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

{"title":"Multi-modal travel route planning considering environmental preference under uncertainties: A distributionally robust optimization approach","authors":"Xiangting Wang ,&nbsp;Ying Lv ,&nbsp;Huijun Sun ,&nbsp;Xingrong Wang ,&nbsp;Chuang Zhu","doi":"10.1016/j.tre.2025.104097","DOIUrl":"10.1016/j.tre.2025.104097","url":null,"abstract":"<div><div>MaaS (Mobility as a Service) is the main trend in future transportation development. From the user perspective, it is primarily manifested as a shift in travel behavior, transitioning from reliance on single modes, such as private cars, to a mixed mode of various transportation options. In order to facilitate providing door-to-door services for travelers, this paper proposes a user-centric route planning approach under a new multi-modal framework, which it considers five travel modes, including bus, metro, car-hailing, as well as bike-sharing and walking that effectively addresses the last mile problem. Given the diverse travel objectives among travelers, this paper integrates travel time, cost, comfort, and green travel awareness into the objective function. Moreover, a multi-modal network travel route optimization model is established to generate route planning that aligns with traveler’s preferences. To address the challenges of multiple time uncertainties and incomplete distribution information resulting from problems such as road congestion and uneven distribution of bike-sharing and car-hailing during a trip, this paper proposes a distributionally robust optimization model to describe the uncertainties in two dimensions of the objective function. A generalized interval-valued trapezoidal possibility distribution is used to describe the time for finding a bike-sharing or for waiting a car-hailing service. The robust objective function and constraints are equivalently formulated as a deterministic model. The distributionally robust optimization model for uncertain travel times of buses and car-hailing services is demonstrated to be semi-infinite but can be safely and equivalently approximated under the Gaussian perturbations ambiguity set. Through comparative analyses with the traditional robust optimization method using experimental cases, the proposed distributionally robust optimization model exhibits superior performance. In addition, sensitivity analyzes are conducted on the relevant factors that influence travelers’ reduction in carbon emissions after the implementation of carbon incentive measures. The results demonstrate the effectiveness of the incentives introduced, which provides valuable information for the government to improve various incentive measures aimed at promoting low-carbon travel among travelers.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104097"},"PeriodicalIF":8.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785898","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":"Are electric vehicles greener than hybrid electric vehicles in carsharing? Insights from large-scale multi-objective simulation-optimization","authors":"Yan Li ,&nbsp;Lu Hu ,&nbsp;Haobin Li ,&nbsp;Ek Peng Chew ,&nbsp;Hao Li ,&nbsp;Juanxiu Zhu","doi":"10.1016/j.tre.2025.104098","DOIUrl":"10.1016/j.tre.2025.104098","url":null,"abstract":"<div><div>Hybrid electric vehicles (HEVs) are perceived as transitional products bridging the gap between fueled vehicles and electric vehicles (EVs) because people intuitively believe that EVs are more environmentally friendly than HEVs. But is this perception true in the context of carsharing services (CSSs)? This paper pioneers a general large-scale multi-objective simulation–optimization (MOSO) method to explore the values of deploying HEVs in CSSs. We firstly develop a physically logical simulation model, emulating operations of CSSs and capturing mesoscopic dynamics of shared vehicles in a link-based traffic network. This model adopts an event-driven discrete-event mechanism, enhancing efficiency while maintaining high fidelity. Subsequently, we design a simulation–optimization framework aimed at achieving Pareto optimality by jointly optimizing station capacity, fleet size, and trip pricing. The goal is twofold: to maximize operational profits and to minimize carbon emissions, thereby quantitatively analyzing the potential of shared HEVs (SHEVs). To tackle the high-dimensional MOSO problem, we introduce the multi-objective optimization into stochastic approximation field by proposing a general algorithm that incorporates the multiple gradient descent algorithm with the simultaneous perturbation stochastic approximation algorithm. Furthermore, we derive its analytical expression for bi-objective optimization problems. We theoretically prove and practically demonstrate its strong global convergence. The efficiency of this method was validated through large-scale computational experiments conducted in Chengdu, Sichuan Province, involving 66,710 decision variables. These experiments showcased the method’s superiority over existing MOSO algorithms. Several groups of sensitivity experiments focusing on vehicle types and traffic scenarios reveal some interesting findings. (1) Regardless of the increase in travel distances, SHEVs, which can be viewed as shared EVs (SEVs) without range anxiety (RA), continue to primarily rely on electricity rather than fuel for their operational mileages. This high utilization of electricity results in lower carbon emissions compared to SEVs. (2) Under any traffic condition, the dual-engine feature of SHEVs significantly reduces the number of failed pickups. (3) As travel demand increases, the state of charge for SEVs may rapidly fall below the threshold that triggers RA, whereas SHEVs maintain a more reliable power supply.