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

{"title":"Sustainable and reliable design of autonomous driving lanes: A chance-constrained extended goal programming approach","authors":"Yu Chen ,&nbsp;Wei Wang ,&nbsp;Xuedong Hua ,&nbsp;David Z.W. Wang ,&nbsp;Jian Wang","doi":"10.1016/j.tre.2025.103973","DOIUrl":"10.1016/j.tre.2025.103973","url":null,"abstract":"<div><div>As autonomous vehicles (AVs) advance and gradually integrate into urban roadways, managing traffic in mixed environments, where human-driven vehicles (HVs) coexist with AVs, has become a critical challenge. Previous research has developed several optimal network design models for autonomous driving lanes (ADLs) to partially address this issue. However, these studies largely overlook uncertainties in AV market share or travel demand, which may lead to either excessive investment in underutilized infrastructure or traffic inefficiencies with insufficient capacity. To address these challenges, this study introduces a chance constrained programming (CCP) approach with sample approximation to effectively incorporate such uncertainties while accommodating varying risk preferences. To harmonize various sustainable development goals, such as network efficiency and social equity, CCP is further combined with extended goal programming, forming the proposed chance-constrained extended goal programming (CCEGP) model in this study for sustainable and reliable ADL design. Additionally, the routing behaviors of HV and AV users, each with differing levels of traffic awareness, are unified within a mixed cross-nested logit-based stochastic user equilibrium model, thereby enhancing behavioral realism and model generalizability. The heuristic coati optimization algorithm is modified for solving, and case studies are conducted to validate its applicability.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"195 ","pages":"Article 103973"},"PeriodicalIF":8.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143302429","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":"Stochastic stay times for interrelated trips in the rural dial-a-ride problem","authors":"Lennart C. Johnsen ,&nbsp;Frank Meisel ,&nbsp;Jan F. Ehmke","doi":"10.1016/j.tre.2025.103968","DOIUrl":"10.1016/j.tre.2025.103968","url":null,"abstract":"<div><div>This paper presents a stochastic version of the dial-a-ride problem with interrelated trips. Interrelated trips refer to transportation requests where travelers need to arrive at meeting locations simultaneously or where round trips involve a specific amount of time spent at destination locations, such as for medical consultations. In this variant of the problem, the durations of the travelers’ stays are considered to be stochastic. Traveler lateness is incredibly challenging in such interrelated transportation schedules because delays can propagate across different vehicles. This is especially relevant for rural dial-a-ride systems, where travelers are restricted to a small choice of transportation services. A purposeful decision making is therefore required to orchestrate the service operations of such vehicle fleets. Hence, we look at smart ways how to enhance the reliability and attractiveness of these systems. Our approach involves a careful examination of how to approximate the distributions of the arrival and service start times of the vehicles at each customer location. To create more reliable schedules, we utilize a chance constraint and incorporate it together with enhanced feasibility checks into an Adaptive Variable Neighborhood Search metaheuristic. The obtained solutions are evaluated in a simulation environment. Through computational experiments, we explore the balance between operational costs and service reliability, as well as the effects of various service policies for managing delayed travelers (e.g., wait or go at meeting requests) on punctuality at subsequent locations.’</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"195 ","pages":"Article 103968"},"PeriodicalIF":8.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143302430","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":"Should an electric vehicle manufacturer buy its own ship? Investment and pricing strategies under uncertainty","authors":"Yanyan Ding,&nbsp;Dong Yang","doi":"10.1016/j.tre.2025.103990","DOIUrl":"10.1016/j.tre.2025.103990","url":null,"abstract":"<div><div>Rising EV exports and regional instability have increased shipping costs. Many electric vehicle manufacturers (EVMs) purchase and operate newly built pure car and truck carriers (PCTC), which are greener and more energy-efficient than traditional car carriers, to lower shipping costs and improve global supply-chain resilience. Meanwhile, EVMs strategically adjust EV prices and production quantities in response to demand shocks caused by unexpected tariffs and economic crises in end markets. To address these challenges, a two-stage decision-making model is proposed to optimize the investing and operating strategies for the EVM. In this model, the EVM first decides whether to purchase PCTC and the ship size, and then decides the optimal EV price and production quantity facing demand uncertainty. Theoretical analysis indicates that the EVM should utilize self-operated shipping under stochastic or linear deterministic demand to enhance cost efficiency while relying on other shipowners under non-linear deterministic demand. Furthermore, the EVM should increase the EV price under the additive stochastic demand and decrease the EV price under the iso-elastic stochastic demand, compared to deterministic consumer demand. In numerical analysis, we consider an emerging EVM, NIO, that exports Chinese EVs to the European market. Numerical results suggest that the EVM should create an “artificial shortage” to maintain a tight market during increased shipping costs or rising demand uncertainty while fostering a loose market to serve all available consumers when demand uncertainty is low.