Transportation Research Part E-Logistics and Transportation Review最新文献

{"title":"Returnable-after-deep-trial policies in e-commerce supply chains with opportunistic consumers","authors":"Zhengzheng Chen,&nbsp;Tiaojun Xiao","doi":"10.1016/j.tre.2026.104728","DOIUrl":"10.1016/j.tre.2026.104728","url":null,"abstract":"<div><div>In response to consumer desire for a deep experience of product performance, some online sellers have strengthened their traditional money back guarantee by implementing the “returnable after a deep trial” (RADT) policy. But such a policy has also encouraged opportunistic behavior. Considering such opportunistic behavior for consumers, we build an e-commerce supply chain model to investigate the price decisions and the return strategy (implementing RADT or not) of online sellers under two selling modes: agency selling and reselling. We show that the RADT is often expensive for consumers. Besides, counterintuitively, when the online seller allows for an increased degree of deep trial, the seller is highly likely to decrease the retail price and the refund amount, simultaneously. Moreover, opportunistic consumers also have strategic value: although consumers’ opportunistic behavior under the RADT seemingly harms the online seller, in fact, the seller may generate profits by strategically encouraging opportunistic behavior. Furthermore, under agency selling, when consumers’ benefit from opportunistic behavior is not extremely small, and most ordinary consumers directly return the product, the RADT is optimal; while under reselling, the RADT is optimal if most ordinary consumers directly return the product. Surprisingly, more opportunistic consumers increase the online seller’s incentive to adopt the RADT policy.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104728"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146724","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}

{"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":"2026-05-01","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}

{"title":"Machine learning algorithms and models for airport gate assignment problem: A systematic literature review","authors":"Hasnain Ali ,&nbsp;Kadir Dönmez ,&nbsp;Wei Lun Lim ,&nbsp;Sameer Alam","doi":"10.1016/j.tre.2026.104734","DOIUrl":"10.1016/j.tre.2026.104734","url":null,"abstract":"&lt;div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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":"2026-05-01","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}

{"title":"Techno-economic optimization for containership green transition under net-zero emission target: a predict-then-optimize framework","authors":"Leqi Zhou ,&nbsp;Jian Gang Jin ,&nbsp;Yuling Gao ,&nbsp;Çağatay Iris ,&nbsp;Tolga Bektaş ,&nbsp;Jiang Hang Chen","doi":"10.1016/j.tre.2026.104731","DOIUrl":"10.1016/j.tre.2026.104731","url":null,"abstract":"<div><div>Shipping companies are under growing pressure to meet net-zero emission target under tightening regulations. To tackle this challenge, this paper extends the techno-economic predict-then-optimize framework to make long-term strategic decisions that jointly minimize greenhouse gas emissions and lifecycle costs over the planning horizon. The framework integrates a vessel-specific fuel consumption prediction model for comprehensive estimation of future emissions, and an optimization model that strategically schedules and combines multiple emission reduction methods. The optimization explicitly accounts for the techno-economic trade-offs among slow steaming, maintenance, fuel switching, and capital investments for retrofits, while ensuring compliance with carbon intensity indicator (CII) regulations and the overall net-zero emission target. A case study on several containerships demonstrates the applicability of the model, with numerical experiments showing that the predict-then-optimize framework achieves a 67% carbon reduction by 2040. Further investigations of the strategies, including illustration of transition plans, comparisons of different strategies, evaluation of marginal costs for each method, and sensitivity analysis on interest rates and regulations, are conducted to provide insights into lifecycle cost-minimizing and emission regulation-compliant strategy design.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104731"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146722","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}

{"title":"Supply chain Holdings: The “Fire Extinguisher” or the “Fuel Booster” for supply chain Risks?","authors":"Duo Wang,&nbsp;Yunge Hu,&nbsp;Yanxi Li","doi":"10.1016/j.tre.2026.104703","DOIUrl":"10.1016/j.tre.2026.104703","url":null,"abstract":"<div><div>Based on relational theory, supply chain shareholding, as a novel feature of supply chains, has the potential to strengthen collaborative ties between upstream and downstream enterprises, effectively mitigating supply chain risks and acting as a “fire extinguisher.” Conversely, it may also intensify interdependencies among firms, making risk transmission within the supply network more likely, thereby functioning as a “fuel booster” for supply chain risks. This study reveals that supply chain holdings play a pivotal “fire extinguisher” role in corporate supply chain risk management, with such effects primarily driven by customer holdings. Specifically, supply chain holdings operate through three principal mechanisms: information transmission facilitation, business stability enhancement, and capital provision within supply chain networks. Heterogeneity analysis further demonstrates that the risk-alleviating effects are particularly pronounced under conditions of substantial supply chain information frictions, elevated external environmental risks, heightened financing constraints, diminished external bargaining power, or weakened shareholder control power. Moreover, the risk-mitigating effect of supply chain holdings persists even under unforeseen risk scenarios. This research not only expands the understanding of the economic consequences of supply chain holding from the perspective of supply chain risk management but also provides an important theoretical foundation for enterprises to effectively prevent supply chain risks and achieve high-quality development.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104703"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110246","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}

