The shipping industry has increasingly faced transportation disruptions due to geopolitical tensions and regional shocks. In response, ocean shipping (OS) carriers have adopted resilience enhancement strategies to maintain service availability and operational continuity. While such strategies ensure carriers’ ability to sustain service provision during disruptions, they may also alter competitive dynamics within shipping alliances and weaken incentives for cooperation. This study develops an incentive-based model with two asymmetric OS carriers to examine the interplay among resilience enhancement, disruption risk, logistics service competition, and shipping alliance. We find that an inferior OS carrier’s resilience enhancement strategy may benefit (or surprisingly harm) itself when the shipping alliance’s dominant carrier cannot (can) effectively enhance the alliance service level to expand the market. Even though the inferior OS carrier’s resilience enhancement strategy may increase its own profitability, we reveal that the dominant carrier’s profitability can be impaired, hampering its incentive for alliance-based cooperation. Our work elucidates the role of resilient shipping service under an uncertain and co-opetitive environment.
{"title":"Resilient service of shipping alliance under disruption risk","authors":"Baozhuang Niu , Jianhua Zhang , Fengfeng Xie , Zhipeng Dai , Xiaomeng Guo","doi":"10.1016/j.tre.2026.104713","DOIUrl":"10.1016/j.tre.2026.104713","url":null,"abstract":"<div><div>The shipping industry has increasingly faced transportation disruptions due to geopolitical tensions and regional shocks. In response, ocean shipping (OS) carriers have adopted resilience enhancement strategies to maintain service availability and operational continuity. While such strategies ensure carriers’ ability to sustain service provision during disruptions, they may also alter competitive dynamics within shipping alliances and weaken incentives for cooperation. This study develops an incentive-based model with two asymmetric OS carriers to examine the interplay among resilience enhancement, disruption risk, logistics service competition, and shipping alliance. We find that an inferior OS carrier’s resilience enhancement strategy may benefit (or surprisingly harm) itself when the shipping alliance’s dominant carrier cannot (can) effectively enhance the alliance service level to expand the market. Even though the inferior OS carrier’s resilience enhancement strategy may increase its own profitability, we reveal that the dominant carrier’s profitability can be impaired, hampering its incentive for alliance-based cooperation. Our work elucidates the role of resilient shipping service under an uncertain and co-opetitive environment.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104713"},"PeriodicalIF":8.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.tre.2026.104721
Mohammad Ali Hassanabadi, S.Ali Torabi
Coopetitive and innovation-driven circular business models (CBMs) are increasingly being proposed to address growing economic and environmental challenges. However, many firms remain hesitant to embrace coopetition, and the adoption of coopetitive models across industries remains limited. This research, as the first attempt to provide a coopetitive and innovation-driven mathematical game-theoretic model in the context of CBMs, explores the potential for environmentally and economically viable innovations within CBMs and analyzes the strategic interactions between coopetitive firms under two distinct power structures. We investigate the conditions under which firms are willing to adopt a coopetition strategy and seek to answer the long-struggling question in the literature regarding the intensity of coopetition between rival firms. We also assess the environmental welfare outcomes of the model and introduce two distinct governmental interventions, each designed to achieve specific objectives. We further relax the power imbalance assumption and conduct a coopetition-based bargaining game between rivals. Our main findings demonstrate that (i) the right choice of power structure not only boosts innovation and environmental welfare within CBMs, it results in higher intensity of coopetition and willingness to collaborate with rivals; (ii) while the proposed supportive mechanisms can encourage firms to cooperate and enhance environmental welfare, they may accelerate the loss of firms’ competitive advantage and CBMs’ financial viability; and (iii) the right choice of supportive mechanism and assigned subsidies can reduce government’s financial burden and strengthen firms’ competitive advantage and environmental welfare. Finally, several managerial and policy implications are derived from the analytical and numerical analyses.
