Pub Date : 2026-02-12DOI: 10.1016/j.tre.2026.104742
Yaoxin Wu , Yue Yu , Lingxiao Wu , Tao Feng , Lu Zhang , Zhenkun Wang , Jie Gao
Clustered vehicle routing problems (CluVRPs) represent a complex class of combinatorial optimization problems with significant real-world relevance. They extend classic VRPs by introducing pre-specified customer clusters and requiring effective routing both between clusters and within each cluster. While numerous deep learning approaches have been developed to address the standard VRP, research on CluVRPs remains relatively limited, presenting opportunities and challenges for advancing solutions to more practical VRPs with cluster-related constraints. This paper offers a deep reinforcement learning (DRL) approach to solving CluVRPs. We propose a cluster-aware attention module in the encoder, along with inter-cluster and intra-cluster decoders to specialize the constructive policies within and between clusters. Symmetrical data augmentation is adopted in the training to improve the performance. Empirical results in different CluVRP variants manifest that the DRL method outperforms existing approaches, consistently offering advantages for various instances.
{"title":"Deep reinforcement learning approach to solving clustered vehicle routing problems","authors":"Yaoxin Wu , Yue Yu , Lingxiao Wu , Tao Feng , Lu Zhang , Zhenkun Wang , Jie Gao","doi":"10.1016/j.tre.2026.104742","DOIUrl":"10.1016/j.tre.2026.104742","url":null,"abstract":"<div><div>Clustered vehicle routing problems (CluVRPs) represent a complex class of combinatorial optimization problems with significant real-world relevance. They extend classic VRPs by introducing pre-specified customer clusters and requiring effective routing both between clusters and within each cluster. While numerous deep learning approaches have been developed to address the standard VRP, research on CluVRPs remains relatively limited, presenting opportunities and challenges for advancing solutions to more practical VRPs with cluster-related constraints. This paper offers a deep reinforcement learning (DRL) approach to solving CluVRPs. We propose a cluster-aware attention module in the encoder, along with inter-cluster and intra-cluster decoders to specialize the constructive policies within and between clusters. Symmetrical data augmentation is adopted in the training to improve the performance. Empirical results in different CluVRP variants manifest that the DRL method outperforms existing approaches, consistently offering advantages for various instances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104742"},"PeriodicalIF":8.8,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174547","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 : 2026-02-11DOI: 10.1016/j.tre.2026.104734
Hasnain Ali , Kadir Dönmez , Wei Lun Lim , Sameer Alam
<div><div>As global air traffic continues to grow, the efficient utilization of airport terminal gates has become critical for adhering to turnaround schedules, minimizing arrival delay propagation, and reducing missed passenger connections. The Gate Assignment Problem (GAP)—which involves allocating arriving (and departing) aircraft to gates under operational constraints—has traditionally been addressed using exact optimization methods, heuristics, and metaheuristics. However, these methods struggle to either scale or adapt to the uncertainty and complexity of real-world airport operations. In recent years, Machine Learning (ML) has emerged as a promising alternative or complement to classical methods, offering a fundamentally data-driven approach to prediction and adaptive decision-making. ML techniques have shown potential to anticipate disruptions before they occur, rapidly approximate optimal solutions, and learn complex, nonlinear patterns in historical gate assignments that are difficult to codify using handcrafted heuristics. Yet, despite increasing academic interest, the application of ML to GAP remains fragmented and poorly synthesized. Existing studies apply diverse ML techniques and hybrid models but rarely benchmark them against traditional or standalone counterparts, and rely on inconsistent evaluation practices—using non-standardized, often proprietary datasets with limited reproducibility—hindering comparative analysis and generalizability.</div><div>This paper presents a systematic literature review (SLR) of ML-based approaches for solving the GAP, covering 21 peer-reviewed studies published between 2016 and 2025. We organize our review around three guiding research questions: (i) the comparative strengths and limitations of ML methods versus traditional optimization techniques; (ii) the design and performance of hybrid ML–optimization frameworks; and (iii) the types of datasets and feature sets used in ML-based GAP studies, and the extent to which they reflect the complexity and variability of real-world airport operations. Following the Kitchenham approach, we synthesize findings from peer-reviewed studies, highlighting trends and gaps to guide future gate assignment research and system development. Our review reveals that ML-based techniques—particularly reinforcement learning and supervised delay predictors—offer strong potential for handling uncertainty and improving decision quality compared to traditional optimization methods. However, their effectiveness is often limited by data availability and lack of interpretability. Hybrid ML–optimization frameworks show promise in combining predictive and search capabilities, but current designs are ad hoc and rarely benchmarked against their standalone components. Most ML-based GAP studies rely on narrow, single-airport datasets that omit key operational dynamics, limiting generalizability and real-world relevance. To address these gaps, we propose future directions: (1) developing