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104098"},"PeriodicalIF":8.3,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777071","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":"Navigating the subsidy path to decarbonization under uncertainties","authors":"Jing Xia ,&nbsp;Yuxin Li ,&nbsp;Wenju Niu ,&nbsp;Weili Xue ,&nbsp;Lianmin Zhang","doi":"10.1016/j.tre.2025.104101","DOIUrl":"10.1016/j.tre.2025.104101","url":null,"abstract":"<div><div>The ongoing debate about the carbon subsidy regime focuses primarily on identifying the appropriate intended recipients and clarifying its role in sustainable operations. This paper assesses the effectiveness of the carbon subsidy regime by examining its impact on decarbonization incentives, profitability, sustainability, and welfare amidst uncertainties in innovation and demand. For that purpose, we develop models for cases where the government implements either the supply-side or the demand-side subsidy regime, as well as the benchmark model without any subsidy. Analysis of the equilibrium outcomes shows that subsidizing the firm (consumers) can effectively incentivize decarbonization innovation when the environmental coefficient is high (low). A key finding is that the supply-side subsidy regime could yield a win–win–win–win outcome for the government, the firm, consumers, and the environment; however, the demand-side subsidy regime may undermine sustainability despite enhancing profitability and welfare. Moreover, the government tends to subsidize consumers as the probability of successful decarbonization increases, whereas heightened demand uncertainty or consumers’ low-carbon preferences will prompt the government to subsidize the firm instead. We extend the model to the bilateral subsidy scenario, demonstrating that this regime may not guarantee a Pareto improvement because, in some cases, it reduces both profitability and sustainability. Notably, our main findings remain robust when: (i) The government has a finite subsidy budget, (ii) the government subsidizes the firm’s production cost instead of the investment cost, (iii) the probability of successful decarbonization is dependent on the firm’s investment efforts, or (iv) green and traditional products coexist in the market cannibalizing each other’s sales.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104101"},"PeriodicalIF":8.3,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777070","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":"Disturbance impact of rainfall on train travel time in China’s high-speed railway network under different spatial–temporal scenarios","authors":"Feng Xue ,&nbsp;Yu Zeng ,&nbsp;Jielin Liang ,&nbsp;Xiaochen Ma ,&nbsp;Yongji Luo","doi":"10.1016/j.tre.2025.104102","DOIUrl":"10.1016/j.tre.2025.104102","url":null,"abstract":"<div><div>Severe rainfall often affects train travel times in a high-speed railway network through speed restrictions and facility failures. The disturbance impact reduces the travel experience for passengers and creates enormous difficulties for train reception and departure at stations. This study presents a method for analysing the disturbance impacts of severe rainfall on train travel time in high-speed railway networks, focusing on the heterogeneity of impacts under different spatial–temporal rainfall scenarios. The average delay of trains at each study station under rainfall conditions is obtained by utilising a Markov chain Monte Carlo method based on kernel density estimation. Taking China’s high-speed railway as an example, we calculate the extent of delays and fluctuations in train travel time for different spatial–temporal rainfall scenarios and identify corresponding critical stations, lines, and rainfall periods. The results show that rainfall in the eastern study area has the greatest disturbance impact on train travel time, causing the extent of train delays and travel time fluctuations averaging 17.64 min/1000 km and 7.18 %. Temporally, the impacts caused by rainfall occurring from 6:00 to 9:00 in the eastern study area are more significant, while rainfall from 12:00 to 15:00 has lesser impacts. Meanwhile, when rainfall occurs from 15:00 to 18:00 in the eastern study area, greater attention should be given to the Ningbo station and the Nanjing-Shanghai railway section. The uneven distribution of train flows and their operational characteristics are the main reasons that impacts have spatial–temporal differences. The study conclusions can provide a reference basis for operation managers to adjust train operation schedules under real rainfall scenarios.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104102"},"PeriodicalIF":8.3,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777072","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":"A rich model for the tramp ship routing and scheduling problem—Solved through column generation","authors":"Alberto Tamburini ,&nbsp;Nina Lange ,&nbsp;David Pisinger","doi":"10.1016/j.tre.2025.104019","DOIUrl":"10.1016/j.tre.2025.104019","url":null,"abstract":"<div><div>We consider the <em>Tramp Ship Routing and Scheduling Problem</em> (TSRSP) in which we plan routes for a fleet of tramp shipping vessels operating on a combined contract and spot market. Earlier research has been fragmented due to variations in the side constraints studied. Hence we present the first unified model that can handle speed optimization, chartering costs, bunker planning, and hull cleaning. The model is solved by column generation, where the columns represent the possible routes of a vessel, while the master problem keeps track of the binding constraints. The pricing problem is solved efficiently using a time–space graph and several dominance rules. Real-life instances with up to 40 vessels, 35 geographic regions, and four months planning horizon can be solved to optimality in less than half an hour. The optimized routes increase earnings by 7% compared to historical schedules. Furthermore, policy-makers can use the model as a simulation of a rational agent behavior.