{"title":"Balancing coopetition in innovation-driven circular business models: the interplay of environmental welfare and competitive advantages","authors":"Mohammad Ali Hassanabadi, S.Ali Torabi","doi":"10.1016/j.tre.2026.104721","DOIUrl":"10.1016/j.tre.2026.104721","url":null,"abstract":"<div><div>Coopetitive and innovation-driven circular business models (CBMs) are increasingly being proposed to address growing economic and environmental challenges. However, many firms remain hesitant to embrace coopetition, and the adoption of coopetitive models across industries remains limited. This research, as the first attempt to provide a coopetitive and innovation-driven mathematical game-theoretic model in the context of CBMs, explores the potential for environmentally and economically viable innovations within CBMs and analyzes the strategic interactions between coopetitive firms under two distinct power structures. We investigate the conditions under which firms are willing to adopt a coopetition strategy and seek to answer the long-struggling question in the literature regarding the intensity of coopetition between rival firms. We also assess the environmental welfare outcomes of the model and introduce two distinct governmental interventions, each designed to achieve specific objectives. We further relax the power imbalance assumption and conduct a coopetition-based bargaining game between rivals. Our main findings demonstrate that (i) the right choice of power structure not only boosts innovation and environmental welfare within CBMs, it results in higher intensity of coopetition and willingness to collaborate with rivals; (ii) while the proposed supportive mechanisms can encourage firms to cooperate and enhance environmental welfare, they may accelerate the loss of firms’ competitive advantage and CBMs’ financial viability; and (iii) the right choice of supportive mechanism and assigned subsidies can reduce government’s financial burden and strengthen firms’ competitive advantage and environmental welfare. Finally, several managerial and policy implications are derived from the analytical and numerical analyses.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104721"},"PeriodicalIF":8.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To fully leverage the advantages of sea-rail intermodal transport, the automation upgrade of the railway center station (RCS) is essential for enabling seamless connectivity between the RCS and the terminal via automated guided vehicles (AGVs). This transformation introduces complex scheduling challenges for sea-rail intermodal automated container terminals (SRIACTs), including multi-directional container flows, coordination among diverse equipment, and AGV charging requirements with battery management. To address these challenges, this paper investigates the multi-equipment collaborative scheduling problem in SRIACTs with consideration of AGV charging. A mixed-integer programming model is formulated with sequencing, timing, and energy constraints, aiming to jointly minimize makespan and total charging time. To improve computational efficiency in large-scale cases, an improved genetic algorithm based on a decomposition-iteration framework is developed according to problem-specific features. Furthermore, to address operational uncertainties, a digital twin-based hybrid rescheduling framework is extended to enable real-time monitoring, disturbance detection, and rapid response to AGV status and battery levels, thereby enhancing system resilience and scheduling flexibility. Extensive numerical experiments are conducted to validate the effectiveness of the proposed algorithm and rescheduling framework. On this basis, comparative analyses are performed on bi-objective formulations and the flexible charging strategy. Additionally, sensitivity analyses examine the impacts of key factors, including objective weights, charging thresholds, rescheduling thresholds, and the number and layout of charging facilities. The findings provide valuable insights for terminal operators in formulating integrated scheduling strategies, optimizing AGV charging plans, and scientifically deploying charging infrastructure during the RCS automation process, thereby promoting sustainable and intelligent terminal operations.