{"title":"Machine learning algorithms and models for airport gate assignment problem: A systematic literature review","authors":"Hasnain Ali , Kadir Dönmez , Wei Lun Lim , Sameer Alam","doi":"10.1016/j.tre.2026.104734","DOIUrl":"10.1016/j.tre.2026.104734","url":null,"abstract":"<div><div>As global air traffic continues to grow, the efficient utilization of airport terminal gates has become critical for adhering to turnaround schedules, minimizing arrival delay propagation, and reducing missed passenger connections. The Gate Assignment Problem (GAP)—which involves allocating arriving (and departing) aircraft to gates under operational constraints—has traditionally been addressed using exact optimization methods, heuristics, and metaheuristics. However, these methods struggle to either scale or adapt to the uncertainty and complexity of real-world airport operations. In recent years, Machine Learning (ML) has emerged as a promising alternative or complement to classical methods, offering a fundamentally data-driven approach to prediction and adaptive decision-making. ML techniques have shown potential to anticipate disruptions before they occur, rapidly approximate optimal solutions, and learn complex, nonlinear patterns in historical gate assignments that are difficult to codify using handcrafted heuristics. Yet, despite increasing academic interest, the application of ML to GAP remains fragmented and poorly synthesized. Existing studies apply diverse ML techniques and hybrid models but rarely benchmark them against traditional or standalone counterparts, and rely on inconsistent evaluation practices—using non-standardized, often proprietary datasets with limited reproducibility—hindering comparative analysis and generalizability.</div><div>This paper presents a systematic literature review (SLR) of ML-based approaches for solving the GAP, covering 21 peer-reviewed studies published between 2016 and 2025. We organize our review around three guiding research questions: (i) the comparative strengths and limitations of ML methods versus traditional optimization techniques; (ii) the design and performance of hybrid ML–optimization frameworks; and (iii) the types of datasets and feature sets used in ML-based GAP studies, and the extent to which they reflect the complexity and variability of real-world airport operations. Following the Kitchenham approach, we synthesize findings from peer-reviewed studies, highlighting trends and gaps to guide future gate assignment research and system development. Our review reveals that ML-based techniques—particularly reinforcement learning and supervised delay predictors—offer strong potential for handling uncertainty and improving decision quality compared to traditional optimization methods. However, their effectiveness is often limited by data availability and lack of interpretability. Hybrid ML–optimization frameworks show promise in combining predictive and search capabilities, but current designs are ad hoc and rarely benchmarked against their standalone components. Most ML-based GAP studies rely on narrow, single-airport datasets that omit key operational dynamics, limiting generalizability and real-world relevance. To address these gaps, we propose future directions: (1) developing","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104734"},"PeriodicalIF":8.8,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160932","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 : 2026-02-11DOI: 10.1016/j.tre.2026.104732
Wenhao Peng , Dujuan Wang , Hengfei Yang , T.C.E. Cheng
The rapid development of smart cities has prompted the upgrading of drone transport services. This study examines a new variant of the drone routing problem (DRP), which considers a homogeneous group of drones transporting medical supplies to multiple hospitals or medical centers with pre-specified deadlines. On each trip, the drone is allowed to land on existing charging platforms, with decisions made regarding when and where to recharge, as well as the duration of each charging session. We also consider that the drone flight time is uncertain, and the drone power consumption is nonlinearly dependent on its payload. To address this problem, we first propose a robust optimization model grounded in the well-known budgeted uncertainty set. Subsequently, we design a tailored branch-and-price (B&P) algorithm. This algorithm employs a variable neighborhood search (VNS) strategy to effectively solve the subproblem. In VNS, we develop four kinds of neighborhood structures to explore the solution space effectively. Also, to avoid falling into a local optimum, a shaking operation is introduced. Extensive numerical experiments are conducted to evaluate the algorithm’s effectiveness, highlight the advantages of robustness in handling uncertainty, and examine how critical model parameters influence the resulting solutions. Finally, we also use the real data of the blood center in Chongqing, China, to illustrate the application of the model.