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104019"},"PeriodicalIF":8.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of strategic performance measures on performance: The role of artificial intelligence and machine learning","authors":"Vipul Garg ,&nbsp;Janeth Gabaldon ,&nbsp;Suman Niranjan ,&nbsp;Timothy G. Hawkins","doi":"10.1016/j.tre.2025.104073","DOIUrl":"10.1016/j.tre.2025.104073","url":null,"abstract":"<div><div>This study highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on business logistics and operations, driving efficiency and strategic decision-making. With the logistics AI market anticipated to reach USD 31.58 billion by 2028, AI and ML’s role in enhancing operational performance and reducing costs is evident. Despite this, the application of AI and ML in improving strategic performance measures—namely Information Sharing, Decision Synchronization, and Logistics Efficiency—and their influence on firm and operational performance remains underexplored.</div><div>This research bridges this gap by leveraging the Dynamic Capabilities View to explore how AI and ML technologies influence the relationship between strategic performance indicators and both firm and operational performance. Utilizing a multi-method analysis, including PLS-SEM and fuzzy-set Qualitative Comparative Analysis, we explore the complex dynamics between strategic performance outcomes and the integration of AI and ML technologies. Our findings from PLS-SEM indicate that AI and ML significantly influence Firm Performance but not Operational Performance. Further analysis highlights that logistics efficiency, integrated with AI and ML, can enhance firm performance, showcasing AI and ML as critical components of firm success.</div><div>This study contributes to the fields of information systems and supply chain management by offering an innovative perspective on how AI and ML can empower firms, particularly within the United States and Canadian Business to Business and Business to Government sectors, to improve their firm and operational performance. It provides a strategic framework for managers to leverage these technologies effectively, enriching both theory and practice.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104073"},"PeriodicalIF":8.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758985","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":"Distributionally robust production and pricing for risk-averse contract-farming supply chains with uncertain demand and yield","authors":"Guomin Xing ,&nbsp;Yuanguang Zhong ,&nbsp;Yong-Wu Zhou ,&nbsp;Bin Cao","doi":"10.1016/j.tre.2025.104074","DOIUrl":"10.1016/j.tre.2025.104074","url":null,"abstract":"<div><div>Contract farming is a common and emerging practice in agricultural supply chains in both developing and developed countries, yet it has not received much attention in prior studies. This paper addresses this gap by examining a risk- and ambiguity-averse contract farming supply chain consisting of a risk-neutral agribusiness manufacturer and a risk-averse smallholder farmer, under conditions of demand and yield randomness with limited distributional information. A distributionally robust Stackelberg game model is developed to tackle this challenging manufacturer-farmer ambiguity problem. The model enables us to determine the risk-averse farmer’s robust production quantity under the conditional value-at-risk criterion, as well as the agribusiness manufacturer’s robust procurement price, which follows a simple threshold policy. Our analysis reveals that there exists a threshold level of risk aversion beyond which the farmer would decline the contract and cease production altogether. Furthermore, we find that the farmer’s risk-averse behavior adversely impacts both parties, even in the worst-case scenario, resulting in lower profits and reduced production quantities for both. Surprisingly, our comparative analysis shows that higher demand variability benefits the risk-averse farmer, creating a win-lose outcome for the farmer and the manufacturer. In contrast, higher yield uncertainty leads to a lose-lose outcome for both parties.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104074"},"PeriodicalIF":8.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739941","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":"Optimizing multinational manufacturing supply chains under diverse carbon policies using carbon gaming","authors":"Yifan Xin ,&nbsp;Ismail M. Ali ,&nbsp;Yangyan Shi ,&nbsp;Daryl L. Essam ,&nbsp;Ripon K. Chakrabortty","doi":"10.1016/j.tre.2025.104091","DOIUrl":"10.1016/j.tre.2025.104091","url":null,"abstract":"<div><div>In response to the environmental impact of human activities, governments worldwide have introduced various carbon policies. However, decision-making for multinational companies with different carbon policies has become a challenge for global supply chains (GSCs). To address the gap in the literature on the equitable allocation of carbon allowances and the operational management of GSCs under heterogeneous regulations, we propose a two-stage framework. First, we introduce the “Carbon Game”—a multiplayer Nash framework