{"title":"Digital twin-based dynamic co-scheduling with AGV energy management in sea-rail intermodal automated container terminals","authors":"Jiaqi Li , Daofang Chang , Furong Wen , Ilkyeong Moon","doi":"10.1016/j.tre.2026.104707","DOIUrl":"10.1016/j.tre.2026.104707","url":null,"abstract":"<div><div>To fully leverage the advantages of sea-rail intermodal transport, the automation upgrade of the railway center station (RCS) is essential for enabling seamless connectivity between the RCS and the terminal via automated guided vehicles (AGVs). This transformation introduces complex scheduling challenges for sea-rail intermodal automated container terminals (SRIACTs), including multi-directional container flows, coordination among diverse equipment, and AGV charging requirements with battery management. To address these challenges, this paper investigates the multi-equipment collaborative scheduling problem in SRIACTs with consideration of AGV charging. A mixed-integer programming model is formulated with sequencing, timing, and energy constraints, aiming to jointly minimize makespan and total charging time. To improve computational efficiency in large-scale cases, an improved genetic algorithm based on a decomposition-iteration framework is developed according to problem-specific features. Furthermore, to address operational uncertainties, a digital twin-based hybrid rescheduling framework is extended to enable real-time monitoring, disturbance detection, and rapid response to AGV status and battery levels, thereby enhancing system resilience and scheduling flexibility. Extensive numerical experiments are conducted to validate the effectiveness of the proposed algorithm and rescheduling framework. On this basis, comparative analyses are performed on bi-objective formulations and the flexible charging strategy. Additionally, sensitivity analyses examine the impacts of key factors, including objective weights, charging thresholds, rescheduling thresholds, and the number and layout of charging facilities. The findings provide valuable insights for terminal operators in formulating integrated scheduling strategies, optimizing AGV charging plans, and scientifically deploying charging infrastructure during the RCS automation process, thereby promoting sustainable and intelligent terminal operations.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104707"},"PeriodicalIF":8.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.tre.2026.104716
Meiyu Liu , Shanshan Gao , Naiqi Liu
In this paper, we study the capacitated hub interdiction problem on a multiple allocation hub-and-spoke network and formulate it as a bi-level optimization model. The rational attacker in the upper-level model interdicts a subset of hubs and maximizes the damage to the defender in the lower-level model. Given that hubs operate with limited capacity, an interdiction would lead to unfulfilled demand, thereby incurring additional penalty costs. In reality, however, demand in urban logistics transportation tends to exhibit significant variation due to its inherent non-stationarity and spatial correlation. Therefore, we model demand uncertainty to make robust hub location and routing decisions. Methodologically, a sub-Gaussian-based ambiguity set is constructed using statistical methods, which involves the family of all probability distributions consistent with known mean, variance and support information about demand. We develop a distributionally robust optimization model for our capacitated hub interdiction problem under the constructed ambiguity set, and then reformulate it as a mixed integer linear programming model, which facilitates us to design an accelerated Benders decomposition algorithm. In particular, we derive robust solutions with both a priori and a posteriori probability guarantees. Case study on the well-known CAB dataset demonstrates the advantages of our optimization method in balancing robustness and conservatism. Furthermore, computational results on the TR dataset illustrate that our proposed algorithm outperforms the CPLEX solver.
{"title":"A new robust capacitated hub interdiction problem under ambiguous demand and its benders decomposition","authors":"Meiyu Liu , Shanshan Gao , Naiqi Liu","doi":"10.1016/j.tre.2026.104716","DOIUrl":"10.1016/j.tre.2026.104716","url":null,"abstract":"<div><div>In this paper, we study the capacitated hub interdiction problem on a multiple allocation hub-and-spoke network and formulate it as a bi-level optimization model. The rational attacker in the upper-level model interdicts a subset of hubs and maximizes the damage to the defender in the lower-level model. Given that hubs operate with limited capacity, an interdiction would lead to unfulfilled demand, thereby incurring additional penalty costs. In reality, however, demand in urban logistics transportation tends to exhibit significant variation due to its inherent non-stationarity and spatial correlation. Therefore, we model demand uncertainty to make robust hub location and routing decisions. Methodologically, a sub-Gaussian-based ambiguity set is constructed using statistical methods, which involves the family of all probability distributions consistent with known mean, variance and support information about demand. We develop a distributionally robust optimization model for our capacitated hub interdiction problem under the constructed ambiguity set, and then reformulate it as a mixed integer linear programming model, which facilitates us to design an accelerated Benders decomposition algorithm. In particular, we derive robust solutions with both a priori and a posteriori probability guarantees. Case study on the well-known CAB dataset demonstrates the advantages of our optimization method in balancing robustness and conservatism. Furthermore, computational results on the TR dataset illustrate that our proposed algorithm outperforms the CPLEX solver.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104716"},"PeriodicalIF":8.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.tre.2026.104709