{"title":"Robust drone delivery with partial recharging strategy in urban medical logistics","authors":"Wenhao Peng , Dujuan Wang , Hengfei Yang , T.C.E. Cheng","doi":"10.1016/j.tre.2026.104732","DOIUrl":"10.1016/j.tre.2026.104732","url":null,"abstract":"<div><div>The rapid development of smart cities has prompted the upgrading of drone transport services. This study examines a new variant of the drone routing problem (DRP), which considers a homogeneous group of drones transporting medical supplies to multiple hospitals or medical centers with pre-specified deadlines. On each trip, the drone is allowed to land on existing charging platforms, with decisions made regarding when and where to recharge, as well as the duration of each charging session. We also consider that the drone flight time is uncertain, and the drone power consumption is nonlinearly dependent on its payload. To address this problem, we first propose a robust optimization model grounded in the well-known budgeted uncertainty set. Subsequently, we design a tailored branch-and-price (B&P) algorithm. This algorithm employs a variable neighborhood search (VNS) strategy to effectively solve the subproblem. In VNS, we develop four kinds of neighborhood structures to explore the solution space effectively. Also, to avoid falling into a local optimum, a shaking operation is introduced. Extensive numerical experiments are conducted to evaluate the algorithm’s effectiveness, highlight the advantages of robustness in handling uncertainty, and examine how critical model parameters influence the resulting solutions. Finally, we also use the real data of the blood center in Chongqing, China, to illustrate the application of the model.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104732"},"PeriodicalIF":8.8,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153272","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 : 2026-02-11DOI: 10.1016/j.tre.2026.104717
Quanyao Cao , T.C. Edwin Cheng , Shun Jia , Yang Liu
As a subfield of the circular economy, industrial symbiosis has rapidly emerged as a development mode that efficiently utilizes waste resources and reduces environmental pollution. However, challenges in industrial cooperation and inadequate corporate financing support have hindered its development. We consider an industrial symbiosis chain consisting of a capital-constrained upstream manufacturer and a downstream manufacturer, where the latter can reuse the upstream manufacturer’s by-product to replace raw materials for production. We analyze purchase order financing as pre-shipment financing and factoring as post-shipment financing, while also considering buyer direct financing as an alternative pre-shipment financing method and purchase commitment as a specific type of smart contract. Given the uncertainty in market demands and the mismatch between the supply and demand of by-products, we develop a two-stage game theoretical model to analyze firms’ production decisions and explore the role of different financing modes. In addition, we examine information friction in both financing and symbiotic aspects and investigate the value of smart contracts within the industrial symbiosis chain. Furthermore, we extend our study to utilize Pigouvian taxation to internalize environmental pollution externalities. This innovation in the financing mode of the industrial symbiotic chain provides theoretical support for supply chain finance aimed at promoting industrial symbiosis and enhancing industrial cooperation through the adoption of smart contracts.