{"title":"Drone scheduling optimization for shore-to-ship delivery and waste recycling","authors":"Tingsong Wang ,&nbsp;Haiqing Zhou ,&nbsp;Xuecheng Tian","doi":"10.1016/j.tre.2026.104708","DOIUrl":"10.1016/j.tre.2026.104708","url":null,"abstract":"<div><div>Traditional shore-to-ship delivery and waste recycling predominantly rely on replenishment vessels, facing critical challenges such as low operational efficiency, high costs, and prolonged waiting times during multi-vessel operations. To address these challenges, this paper addresses the Drone Scheduling Problem in Shore-to-Ship Delivery and Recycling (DSP-SSDR). Specifically, we develop a mixed-integer programming (MIP) model to minimize the total completion time of all required tasks while accounting for practical constraints including drone load capacity, time windows, battery consumption, multiple trips, and dynamic base station return. To efficiently solve this problem, an adaptive large neighborhood search (ALNS) algorithm is proposed and improved, which dynamically adapts removal and repair operators and incorporates a two-layer local search to improve the solution quality. Numerical experiments using practical realistic test cases demonstrate that the proposed ALNS algorithm achieves superior performance compared to the commercial solver Gurobi, particularly in computational efficiency for large-scale instances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104708"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135140","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}

{"title":"Balancing coopetition in innovation-driven circular business models: the interplay of environmental welfare and competitive advantages","authors":"Mohammad Ali Hassanabadi,&nbsp;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":"2026-05-01","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}

{"title":"Resilient service of shipping alliance under disruption risk","authors":"Baozhuang Niu ,&nbsp;Jianhua Zhang ,&nbsp;Fengfeng Xie ,&nbsp;Zhipeng Dai ,&nbsp;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":"2026-05-01","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}

{"title":"Robust prepositioning and allocation of maritime search and rescue vessels with incident location uncertainty","authors":"Mina Valaei ,&nbsp;Ahmed Saif ,&nbsp;Hassan Sarhadi ,&nbsp;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":"2026-05-01","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}

{"title":"The logistics vehicle charging station location selection and routing problem with partial recharging and shared fleets","authors":"Yong Wang ,&nbsp;Shiqi Zhang ,&nbsp;Jing Liu ,&nbsp;Yuanhan Wei ,&nbsp;Haizhong Wang","doi":"10.1016/j.tre.2026.104723","DOIUrl":"10.1016/j.tre.2026.104723","url":null,"abstract":"<div><div>Growing global concern over environmental protection and climate change has led governments worldwide to promote electric vehicles to support carbon neutrality goals. However, limited charging infrastructure, particularly for urban logistics, remains a major barrier to the adoption of electric vehicles for delivery services. This study examines the logistics vehicle charging station location selection and routing problem with partial recharging and shared fleets. An electric vehicle charging model and a nonlinear energy consumption model are formulated to better represent real-world energy use and charging behavior. A bi-objective optimization model is proposed to minimize total operating costs and the required number of vehicles. To solve the model, a hybrid algorithm combining 3D <em>k</em>-means clustering and an improved multi-objective particle swarm optimization (IMOPSO) is developed. The 3D <em>k</em>-means clustering groups spatiotemporal customer data to support periodic resource allocation. The IMOPSO incorporates an enhanced update mechanism and elite selection to improve solution quality and convergence speed. In addition, a resource sharing strategy and a charging station insertion method are applied to further optimize vehicle deployment and station selection. The performance of IMOPSO is evaluated against the CPLEX solver, an improved non-dominated sorting genetic algorithm II, a flexible variable neighborhood search algorithm, and a multi-objective genetic algorithm with simulated annealing. A real-world case study in Chongqing City, China, assesses the proposed approach under sensitivity analysis of model parameters, five recharging levels, multiple resource sharing scenarios, and different collaboration modes. The results indicate that the proposed method supports efficient planning of urban delivery systems and charging infrastructure, contributing to a greener and more cost-effective logistics network.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104723"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110247","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}