Xiaohan Liu , Arsalan Najafi , Sheng Jin , Hua Wang , Xiaolei Ma , Kun Gao
Public transport electrification contributes to the net-zero goal in the transport sector. However, high-power bus charging during peak hours places additional strain on the grid, while under-utilization of charging infrastructure limits its potential economic and social benefits. This study focuses on these challenges through integrated and shared optimization of battery electric buses (BEB) and shared micromobility systems (SMS) incorporating solar photovoltaic. We present a bi-level mixed-integer linear programming model (B-MILM) to jointly optimize BEB charging infrastructure, BEB charging schedules, solar PV installed capacity, and SMS charging schedule. The B-MILM is solved using a value-function-based exact approach. We derive a group of inequalities based on the problem characteristics to reduce solution time. A large-scale case study in Gothenburg, Sweden, demonstrates that solar photovoltaic and shared charging services yield annual cost savings 110% - 120% above investment costs for public transit agencies, even when the service fee revenue is excluded. Charging dispatching costs for e-scooter operators are reduced by up to 54%, and daily BEB charging grid loads decrease by 3% to 34% across seasons. The greenhouse emissions from electricity consumption of BEBs and e-scooters are reduced by 3%. The results offer new insights for sustainable charging and energy infrastructure planning and management for electric public transit.
{"title":"Integrated and shared charging optimization of electric buses and shared micromobility incorporating solar photovoltaic","authors":"Xiaohan Liu , Arsalan Najafi , Sheng Jin , Hua Wang , Xiaolei Ma , Kun Gao","doi":"10.1016/j.tre.2026.104709","DOIUrl":"10.1016/j.tre.2026.104709","url":null,"abstract":"<div><div>Public transport electrification contributes to the net-zero goal in the transport sector. However, high-power bus charging during peak hours places additional strain on the grid, while under-utilization of charging infrastructure limits its potential economic and social benefits. This study focuses on these challenges through integrated and shared optimization of battery electric buses (BEB) and shared micromobility systems (SMS) incorporating solar photovoltaic. We present a bi-level mixed-integer linear programming model (B-MILM) to jointly optimize BEB charging infrastructure, BEB charging schedules, solar PV installed capacity, and SMS charging schedule. The B-MILM is solved using a value-function-based exact approach. We derive a group of inequalities based on the problem characteristics to reduce solution time. A large-scale case study in Gothenburg, Sweden, demonstrates that solar photovoltaic and shared charging services yield annual cost savings 110% - 120% above investment costs for public transit agencies, even when the service fee revenue is excluded. Charging dispatching costs for e-scooter operators are reduced by up to 54%, and daily BEB charging grid loads decrease by 3% to 34% across seasons. The greenhouse emissions from electricity consumption of BEBs and e-scooters are reduced by 3%. The results offer new insights for sustainable charging and energy infrastructure planning and management for electric public transit.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104709"},"PeriodicalIF":8.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.tre.2026.104705
Kaifu Li, Deqing Ma, Jinsong Hu, Xue Wang
Product-harm crises happen unexpectedly, triggering product recalls and altering consumer psychology, posing significant challenges to brands. This paper examines a monopoly brand selling a single product, identifying three crisis scenarios: no crisis, severe crisis, and mild crisis. Incorporating the crisis’s long-term effect, consumers’ price mapping psychology (PMP), and their vigilance to the crisis, we explore the dynamic pricing strategy for a far-sighted brand manager. The results suggest that in the absence of a crisis, the brand manager, weighing against consumers’ PMP and the law of demand (LOD), sets price based on the product’s basic quality. Regardless of whether the product survives the crisis, a risk premium will always be charged before a crisis to cushion recall costs. After the crisis, price drops, but demand may soften as consumers grow intolerant of implicated products and require products with superior basic quality. Thus, the crisis and its long-term effects inevitably harm both the supply and demand sides. Although the negative impact cannot be eliminated by dynamic pricing strategies, the brand can benefit from greater market share and minimize profit loss rates by leveraging consumers’ PMP and laxity. Interestingly, despite being exploited, consumers benefit from increased utility and consumer surplus. Notably, the hazard myopia of a brand manager is only more beneficial when the crisis arrives later. Brands confronted with crises must reduce production costs or be priced out of the market. By capitalizing on recalled products’ salvage value, the brand will lower the risk premium due to eased recall cost pressures.