{"title":"Financing strategy for industrial symbiosis chains with a capital-constrained upstream manufacturer considering by-product supply-demand mismatch","authors":"Quanyao Cao , T.C. Edwin Cheng , Shun Jia , Yang Liu","doi":"10.1016/j.tre.2026.104717","DOIUrl":"10.1016/j.tre.2026.104717","url":null,"abstract":"<div><div>As a subfield of the circular economy, industrial symbiosis has rapidly emerged as a development mode that efficiently utilizes waste resources and reduces environmental pollution. However, challenges in industrial cooperation and inadequate corporate financing support have hindered its development. We consider an industrial symbiosis chain consisting of a capital-constrained upstream manufacturer and a downstream manufacturer, where the latter can reuse the upstream manufacturer’s by-product to replace raw materials for production. We analyze purchase order financing as pre-shipment financing and factoring as post-shipment financing, while also considering buyer direct financing as an alternative pre-shipment financing method and purchase commitment as a specific type of smart contract. Given the uncertainty in market demands and the mismatch between the supply and demand of by-products, we develop a two-stage game theoretical model to analyze firms’ production decisions and explore the role of different financing modes. In addition, we examine information friction in both financing and symbiotic aspects and investigate the value of smart contracts within the industrial symbiosis chain. Furthermore, we extend our study to utilize Pigouvian taxation to internalize environmental pollution externalities. This innovation in the financing mode of the industrial symbiotic chain provides theoretical support for supply chain finance aimed at promoting industrial symbiosis and enhancing industrial cooperation through the adoption of smart contracts.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104717"},"PeriodicalIF":8.8,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161073","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 : 2026-02-10DOI: 10.1016/j.tre.2026.104733
Chung-Shan Yang
The maritime logistics sector faces growing pressure to adapt its operations in response to escalating geopolitical disruptions and sustainability imperatives. This study develops and empirically tests an integrated structural model to examine how geopolitical shocks and sustainability requirements influence value chain restructuring, supply chain reconfiguration, and overall supply chain performance. Drawing on global value chain theory and the dynamic capabilities framework, the model is evaluated using data from 154 senior executives in the maritime logistics sector. Measurement constructs are validated through confirmatory factor analysis (CFA), and hypotheses are tested using structural equation modeling (SEM). Results reveal that sustainability requirements exert a stronger influence on long-term value chain restructuring, while geopolitical shocks significantly affect both value chain restructuring and supply chain reconfiguration. In particular, geopolitical shocks display a significant direct effect on tactical reconfiguration and a stronger effect on strategic restructuring, which in turn cascades into downstream reconfiguration. Both restructuring and reconfiguration are found to significantly enhance supply chain performance. This research offers novel empirical evidence on adaptive capabilities in maritime logistics, contributing to the theoretical understanding of supply chain resilience and informing policy and managerial strategies in an era of systemic disruption.
{"title":"Geopolitical disruptions and sustainability imperatives: a structural model of value chain and supply chain transformation in maritime logistics","authors":"Chung-Shan Yang","doi":"10.1016/j.tre.2026.104733","DOIUrl":"10.1016/j.tre.2026.104733","url":null,"abstract":"<div><div>The maritime logistics sector faces growing pressure to adapt its operations in response to escalating geopolitical disruptions and sustainability imperatives. This study develops and empirically tests an integrated structural model to examine how geopolitical shocks and sustainability requirements influence value chain restructuring, supply chain reconfiguration, and overall supply chain performance. Drawing on global value chain theory and the dynamic capabilities framework, the model is evaluated using data from 154 senior executives in the maritime logistics sector. Measurement constructs are validated through confirmatory factor analysis (CFA), and hypotheses are tested using structural equation modeling (SEM). Results reveal that sustainability requirements exert a stronger influence on long-term value chain restructuring, while geopolitical shocks significantly affect both value chain restructuring and supply chain reconfiguration. In particular, geopolitical shocks display a significant direct effect on tactical reconfiguration and a stronger effect on strategic restructuring, which in turn cascades into downstream reconfiguration. Both restructuring and reconfiguration are found to significantly enhance supply chain performance. This research offers novel empirical evidence on adaptive capabilities in maritime logistics, contributing to the theoretical understanding of supply chain resilience and informing policy and managerial strategies in an era of systemic disruption.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104733"},"PeriodicalIF":8.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146723","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 : 2026-02-10DOI: 10.1016/j.tre.2026.104693
Mina Valaei , Ahmed Saif , Hassan Sarhadi , Hamid Afshari