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"195 ","pages":"Article 103990"},"PeriodicalIF":8.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143302701","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":"Operational synchromodal transport planning methodologies: Review and roadmap","authors":"Yimeng Zhang ,&nbsp;Xiangrong Tan ,&nbsp;Mi Gan ,&nbsp;Xiaobo Liu ,&nbsp;Bilge Atasoy","doi":"10.1016/j.tre.2024.103915","DOIUrl":"10.1016/j.tre.2024.103915","url":null,"abstract":"<div><div>This review aims to explore the potential for synchromodal transport planning at the operational level. Synchromodal transport planning involves the optimization of the movement of freights across multiple transport modes, with the objective of minimizing cost, improving efficiency, and promoting sustainability. Through this review, we provide a roadmap for methodological developments in the area of operational synchromodal transport planning research. The roadmap provides a comprehensive categorization of different fields and their trends. The fundamentals of synchromodal transport planning are evolved to more flexible planning approaches that take practical considerations and multiple objectives into account. Dynamic planning is evolving to become more adaptive and resilient to changing environments. Finally, collaborative planning will continue to integrate both vertical and horizontal collaboration with distributed optimization approaches. With dynamic and collaborative approaches considering preferences, the full potential of synchromodal transport planning can be unlocked towards efficient and sustainable freight transportation.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103915"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867659","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":"Optimal pricing and collection decisions in a two-period closed-loop supply chain considering channel inconvenience","authors":"Bocan Shu ,&nbsp;Jie Wei ,&nbsp;Hui Cao","doi":"10.1016/j.tre.2024.103869","DOIUrl":"10.1016/j.tre.2024.103869","url":null,"abstract":"<div><div>Improving recovery efficiency is a key concern for collectors in a closed-loop supply chain (CLSC) with remanufacturing, as customers often consider the inconvenience of recycling channels when returning used products. This issue profoundly affects collectors’ capacity to recover materials. In a two-period CLSC with remanufacturing, including a manufacturer and a retailer, we develop game-theoretical models in the centralized and decentralized scenarios and compare the optimal solutions, consumer surplus, social welfare and environmental impact of different models through analytical and numerical analysis. Our aim is to examine firms’ dynamic pricing strategies and collection investment decisions by considering customers’ perception of channel inconvenience. There are four main findings. Firstly, in the centralized model and the retailer collection model, the decision-maker lowers the retail price in the first period. However, in the competitive collection model, the manufacturer and the retailer raise the wholesale and retail prices in the first period, respectively. Secondly, in the retailer collection model, as the recycling revenue increases, the manufacturer, although not involved in collecting, the profit also increases due to the free-rider behavior. Thirdly, in the competitive collection model, when the remanufacturing cost savings is relatively high, the collection investment of the manufacturer is much larger than that of the retailer, resulting in the retailer failing to collect any product and giving up collecting. Finally, the collection competition improves the total collection rate and environmental performance but reduces the profit of the manufacturer and the retailer, as well as the consumer surplus and social welfare. Therefore, we design a two-part tariff contract to coordinate the decentralized model and effectively improve the performance of the supply chain.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103869"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788861","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 multi-task deep reinforcement learning approach to real-time railway train rescheduling","authors":"Tao Tang ,&nbsp;Simin Chai ,&nbsp;Wei Wu ,&nbsp;Jiateng Yin ,&nbsp;Andrea D’Ariano","doi":"10.1016/j.tre.2024.103900","DOIUrl":"10.1016/j.tre.2024.103900","url":null,"abstract":"<div><div>In high-speed railway systems, unexpected disruptions can result in delays of trains, significantly affecting the quality of service for passengers. Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the face of such unforeseen disruptions. Most existing studies on TTR are based on integer programming (IP) techniques and are required to solve IP models repetitively in case of disruptions, which however may be very time-consuming and greatly limit their usefulness in practice. Our study first proposes a multi-task deep reinforcement learning (MDRL) approach for TTR. Our MDRL is constructed and trained offline with a large number of historical disruptive events, enabling to generate TTR decisions in real-time for different disruption cases. Specifically, we transform the TTR problem into a Markov decision process considering the retiming and rerouting of trains. Then, we construct the MDRL framework with the definition of state, action, transition, reward, and value function approximations with neural networks for each agent (i.e., rail train), by considering the information of different disruption events as tasks. To overcome the low training efficiency and huge memory usage in the training of MDRL, given a large number of disruptive events in the historical data, we develop a new and high-efficient training method based on a Quadratic assignment programming (QAP) model and a Frank-Wolfe-based algorithm. Our QAP model optimizes only a small number but most “representative” tasks from the historical data, while the Frank-Wolfe-based algorithm approximates the nonlinear terms in the value function of MDRL and updates the model parameters among different training tasks concurrently. Finally, based on the real-world data from the Beijing–Zhangjiakou high-speed railway systems, we evaluate the performance of our MDRL approach by benchmarking it against state-of-the-art approaches in the literature. Our computational results demonstrate that an offline-trained MDRL is able to generate near-optimal TTR solutions in real-time against different disruption scenarios, and it evidently outperforms state-of-art models regarding solution quality and computational time.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103900"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816530","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":"Online configuration of reservable parking spaces: An agent-based deep reinforcement learning approach","authors":"Minghui Xie ,&nbsp;Siyu Lin ,&nbsp;Sen Wei ,&nbsp;Xinying Zhang ,&nbsp;Yao Wang ,&nbsp;Yuanqing Wang","doi":"10.1016/j.tre.2024.103887","DOIUrl":"10.1016/j.tre.2024.103887","url":null,"abstract":"<div><div>Unevenly distributed parking demand frequently leads to the overconsumption of popular parking lots, resulting in increased regional travel costs and traffic congestion. Configuring reservable parking spaces in parking lots based on online reservation systems is a prevalent solution to alleviate these issues. However, existing static configuration methods are inadequate for addressing time-varying parking demand, presenting significant challenges in determining the optimal number of reservable parking spaces across different parking lots over time. Thus, to address these challenges and reduce the total travel time in popular reservation-enabled management areas, this paper proposes a dynamic configuration model for reservable parking spaces utilizing agent-based deep reinforcement learning. The model can dynamically schedule the ratio of reservable parking spaces in an environment where reserved users and non-reserved users coexist, thereby influencing parking users’ choice behavior and balancing demand distribution. Experimental results on a real-world simulator show that, compared to baseline methods, the proposed model can effectively configure reservable parking spaces online. It conservatively reduces the total travel time by 21.4% and alleviates parking cruising and waiting in the management area. This approach is prospective for smart parking management.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103887"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867657","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":"Dynamic hub capacity planning in hyperconnected relay transportation networks under uncertainty","authors":"Xiaoyue Liu ,&nbsp;Jingze Li ,&nbsp;Mathieu Dahan ,&nbsp;Benoit Montreuil","doi":"10.1016/j.tre.2024.103940","DOIUrl":"10.1016/j.tre.2024.103940","url":null,"abstract":"<div><div>In this article, we consider a truck carrier aiming to set contracts with multiple hub providers to reserve hub capacities in a hyperconnected relay transportation network. This network enables long-haul freight shipments to be transported by multiple short-haul drivers commuting between fixed-base hubs, promoting a driver-friendly approach. We introduce the dynamic stochastic hub capacity-routing problem (DS-HCRP), which is a two-stage stochastic program to determine hub contracted capacities for each planning period that minimizes hub and subsequent transportation costs given demand and travel time uncertainty. To overcome the difficulty in solving this NP-hard problem, we propose a combinatorial Benders decomposition (CBD) algorithm based on a tailored implementation of branch-and-Benders-cut. In addition, we design a heuristic initial cut pool generation method to restrict the search space within the CBD algorithm. Experimental results from a case study in the automotive delivery sector demonstrate that our algorithm outperforms other commonly used approaches in terms of solution quality and convergence speed. Furthermore, the results show that the proposed model offers potential savings of up to 22.96% in hub costs and 8.47% in total costs compared to its static deterministic counterpart by effectively mitigating the impact of demand fluctuations and network disruptions, thus highlighting the advantages of dynamic and stochastic integration in capacity planning.