{"title":"Dynamic pricing strategies based on Consumers’ psychology during product-harm crises","authors":"Kaifu Li, Deqing Ma, Jinsong Hu, Xue Wang","doi":"10.1016/j.tre.2026.104705","DOIUrl":"10.1016/j.tre.2026.104705","url":null,"abstract":"<div><div>Product-harm crises happen unexpectedly, triggering product recalls and altering consumer psychology, posing significant challenges to brands. This paper examines a monopoly brand selling a single product, identifying three crisis scenarios: no crisis, severe crisis, and mild crisis. Incorporating the crisis’s long-term effect, consumers’ price mapping psychology (PMP), and their vigilance to the crisis, we explore the dynamic pricing strategy for a far-sighted brand manager. The results suggest that in the absence of a crisis, the brand manager, weighing against consumers’ PMP and the law of demand (LOD), sets price based on the product’s basic quality. Regardless of whether the product survives the crisis, a risk premium will always be charged before a crisis to cushion recall costs. After the crisis, price drops, but demand may soften as consumers grow intolerant of implicated products and require products with superior basic quality. Thus, the crisis and its long-term effects inevitably harm both the supply and demand sides. Although the negative impact cannot be eliminated by dynamic pricing strategies, the brand can benefit from greater market share and minimize profit loss rates by leveraging consumers’ PMP and laxity. Interestingly, despite being exploited, consumers benefit from increased utility and consumer surplus. Notably, the hazard myopia of a brand manager is only more beneficial when the crisis arrives later. Brands confronted with crises must reduce production costs or be priced out of the market. By capitalizing on recalled products’ salvage value, the brand will lower the risk premium due to eased recall cost pressures.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104705"},"PeriodicalIF":8.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.tre.2026.104704
Boyi Su , Fangsheng Wang , Shuai Su , Andrea D’Ariano , Zhikai Wang , Tao Tang
Metro trains inevitably encounter faults during operation, leading to disturbances or disruptions. Considering the uncertainties in both the scenario type (such as delay, out-of-service, and rescue) and the duration of these disturbances or disruptions, this paper investigates the real-time train rescheduling problem in the context of Industry 5.0. A risk-averse two-stage stochastic programming model is formulated to generate rescheduling solutions for each possible uncertainty realization and ensure their seamless transition. In this model, the first stage makes rescheduling decisions that are independent of uncertainty realizations, such as the number of dispatched backup trains and whether to short-turn trains during fault handling. The second stage adopts all dispatching measures applicable to metro lines and makes additional rescheduling decisions. To integrate human factors into decision-making, the general conservative attitude of dispatchers towards risk management is captured using a mean-conditional value-at-risk criterion. Under the traditional integer L-shaped framework, the model is decomposed into a first-stage master problem and several second-stage subproblems. Aligning with the technological advancements of Industry 5.0, supervised machine learning is used to predict the objective values of the subproblems instead of solving them explicitly, thereby enabling the rapid addition of approximate optimality cuts and improving computational efficiency. Numerical experiments are conducted on the Beijing Yizhuang Metro Line. The computational results show that the proposed solution approach reduces the average computation time by 99.02% compared to GUROBI, and the developed stochastic model lowers the average objective value by over 22% compared to the practical strategy, contributing to the development of intelligent and resilient metro systems.