This paper presents a new robust optimization approach for prepositioning search and rescue (SAR) vessels to coastal marine stations and allocating maritime areas to them. The objective is to maximize the coverage of maritime incidents, considering operational constraints related to vessel capacities, volunteers’ participation and involvement, total travel distances, and the timeliness of rescue operations. To account for uncertainty in the spatial distribution of future incidents, two types of uncertainty sets are developed, and the location-allocation is optimized for the worst-case distribution within each set. The first one is based on the Total Variation (TV) ϕ-divergence, while the second one uses the 1-Wasserstein distance to measure the deviation between the nominal and the true distribution. Furthermore, a Benders decomposition (BD) algorithm is developed to solve the robust problems more efficiently. The proposed approaches are implemented to design a SAR network in the Gulf of Finland using historical incident data. Numerical results demonstrated the superior out-of-sample average and quartile performances of the robust models, despite their higher computational burdens, compared to the deterministic one, and that the ϕ-divergence uncertainty set led to less conservative solutions compared to the Wasserstein-metric-based set. Furthermore, the BD algorithm significantly reduced the computational time for middle-range values of the uncertainty budgets, enabling real-sized instances to be solved effectively. A detailed sensitivity analysis was performed, and managerial insights were drawn from the results. Most notably, both the total allowable travel distance and the response time threshold significantly affected incident coverage in busy days, with a more profound impact of the former factor, whereas only the latter moderately affected coverage in low-activity days. The paper contributes to the maritime SAR literature by demonstrating how the spatial uncertainty of future incidents can be handled rigorously rather than relying naively on historical incident patterns to design SAR networks.
{"title":"Robust prepositioning and allocation of maritime search and rescue vessels with incident location uncertainty","authors":"Mina Valaei , Ahmed Saif , Hassan Sarhadi , Hamid Afshari","doi":"10.1016/j.tre.2026.104693","DOIUrl":"10.1016/j.tre.2026.104693","url":null,"abstract":"<div><div>This paper presents a new robust optimization approach for prepositioning search and rescue (SAR) vessels to coastal marine stations and allocating maritime areas to them. The objective is to maximize the coverage of maritime incidents, considering operational constraints related to vessel capacities, volunteers’ participation and involvement, total travel distances, and the timeliness of rescue operations. To account for uncertainty in the spatial distribution of future incidents, two types of uncertainty sets are developed, and the location-allocation is optimized for the worst-case distribution within each set. The first one is based on the Total Variation (TV) <em>ϕ</em>-divergence, while the second one uses the 1-Wasserstein distance to measure the deviation between the nominal and the true distribution. Furthermore, a Benders decomposition (BD) algorithm is developed to solve the robust problems more efficiently. The proposed approaches are implemented to design a SAR network in the Gulf of Finland using historical incident data. Numerical results demonstrated the superior out-of-sample average and quartile performances of the robust models, despite their higher computational burdens, compared to the deterministic one, and that the <em>ϕ</em>-divergence uncertainty set led to less conservative solutions compared to the Wasserstein-metric-based set. Furthermore, the BD algorithm significantly reduced the computational time for middle-range values of the uncertainty budgets, enabling real-sized instances to be solved effectively. A detailed sensitivity analysis was performed, and managerial insights were drawn from the results. Most notably, both the total allowable travel distance and the response time threshold significantly affected incident coverage in busy days, with a more profound impact of the former factor, whereas only the latter moderately affected coverage in low-activity days. The paper contributes to the maritime SAR literature by demonstrating how the spatial uncertainty of future incidents can be handled rigorously rather than relying naively on historical incident patterns to design SAR networks.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104693"},"PeriodicalIF":8.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153277","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 : 2026-02-10DOI: 10.1016/j.tre.2026.104722
Peixiang Wang , Hongye Zhao , Yufei Li , Runzhi Tan , Qunlong Chen , Wei Qin , Heng Huang , Yu Tian , Dong Xu
Efficient container stowage planning in large port operations is paramount for ensuring vessel stability, navigational safety, and overall terminal efficiency, especially under the growing demands for smarter and greener maritime logistics. Port operators must meticulously arrange thousands of containers, adhering to preliminary plans from shipping lines while optimizing for critical factors such as weight distribution, minimization of container relocations within the yard, and seamless integration with dynamic terminal operational processes like twin-lift handling and quay crane workflows. This study proposes an optimization framework to generate complete stowage plans for large vessels from the terminal’s perspective, distinguished by its ability to identify and eliminate complex relocation patterns involving multiple containers. An efficient solution methodology is designed, integrating mixed-integer programming models for initial bay allocation and subsequent slot positioning, followed by an advanced neighborhood search algorithm. This search algorithm incorporates a novel graph-based relocation detection technique, utilizing matrix exponentiation to identify and minimize complex k-cycle relocation patterns. Based on operational data from a major international terminal (Yangshan Port), extensive numerical experiments and a real-world case study were conducted. The results validate the framework’s capability to produce robust, high-quality stowage plans for large vessels about 10 minutes, leading to a 16.8% reduction in container relocations and enhanced terminal efficiency, thereby offering valuable managerial insights for advanced stowage planning.
