogeneous charger power levels and vehicle-charger compatibility, aiming to minimize the conditional expected values of grid overload and total tardiness, while also minimizing the undelivered energy to users. Given the problem’s computational complexity, we adapt four evolutionary algorithms (EAs), namely, extensions of the Non-Dominated Sorting Genetic Algorithm (NSGA), namely NSGA-II and NSGA-III, alongside other state-of-the-art multi-objective metaheuristics, including the Multi-Objective Cuckoo Search (MOCS) algorithm, and the Multi-Objective Grey Wolf Optimizer (MOGWO) by defining problem-specific operators to explore the search space and efficiently approximate the optimal Pareto front. Assuming lognormally distributed charging durations, we conducted a comparative experimental analysis on real-world data to evaluate the four methods and revealed that MOCS algorithm outperforms the other competitors.
{"title":"Multi-objective electric vehicle charging scheduling under stochastic duration uncertainty","authors":"Aimen Khiar , Mohamed el Amine Brahmia , Ammar Oulamara , Lhassane Idoumghar","doi":"10.1016/j.omega.2025.103506","DOIUrl":"10.1016/j.omega.2025.103506","url":null,"abstract":"<div><div>The ongoing electrification of the transport sector, driven by the numerous advantages of electric vehicles (EVs), introduces new challenges related to charging logistics, particularly due to long charging durations and uncertain conditions, posing significant negative impacts on grid stability and user satisfaction. While existing literature on EV charging scheduling often assumes deterministic charging durations, real-world conditions introduce randomness due to uncontrollable factors such as battery state-of-charge (SoC), fluctuating grid demand, and ambient temperature. In this paper, we address the <em>Electric Vehicle Charging Scheduling Problem</em> (EVCSP) under uncertain charging durations. First, we introduce a novel, flexible multi-objective scheduling model operating on a continuous time horizon, considering stochastic charging durations and incorporating controlled preemptions during charging, where the non-preemptive mode is a particular case. Then, we prove that finding a feasible assignment of EVs to chargers is strongly NP-hard under this uncertainty, even assuming identical chargers. Our model accounts for realistic constraints, including heterogeneous charger power levels and vehicle-charger compatibility, aiming to minimize the conditional expected values of grid overload and total tardiness, while also minimizing the undelivered energy to users. Given the problem’s computational complexity, we adapt four evolutionary algorithms (EAs), namely, extensions of the Non-Dominated Sorting Genetic Algorithm (NSGA), namely NSGA-II and NSGA-III, alongside other state-of-the-art multi-objective metaheuristics, including the Multi-Objective Cuckoo Search (MOCS) algorithm, and the Multi-Objective Grey Wolf Optimizer (MOGWO) by defining problem-specific operators to explore the search space and efficiently approximate the optimal Pareto front. Assuming lognormally distributed charging durations, we conducted a comparative experimental analysis on real-world data to evaluate the four methods and revealed that MOCS algorithm outperforms the other competitors.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103506"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925462","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}
Pub Date : 2026-06-01Epub Date: 2025-12-08DOI: 10.1016/j.omega.2025.103485
Changchun Liu , Xi Xiang
This paper examines the impact of introducing a new fare product on consumer purchasing behavior within the established framework of airline price discrimination strategies. Specifically, it analyzes changes in the sales distribution of existing fare products following the implementation of the new option. We first present a model that captures the essential characteristics of airline price discrimination and elucidates how the introduction of a new fare product influences firm revenue, consumer surplus, and customer decision-making. Through close collaboration with industry stakeholders, we identify two critical factors that significantly affect practical pricing strategies and consumer choice behavior: the operational use of load factor metrics and the heterogeneity in customer valuations across different fare products. Building on these industry insights, we investigate how the introduction of a new fare product interacts with load factor constraints and the misestimation of valuation differences between fare products, ultimately shaping consumer responses. In the empirical component of the study, we utilize transaction-level data from a major airline to validate the model’s predictions and derive additional managerial insights. Our analysis reveals a strong association between load factor levels and customer purchasing patterns. Moreover, the accurate estimation of the valuation gap across fare products proves to be a crucial determinant of consumer behavior. The joint analysis of load factor and valuation heterogeneity highlights their intertwined role in shaping observed purchase dynamics, demonstrating that load factor considerations and valuation misalignments jointly influence market outcomes.
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Pub Date : 2026-06-01Epub Date: 2025-12-17DOI: 10.1016/j.omega.2025.103502
Anna Timonina-Farkas
Multi-stage stochastic optimization is a well-known quantitative tool applied in a wide variety of decision-making problems. In this article, we focus on generalized flood risk management problems with Fréchet distributions used to describe the uncertainty. Theoretical solutions of such problems can be found explicitly only in exceptional cases due to their variational form and interdependency of uncertainty in time, e.g., due to cascading impacts of extreme floods. Nevertheless, numerical methods based on Monte Carlo sampling are inaccurate, as the Law of Large Numbers must hold for sufficient approximation quality. To overcome this shortcoming, we introduce an approximation scheme that computes and groups together optimal quantizers of Fréchet distributions. The groups are distinguished by a particular risk threshold and differentiate between higher- and lower-impact floods. We consider optimality of quantization methods in the sense of the minimal Kantorovich–Wasserstein distance. Depending on the group, to which a quantizer belongs, and on the form of the optimization problem, we propose two dynamic programming schemes: with accelerated dynamics and with non-accelerated dynamics. For the accelerated method, the groups of quantizers are used to cut scenario trees and guarantee optimality gaps close to zero. For the non-accelerated method, the probabilities of quantizers are used to weight value functions and bound the approximation error with convergence guarantees. Global solution is guaranteed under convexity and monotonicity conditions on the value functions. Considering cases with and without circular economy indicators able to reduce emissions, we apply the methods we developed to the governmental budget allocation problem under flood risk in Austria.
{"title":"Group-and-cut approach for dynamic programming with Fréchet-distributed quantizers","authors":"Anna Timonina-Farkas","doi":"10.1016/j.omega.2025.103502","DOIUrl":"10.1016/j.omega.2025.103502","url":null,"abstract":"<div><div>Multi-stage stochastic optimization is a well-known quantitative tool applied in a wide variety of decision-making problems. In this article, we focus on generalized flood risk management problems with Fréchet distributions used to describe the uncertainty. Theoretical solutions of such problems can be found explicitly only in exceptional cases due to their variational form and interdependency of uncertainty in time, e.g., due to cascading impacts of extreme floods. Nevertheless, numerical methods based on Monte Carlo sampling are inaccurate, as the Law of Large Numbers must hold for sufficient approximation quality. To overcome this shortcoming, we introduce an approximation scheme that computes and groups together <em>optimal</em> quantizers of Fréchet distributions. The groups are distinguished by a particular risk threshold and differentiate between higher- and lower-impact floods. We consider optimality of quantization methods in the sense of the minimal Kantorovich–Wasserstein distance. Depending on the group, to which a quantizer belongs, and on the form of the optimization problem, we propose two dynamic programming schemes: with accelerated dynamics and with non-accelerated dynamics. For the accelerated method, the groups of quantizers are used to cut scenario trees and guarantee optimality gaps close to zero. For the non-accelerated method, the probabilities of quantizers are used to weight value functions and bound the approximation error with convergence guarantees. Global solution is guaranteed under convexity and monotonicity conditions on the value functions. Considering cases with and without circular economy indicators able to reduce <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions, we apply the methods we developed to the governmental budget allocation problem under flood risk in Austria.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"141 ","pages":"Article 103502"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840661","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}
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