{"title":"Real-time metro train rescheduling under uncertainties: A hybrid machine learning and integer L-shaped approach","authors":"Boyi Su , Fangsheng Wang , Shuai Su , Andrea D’Ariano , Zhikai Wang , Tao Tang","doi":"10.1016/j.tre.2026.104704","DOIUrl":"10.1016/j.tre.2026.104704","url":null,"abstract":"<div><div>Metro trains inevitably encounter faults during operation, leading to disturbances or disruptions. Considering the uncertainties in both the scenario type (such as delay, out-of-service, and rescue) and the duration of these disturbances or disruptions, this paper investigates the real-time train rescheduling problem in the context of Industry 5.0. A risk-averse two-stage stochastic programming model is formulated to generate rescheduling solutions for each possible uncertainty realization and ensure their seamless transition. In this model, the first stage makes rescheduling decisions that are independent of uncertainty realizations, such as the number of dispatched backup trains and whether to short-turn trains during fault handling. The second stage adopts all dispatching measures applicable to metro lines and makes additional rescheduling decisions. To integrate human factors into decision-making, the general conservative attitude of dispatchers towards risk management is captured using a mean-conditional value-at-risk criterion. Under the traditional integer L-shaped framework, the model is decomposed into a first-stage master problem and several second-stage subproblems. Aligning with the technological advancements of Industry 5.0, supervised machine learning is used to predict the objective values of the subproblems instead of solving them explicitly, thereby enabling the rapid addition of approximate optimality cuts and improving computational efficiency. Numerical experiments are conducted on the Beijing Yizhuang Metro Line. The computational results show that the proposed solution approach reduces the average computation time by 99.02% compared to GUROBI, and the developed stochastic model lowers the average objective value by over 22% compared to the practical strategy, contributing to the development of intelligent and resilient metro systems.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104704"},"PeriodicalIF":8.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.tre.2026.104711
Lunhai Liang , Fei Ye , T.C. Edwin Cheng
This paper investigates the interplay between a platform’s information sharing decisions and its business mode selections and demonstrates how suppliers’ freshness-keeping efforts affect this interplay in a fresh produce supply chain with a supplier and a platform. We develop a theoretical model with stochastic and freshness-dependent demand, which the supplier can maintain product freshness through freshness-keeping efforts. The platform may share private demand information to support the supplier’s freshness-keeping decision and operates three business modes, namely the reselling mode (R), agency mode (A), and hybrid mode (H). Our findings show that under Mode A, the platform always voluntarily shares information, achieving a win–win outcome for both the platform and the supplier. Under Modes R and H, although information sharing drives up the wholesale price, the platform still prefers to share information when the supplier’s freshness-keeping efficiency is sufficiently high. Specifically, under Mode H, a dual-threshold information-sharing policy emerges: even when freshness-keeping efficiency is moderate, the platform shares information only when the commission rate is moderate. Furthermore, we find that information sharing motivates the supplier to enhance freshness-keeping efforts, which in turn improves product freshness. This mechanism termed the freshness-keeping improvement effect of information sharing functions as a critical incentive for the platform to voluntarily share demand information. Additionally, adopting Mode H may increase the wholesale price and exacerbate the double-marginalization problem when the commission rate is sufficiently low. Finally, we find that the platform and the supplier can reach consensus on the HS strategy (i.e., Mode H with information sharing) and subsequently achieve a win–win outcome only when the commission rate is moderate. However, information sharing may hinder consensus on adopting Mode H and narrow the win–win region..