{"title":"Hybrid optimization strategies for terminal-oriented container stowage and relocation in large port operations","authors":"Peixiang Wang , Hongye Zhao , Yufei Li , Runzhi Tan , Qunlong Chen , Wei Qin , Heng Huang , Yu Tian , Dong Xu","doi":"10.1016/j.tre.2026.104722","DOIUrl":"10.1016/j.tre.2026.104722","url":null,"abstract":"<div><div>Efficient container stowage planning in large port operations is paramount for ensuring vessel stability, navigational safety, and overall terminal efficiency, especially under the growing demands for smarter and greener maritime logistics. Port operators must meticulously arrange thousands of containers, adhering to preliminary plans from shipping lines while optimizing for critical factors such as weight distribution, minimization of container relocations within the yard, and seamless integration with dynamic terminal operational processes like twin-lift handling and quay crane workflows. This study proposes an optimization framework to generate complete stowage plans for large vessels from the terminal’s perspective, distinguished by its ability to identify and eliminate complex relocation patterns involving multiple containers. An efficient solution methodology is designed, integrating mixed-integer programming models for initial bay allocation and subsequent slot positioning, followed by an advanced neighborhood search algorithm. This search algorithm incorporates a novel graph-based relocation detection technique, utilizing matrix exponentiation to identify and minimize complex k-cycle relocation patterns. Based on operational data from a major international terminal (Yangshan Port), extensive numerical experiments and a real-world case study were conducted. The results validate the framework’s capability to produce robust, high-quality stowage plans for large vessels about 10 minutes, leading to a 16.8% reduction in container relocations and enhanced terminal efficiency, thereby offering valuable managerial insights for advanced stowage planning.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104722"},"PeriodicalIF":8.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153278","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 : 2026-02-10DOI: 10.1016/j.tre.2026.104738
Hui Liu , Guanghua Song , Song Huang
This study investigates firms’ incentive provision decisions for consumer deliberation in a dyadic supply chain wherein consumers face valuation uncertainty regarding a new product. In contrast to conventional models focusing solely on pure profit maximization, we consider a manufacturer and retailer who also value consumer surplus. Using a game-theoretical model, we analyze two scenarios: one where the manufacturer has a dual-purpose concern and another where the retailer does. The strategic interaction between the firm’s concern for consumer surplus and consumers’ deliberation behavior yields nontrivial implications for equilibrium strategies and channel performance. First, the firm’s dual-purpose concern fundamentally alters the manufacturer’s incentive provision strategies. Specifically, the manufacturer’s incentive to inhibit consumer deliberation increases with the firm’s degree of concern for consumer surplus, with this effect amplified in the case of the dual-purpose retailer. Second, counter to the intuition that a dual-purpose retailer would be worse off by deviating from pure profit maximization, we find that in certain scenarios, the retailer’s dual-purpose orientation can yield a “win-win” situation for both firms. However, this outcome does not occur when the manufacturer adopts a dual-purpose focus. Third, both the deliberation cost and degree of concern regarding consumer surplus exhibit non-monotonic effects on the firms’ profits. Notably, consumer surplus and social welfare with a dual-purpose retailer are superior, or at least equivalent, to those with a dual-purpose manufacturer. Finally, the main results remain qualitatively valid when both firms are concerned about consumer surplus or when either firm places excessive focus on the same.