with a bi-level nested structure, where asymmetric manufacturers strategically determine their pricing, production, and carbon allowance allocation under diverse carbon taxes and subsidies. We then embed these equilibrium strategies into a multi-objective mixed integer linear programming (MILP) model to optimize GSC decisions, including fleet composition and network configuration. By integrating game-theoretic principles with multi-objective optimization, our framework provides new management insights. Numerical experiments show that carbon-efficient manufacturers should prioritize green technology investments to consolidate their advantage and use carbon labelling to capture environmentally conscious markets, while carbon-inefficient manufacturers should focus on cost-saving strategies, delaying green investments under stringent policies such as high taxes or low subsidies. Carbon-efficient manufacturers are also more responsive to policy changes. From a policy perspective, while both carbon taxes and subsidies generally incentivize green technology adoption, subsidies prove more effective and result in greater emission reductions. Across all tested scenarios, our method achieves a 15.07% profit increase and a 1.77% emission reduction compared to the Grandfathering approach. These findings inform multinational firms’ competitive strategies and help policymakers balance subsidies and taxes.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104091"},"PeriodicalIF":8.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven optimization for drone delivery service planning with online demand","authors":"Aditya Paul ,&nbsp;Michael W. Levin ,&nbsp;S. Travis Waller ,&nbsp;David Rey","doi":"10.1016/j.tre.2025.104095","DOIUrl":"10.1016/j.tre.2025.104095","url":null,"abstract":"<div><div>In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics service providers are constantly grappling with the challenge of uncertain real-time demand. This study investigates the problem of planning drone delivery service through an urban air traffic network to fulfill dynamic and stochastic demand. Customer requests – if accepted – generate profit and are serviced by individual drone flights as per request origins, destinations and time windows. We cast this stochastic optimization problem as a Markov decision process. We present a novel data-driven optimization approach which generates predictive prescriptions of parameters of a surrogate optimization formulation. Our solution method consists of synthesizing training data via lookahead simulations to train a supervised machine learning model for predicting relative link priority based on the state of the network. This knowledge is then leveraged to selectively create weighted reserve capacity in the network and via a surrogate objective function that controls the trade-off between reserve capacity and profit maximization to maximize the cumulative profit earned. Using numerical experiments based on benchmarking transportation networks, the resulting data-driven optimization policy is shown to outperform a myopic policy. Sensitivity analyses on learning parameters reveal insights into the design of efficient policies for drone delivery service planning with online demand.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104095"},"PeriodicalIF":8.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving green urban mobility: A study on shared electric vehicles versus taxis","authors":"Song-Man Wu ,&nbsp;Qianqian Wang ,&nbsp;Sai-Ho Chung ,&nbsp;Li Hu ,&nbsp;Yui-yip Lau ,&nbsp;Shi Qiang Liu","doi":"10.1016/j.tre.2025.104094","DOIUrl":"10.1016/j.tre.2025.104094","url":null,"abstract":"<div><div>Future cities prioritize green development to combat climate change, focusing on reducing energy consumption, exhaust emissions, and traffic congestion. Shared electric vehicles offer eco-friendly transportation, improving vehicle utilization, reducing resource waste, and mitigating environmental pollution through sharing. Despite these benefits, range anxiety and inconvenience hinder widespread adoption, with many still opting for taxis. Currently, most taxis worldwide rely on traditional fuel, leading to high fuel consumption and unnecessary carbon emissions and waste as drivers frequently search for customers on the roads. Different from Shared Charging Electric Vehicles (SCEVs) in the current market, this paper proposes Shared Battery-swapping Electric Vehicles (SBEVs), integrating battery-swapping technology into shared electric vehicles. This innovation aims to enhance convenience, attracting users to eco-friendly transportation and reducing reliance on traditional fuel taxis. Hence by constructing two business models of the current competitive market (Model 1) and the future competitive market (Model 2), this paper analyzes consumer preferences for taxis and SCEVs in Model 1, as well as explores the conditions for encouraging more consumers to choose SBEVs over taxis by improving the convenience of SBEVs in Model 2. The optimal operational decisions of supply chain participants in both markets are obtained, including the car-sharing operator determining the optimal SCEV usage price in Model 1, and the optimal SBEV usage price and the optimal efforts to enhance the convenience degree of SBEVs by installing battery swapping infrastructure in Model 2. This study provides valuable insights for driving green practices and operational improvements in the shared electric vehicle sector.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104094"},"PeriodicalIF":8.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734907","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}