{"title":"Inducing information provision on hybrid fresh produce e-commerce platforms via supplier freshness-keeping effort","authors":"Lunhai Liang , Fei Ye , T.C. Edwin Cheng","doi":"10.1016/j.tre.2026.104711","DOIUrl":"10.1016/j.tre.2026.104711","url":null,"abstract":"<div><div>This paper investigates the interplay between a platform’s information sharing decisions and its business mode selections and demonstrates how suppliers’ freshness-keeping efforts affect this interplay in a fresh produce supply chain with a supplier and a platform. We develop a theoretical model with stochastic and freshness-dependent demand, which the supplier can maintain product freshness through freshness-keeping efforts. The platform may share private demand information to support the supplier’s freshness-keeping decision and operates three business modes, namely the reselling mode (R), agency mode (A), and hybrid mode (H). Our findings show that under Mode A, the platform always voluntarily shares information, achieving a win–win outcome for both the platform and the supplier. Under Modes R and H, although information sharing drives up the wholesale price, the platform still prefers to share information when the supplier’s freshness-keeping efficiency is sufficiently high. Specifically, under Mode H, a dual-threshold information-sharing policy emerges: even when freshness-keeping efficiency is moderate, the platform shares information only when the commission rate is moderate. Furthermore, we find that information sharing motivates the supplier to enhance freshness-keeping efforts, which in turn improves product freshness. This mechanism termed the freshness-keeping improvement effect of information sharing functions as a critical incentive for the platform to voluntarily share demand information.<!--> <!-->Additionally, adopting Mode H may increase the wholesale price and exacerbate the double-marginalization problem when the commission rate is sufficiently low. Finally, we find that the platform and the supplier can reach consensus on the HS strategy (i.e., Mode H with information sharing) and subsequently achieve a win–win outcome only when the commission rate is moderate. However, information sharing may hinder consensus on adopting Mode H and narrow the win–win region..</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104711"},"PeriodicalIF":8.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.tre.2026.104710
Majid Karimi , Nima Zaerpour , René de Koster
In warehouses, products are often not stored in their optimal positions, elongating retrieval and order picking time. A main reason is that storage assignment is based on historical demand frequency, whereas current demand patterns might just differ. However, as many warehouses are now automated or robotized, opportunities exist to dynamically and opportunistically reposition product loads based on real known demand and still reduce the makespan (the total time needed for retrieval, storage, and optional repositioning). We investigate the optimal retrieval of a known block of requests by explicitly additionally allowing in-between repositioning options. Surprisingly, in spite of the extra work and time involved, we show opportunistic repositioning may indeed be beneficial for reducing the makespan. We study the problem for two automated unit-load storage warehouses: automated storage and retrieval (AS/R) crane-based systems and robotic mobile fulfillment (RMF) systems, which have different travel metrics for the retrieval robots. The data-driven storage and repositioning (DDSR) problem, formulated as an integer linear program, leverages actual customer order data. The problem appears to be intractable for realistic systems due to the combinatorial nature of the possible repositions. We then reformulate the model, making it more tractable for moderate-sized problems. This model appears to beat real-life storage assignment heuristics like closest-open location assignment or demand-frequency class-based storage (even when these have full foresight of demand changes). The benefits appear to be around a 14%-30% shorter makespan, depending on the number of loads to be retrieved. For larger rack space utilization, the benefits decrease (since there are fewer options for repositioning). The method is sufficiently fast to be used in real warehouse systems, e.g., by using a rolling horizon policy where repositions are calculated for the next block of requests while the current requests are executed. Our method offers managers an additional powerful tool to reduce system response time and thereby increase throughput capacity by smarter scheduling of their automated equipment and more efficient use of available storage space.