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103940"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887898","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 two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times","authors":"Yuanhan Wei ,&nbsp;Yong Wang ,&nbsp;Xiangpei Hu","doi":"10.1016/j.tre.2024.103954","DOIUrl":"10.1016/j.tre.2024.103954","url":null,"abstract":"<div><div>With the rapid expansion of e-commerce and the resulting surge in parcel delivery demands, the integration of trucks and unmanned ground vehicles (UGVs) in last-mile package delivery provides a more efficient and sustainable venue for a logistics system. However, coordinating trucks and UGVs in the context of fluctuating traffic conditions, especially with varying travel times, continues to be a significant challenge. This study addresses this issue by proposing and solving a two-echelon truck-UGV routing problem with time-dependent travel times. The first echelon encompasses transporting goods from the warehouse to satellites using trucks, considering time-dependent travel times. The second echelon involves distributing goods from satellites to customers using UGVs. Initially, a continuous-time time-dependent travel model is proposed based on the fluid queueing model to estimate vehicle travel times under varying traffic conditions. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs and the number of UGVs used. Subsequently, a novel hybrid algorithm combining an improved three dimension <em>k</em>-nearest neighbor clustering algorithm with an improved multiobjective adaptive large neighborhood search method is developed to solve the model. This algorithm incorporates the adaptive score adjustment and Pareto solution selection strategies to enhance algorithm convergence and evaluate solution quality. The acceptance criterion for new solutions is redesigned based on multiobjective function values to explore the search space more thoroughly. Additionally, the algorithm’s computational performance is verified by comparing it with the CPLEX solver for small-scale problems and with multiobjective ant colony optimization, multiobjective evolutionary algorithms, multiobjective particle swarm optimization, multiobjective monarch butterfly optimization, and multiobjective harmony search algorithms for medium-to-large problems. The results demonstrate the superior convergence, uniformity, and spread of the proposed algorithm. Furthermore, a real-world case study employing traffic information of Dalian city, China, supports that the proposed method enhances the efficiency of delivery. Four different time-dependent travel times model are proposed to analyze the outperformance of the time-dependent travel model in this study. Finally, the sensitivity analysis considers different road congestion states and UGV capacities, aiming to reduce transportation costs, and overcome high coordination and congestion costs in the network. This study offers robust methodologies for theoretically and practically addressing the two-echelon truck-UGV routing problem with time-dependent travel times, providing essential insights for promoting development, enhancing smart city integration, and boosting operational efficiency.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103954"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939698","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":"Selection of R&D techniques: The influence of spillover effects and government subsidies","authors":"Kehong Chen ,&nbsp;Yiming Fan","doi":"10.1016/j.tre.2024.103879","DOIUrl":"10.1016/j.tre.2024.103879","url":null,"abstract":"<div><div>This study examines the research and development (R&amp;D) investment strategies of two competing logistics firms under the influence of spillover effects and government subsidies. Firms must decide whether to invest in similar or distinct R&amp;D techniques, or to forgo R&amp;D entirely. Spillover effects occur only when firms adopt different R&amp;D techniques, including cases where one firm chooses not to invest in R&amp;D while the other does. Our findings show that high spillover effects discourage firms from investing in R&amp;D, while low spillover effects induce firms to choose the same R&amp;D techniques. However, social welfare cannot be maximized under the equilibrium state established by free competition among firms. Subsequently, we investigate the impact of government subsidies on firms’ operational decisions, finding that firms choose different R&amp;D techniques when spillover effects are low and R&amp;D costs are high. Notably, government subsidies can partially rectify the misalignment between the Nash equilibrium and maximization of social welfare. This implies that, under certain conditions, government intervention can achieve a dual optimization of firm profits and social welfare. This is crucial for supply chain management, as it ensures both logistics efficiency and competitive pricing, ultimately benefiting the entire supply chain system.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103879"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788852","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}