{"title":"Incentive provision for consumer deliberation in a supply chain with dual-purpose organizations","authors":"Hui Liu , Guanghua Song , Song Huang","doi":"10.1016/j.tre.2026.104738","DOIUrl":"10.1016/j.tre.2026.104738","url":null,"abstract":"<div><div>This study investigates firms’ incentive provision decisions for consumer deliberation in a dyadic supply chain wherein consumers face valuation uncertainty regarding a new product. In contrast to conventional models focusing solely on pure profit maximization, we consider a manufacturer and retailer who also value consumer surplus. Using a game-theoretical model, we analyze two scenarios: one where the manufacturer has a dual-purpose concern and another where the retailer does. The strategic interaction between the firm’s concern for consumer surplus and consumers’ deliberation behavior yields nontrivial implications for equilibrium strategies and channel performance. First, the firm’s dual-purpose concern fundamentally alters the manufacturer’s incentive provision strategies. Specifically, the manufacturer’s incentive to inhibit consumer deliberation increases with the firm’s degree of concern for consumer surplus, with this effect amplified in the case of the dual-purpose retailer. Second, counter to the intuition that a dual-purpose retailer would be worse off by deviating from pure profit maximization, we find that in certain scenarios, the retailer’s dual-purpose orientation can yield a “win-win” situation for both firms. However, this outcome does not occur when the manufacturer adopts a dual-purpose focus. Third, both the deliberation cost and degree of concern regarding consumer surplus exhibit non-monotonic effects on the firms’ profits. Notably, consumer surplus and social welfare with a dual-purpose retailer are superior, or at least equivalent, to those with a dual-purpose manufacturer. Finally, the main results remain qualitatively valid when both firms are concerned about consumer surplus or when either firm places excessive focus on the same.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104738"},"PeriodicalIF":8.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152702","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 : 2026-02-10DOI: 10.1016/j.tre.2026.104726
Hyunhwa Kim, Denissa Sari Darmawi Purba, Eleftheria Kontou
In the aftermath of hazards, energy supply to demand nodes is constrained due to power outages caused by damaged infrastructure. Electric uncrewed ground vehicles (UGVs) and aerial vehicles (UAVs) can play a crucial role in providing backup power, discharging energy directly to meet demand, and recharging en-route if necessary. However, charging infrastructure that replenishes backup power has limited capacity and can be inoperable due to disruptions of the main power system. Since certain energy demands are urgent in post-disaster contexts (i.e., healthcare loads), UGVs and UAVs prioritize these critical needs. As a result, UGVs and UAVs may meet only portion of the energy needs due to constrained resources. This research aims to design a bidirectional energy supply logistics model, as a two-echelon electric vehicle location-routing problem with resource-constrained demand allocation and time windows, employing UGVs and UAVs. The first echelon involves deploying UGVs and UAVs from a depot to satellite charging locations. In the second echelon, these vehicles travel from satellites to serve local energy demand, either fully or partially within the constrained resource budget discharging their batteries and recharging en-route as needed. We formulate this model and propose a metaheuristic solution which consists of a two-stage approach based on the adaptive large neighborhood search. Numerical experiments on benchmark instances were conducted to evaluate the novel heuristic’s performance compared to a commercial solver. Sensitivity analysis was carried out to examine the impact of UAV and UGV batteries capacity, budget for charging infrastructure, and the amount of energy resources. We applied our model to a real-world case of post-wildfire humanitarian aid in California’s counties aiming to supply energy during large-scale power loss in the region.