{"title":"“Keeping up with changing customer demand”: An adaptive data-driven approach for storage and repositioning decisions in automated g warehouses","authors":"Majid Karimi , Nima Zaerpour , René de Koster","doi":"10.1016/j.tre.2026.104710","DOIUrl":"10.1016/j.tre.2026.104710","url":null,"abstract":"<div><div>In warehouses, products are often not stored in their optimal positions, elongating retrieval and order picking time. A main reason is that storage assignment is based on historical demand frequency, whereas current demand patterns might just differ. However, as many warehouses are now automated or robotized, opportunities exist to dynamically and opportunistically reposition product loads based on real known demand and still reduce the makespan (the total time needed for retrieval, storage, and optional repositioning). We investigate the optimal retrieval of a known block of requests by explicitly additionally allowing in-between repositioning options. Surprisingly, in spite of the extra work and time involved, we show opportunistic repositioning may indeed be beneficial for reducing the makespan. We study the problem for two automated unit-load storage warehouses: automated storage and retrieval (AS/R) crane-based systems and robotic mobile fulfillment (RMF) systems, which have different travel metrics for the retrieval robots. The data-driven storage and repositioning (DDSR) problem, formulated as an integer linear program, leverages actual customer order data. The problem appears to be intractable for realistic systems due to the combinatorial nature of the possible repositions. We then reformulate the model, making it more tractable for moderate-sized problems. This model appears to beat real-life storage assignment heuristics like closest-open location assignment or demand-frequency class-based storage (even when these have full foresight of demand changes). The benefits appear to be around a 14%-30% shorter makespan, depending on the number of loads to be retrieved. For larger rack space utilization, the benefits decrease (since there are fewer options for repositioning). The method is sufficiently fast to be used in real warehouse systems, <em>e.g.</em>, by using a rolling horizon policy where repositions are calculated for the next block of requests while the current requests are executed. Our method offers managers an additional powerful tool to reduce system response time and thereby increase throughput capacity by smarter scheduling of their automated equipment and more efficient use of available storage space.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104710"},"PeriodicalIF":8.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.tre.2026.104701
Yun Liu , Jun Xia , Zhou Xu
Drones equipped with industrial sensors offer a promising solution for environmental surveillance. This paper studies a new drone scheduling problem for sea area emission surveillance, where drones are utilized to monitor vessel emissions across a continuous sea area for a given planning horizon. The challenges of this optimization problem stem from the varying monitoring requirements within a continuous area due to vessel dynamics and the operational issues of drone deployment, such as multi-trip operations. To address these issues, we discretize the continuous sea area using hexagonal grids and represent the problem through a time-expanded network, resulting in a mixed-integer linear programming formulation for its optimization. To solve large-scale instances, we propose a Lagrangian relaxation-based approach enhanced with a customized lower bounding heuristic. Numerical experiments demonstrate that our approach is very effective and efficient in obtaining high-quality solutions. We conduct a real-world case study based on the Gulf of Mexico’s AIS data to examine the practical implementation of the proposed optimization tool. Furthermore, we investigate how the drone’s operational factors, including the sensor range, endurance, and operational flexibility, affect the monitoring performance.
{"title":"Drone scheduling optimization for continuous sea area monitoring","authors":"Yun Liu , Jun Xia , Zhou Xu","doi":"10.1016/j.tre.2026.104701","DOIUrl":"10.1016/j.tre.2026.104701","url":null,"abstract":"<div><div>Drones equipped with industrial sensors offer a promising solution for environmental surveillance. This paper studies a new drone scheduling problem for sea area emission surveillance, where drones are utilized to monitor vessel emissions across a continuous sea area for a given planning horizon. The challenges of this optimization problem stem from the varying monitoring requirements within a continuous area due to vessel dynamics and the operational issues of drone deployment, such as multi-trip operations. To address these issues, we discretize the continuous sea area using hexagonal grids and represent the problem through a time-expanded network, resulting in a mixed-integer linear programming formulation for its optimization. To solve large-scale instances, we propose a Lagrangian relaxation-based approach enhanced with a customized lower bounding heuristic. Numerical experiments demonstrate that our approach is very effective and efficient in obtaining high-quality solutions. We conduct a real-world case study based on the Gulf of Mexico’s AIS data to examine the practical implementation of the proposed optimization tool. Furthermore, we investigate how the drone’s operational factors, including the sensor range, endurance, and operational flexibility, affect the monitoring performance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104701"},"PeriodicalIF":8.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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