{"title":"Bidirectional energy supply logistics using uncrewed electric aerial and ground vehicles: A two-echelon location-routing problem with resource-constrained demand allocation and time windows","authors":"Hyunhwa Kim, Denissa Sari Darmawi Purba, Eleftheria Kontou","doi":"10.1016/j.tre.2026.104726","DOIUrl":"10.1016/j.tre.2026.104726","url":null,"abstract":"<div><div>In the aftermath of hazards, energy supply to demand nodes is constrained due to power outages caused by damaged infrastructure. Electric uncrewed ground vehicles (UGVs) and aerial vehicles (UAVs) can play a crucial role in providing backup power, discharging energy directly to meet demand, and recharging en-route if necessary. However, charging infrastructure that replenishes backup power has limited capacity and can be inoperable due to disruptions of the main power system. Since certain energy demands are urgent in post-disaster contexts (i.e., healthcare loads), UGVs and UAVs prioritize these critical needs. As a result, UGVs and UAVs may meet only portion of the energy needs due to constrained resources. This research aims to design a bidirectional energy supply logistics model, as a two-echelon electric vehicle location-routing problem with resource-constrained demand allocation and time windows, employing UGVs and UAVs. The first echelon involves deploying UGVs and UAVs from a depot to satellite charging locations. In the second echelon, these vehicles travel from satellites to serve local energy demand, either fully or partially within the constrained resource budget discharging their batteries and recharging en-route as needed. We formulate this model and propose a metaheuristic solution which consists of a two-stage approach based on the adaptive large neighborhood search. Numerical experiments on benchmark instances were conducted to evaluate the novel heuristic’s performance compared to a commercial solver. Sensitivity analysis was carried out to examine the impact of UAV and UGV batteries capacity, budget for charging infrastructure, and the amount of energy resources. We applied our model to a real-world case of post-wildfire humanitarian aid in California’s counties aiming to supply energy during large-scale power loss in the region.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104726"},"PeriodicalIF":8.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152642","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 : 2026-02-09DOI: 10.1016/j.tre.2026.104739
Junkai Zhang, Kap Hwan Kim, Ningning Song, Xuehao Feng
The yard slot allocation problem (SAP), which concerns locating containers in the storage yard, could critically affect the performance of ports. The optimization of this problem is challenging due to the complex operational conditions and real-time decision requirement in practice. As a new type of layout, the U-shaped layout offers external and internal trucks (ETs and ITs) novel combinations of travel routes and container handover points that may result in unique characteristics for the SAP. This study addresses the SAP under the U-shaped layout to minimize the delay time of ITs and ETs. A novel simulation-based evaluation method considering multiple criteria is proposed to allocate slots for arriving containers. In this method, an evolving neural decision network (ENDN) is developed to explore the influence of real-time information on the weights of these criteria. We develop an efficient genetic algorithm tailored to optimize the parameters of the ENDN. A simulation model is developed to evaluate the algorithm’s performance under realistic operational uncertainties that may promote the practical implementation of the ENDN. The experimental results demonstrate that our method can determine slot allocations of shorter total vehicle delay time compared with existing methods.
{"title":"Simulation-based optimization of yard slot allocation in U-shaped container terminals","authors":"Junkai Zhang, Kap Hwan Kim, Ningning Song, Xuehao Feng","doi":"10.1016/j.tre.2026.104739","DOIUrl":"10.1016/j.tre.2026.104739","url":null,"abstract":"<div><div>The yard slot allocation problem (SAP), which concerns locating containers in the storage yard, could critically affect the performance of ports. The optimization of this problem is challenging due to the complex operational conditions and real-time decision requirement in practice. As a new type of layout, the U-shaped layout offers external and internal trucks (ETs and ITs) novel combinations of travel routes and container handover points that may result in unique characteristics for the SAP. This study addresses the SAP under the U-shaped layout to minimize the delay time of ITs and ETs. A novel simulation-based evaluation method considering multiple criteria is proposed to allocate slots for arriving containers. In this method, an evolving neural decision network (ENDN) is developed to explore the influence of real-time information on the weights of these criteria. We develop an efficient genetic algorithm tailored to optimize the parameters of the ENDN. A simulation model is developed to evaluate the algorithm’s performance under realistic operational uncertainties that may promote the practical implementation of the ENDN. The experimental results demonstrate that our method can determine slot allocations of shorter total vehicle delay time compared with existing methods.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104739"},"PeriodicalIF":8.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146720","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号