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

{"title":"The impact of national centralized drug procurement policy on firm performance: Evidence from Chinese pharmaceutical firms","authors":"Huan He ,&nbsp;Yuanzhu Zhan ,&nbsp;Baofeng Huo ,&nbsp;Yufeng Zhang ,&nbsp;Xiaojie Shi","doi":"10.1016/j.tre.2025.104225","DOIUrl":"10.1016/j.tre.2025.104225","url":null,"abstract":"<div><div>China’s national centralized drug procurement policy, as a form of group purchasing, has successfully improved drug affordability and accessibility through significant price reductions. While prior studies have examined the long-term policy effects on pharmaceutical firms, critical gaps exist in understanding the immediate financial consequences for pharmaceutical firms, and how firm characteristics, supply chain factor, and product market factor moderate the relationship. To address this gap, we leverage data from the China Stock Market and Accounting Research (CSMAR) database and the Cninfo and utilize an event study methodology to analyze the policy impact on the financial performance of 205 Chinese pharmaceutical firms in the stock market. The results show that the policy shift led to a significant decrease in firm value by −2.67% on the day of the event, equating to approximately $661 million. Moreover, firms exhibiting higher innovation or sales intensity tend to experience a more pronounced negative impact, while those with a more concentrated supply chain are less vulnerable to regulatory changes. However, the positive role of product internationalization is not significant. The post-hoc analysis reveals that both state-owned and non-state-owned firms experienced notable declines in stock values and this adverse effect has no significant difference between the two kinds of firms. Moreover, the long-term effect analysis indicates that this negative effect can last for one year in the stock market. Our findings shed light on both firm managers and policymakers in developing regulatory adaptation strategies and responding to the impact of this policy shift.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104225"},"PeriodicalIF":8.3,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178594","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":"Data-driven predictive model for dynamic expected travel time estimation in rail freight networks: A case study","authors":"Suraj Kumar ,&nbsp;Ayush Sharma ,&nbsp;Gaurav Kumar","doi":"10.1016/j.tre.2025.104201","DOIUrl":"10.1016/j.tre.2025.104201","url":null,"abstract":"<div><div>Rail freight is vital for economic growth due to its efficiency and environmental benefits, but its lack of fixed schedules due to various delay factors poses challenges for accurate Expected Travel Time (ETT) predictions. This research addresses the need for real-time, accurate and dynamic ETT predictions crucial for maintaining efficient supply chains by developing a novel predictive model that leverages real-time data. The model ensembles Graph Convolutional Network-Long Short-Term Memory (GCN-LSTM) and Kalman Filters (KF) models to capture the complex spatiotemporal interactions and diverse traction behaviours within the freight train railway network. The methodology comprises three phases: modeling, schedule generation, and dynamic updating. In the modeling phase, historical train movement data is used to develop predictive models, with KF handling state-space representation and GCN-LSTM capturing spatial and temporal dependencies. These models are ensembled to enhance prediction accuracy. The schedule generation phase estimates travel times using the ensembled model, the dynamic updating phase refines predictions using real-time data, while congestion is assessed by clustering congested areas with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and propagating these clusters through KF. The proposed model is compared with different state-of-art predictive models. The methodology’s effectiveness was validated using real-world data from Indian Railway freight operations. The proposed model demonstrated superior accuracy, with Mean Absolute Percentage Error of 19.51%, while the moving average-based model which was previously being used by the Indian Railway had an error of 44.34%. This approach, implemented as a decision support system for Indian Railways’ daily operations, provides advanced planning solutions to manage the growing complexities of rail freight logistics effectively.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104201"},"PeriodicalIF":8.3,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166449","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":"Instant food on your table: The role of logistics service quality dimensions in the adoption of instant online food delivery services","authors":"Aditya Kumar Sahu ,&nbsp;Mohd Ziyauddin Khan ,&nbsp;Piyush Gupta","doi":"10.1016/j.tre.2025.104205","DOIUrl":"10.1016/j.tre.2025.104205","url":null,"abstract":"<div><div>The online food-ordering service industry experienced substantial growth due to the COVID-19 pandemic. Several online food delivery service (OFDS) providers enhanced their logistical capabilities and introduced instant<!--> <!-->delivery choices (Instant OFDS)<!--> <!-->to improve customer experiences. However, research concerning the logistical factors that influence customers’ adoption of Instant OFDS is scarce. Therefore, a sequential mixed-methodology approach was used to investigate the impact of various logistics service quality (LSQ) factors on the adoption of Instant OFDS. A qualitative study (Study 1) was conducted with 22 customers who had utilised Instant OFDS, the aim of which was to determine the primary LSQ factors that facilitate or impede the adoption of Instant OFDS. The qualitative analysis revealed that the prominent factors driving the adoption of Instant OFDS included timeliness, order discrepancy handling, and personnel contact quality. In contrast, the elements that discouraged customers included order condition, order release quantity, and order accuracy. These LSQ factors were further investigated to thoroughly comprehend the decision-making process that customers go through when selecting Instant OFDS via a quantitative investigation (Study II) employing behavioural reasoning theory (BRT) based on data from 407 customers. The BRT model, which consists of process beliefs, reasons for and against adopting Instant OFDS (LSQ factors), and attitude and intention towards adopting Instant OFDS, was tested using structural equation modelling. The moderating effects of price consciousness were also examined on the relationships between these variables. The results of the hypotheses testing indicated that customers’ process beliefs played a crucial role in influencing their reasoning (both in favour and against) and attitude towards adopting Instant OFDS. Whereas ‘reasons for’ factors influenced the customers’ intention, ‘reasons against’ factors did not influence attitude or intention. Moreover, no moderating effect of price consciousness was found on any of these relationships. The findings of this study highlight several key insights regarding the adoption of instant delivery choices. Thus, the present study provides significant contributions to the literature and practical implications for managers working in the OFDS sector.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104205"},"PeriodicalIF":8.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166527","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":"Flight scheduling and pricing with high-speed rail coopetition and delay uncertainty","authors":"Enoch Lee ,&nbsp;Yue Huai ,&nbsp;Hong K. Lo ,&nbsp;Anming Zhang","doi":"10.1016/j.tre.2025.104219","DOIUrl":"10.1016/j.tre.2025.104219","url":null,"abstract":"<div><div>This paper explores flight scheduling and pricing strategies for airlines under coopetition with high-speed rail (HSR) in intercity travel markets, considering the impact of potential delays and missed connections on passenger choices. A stochastic user equilibrium model is developed to account for the passenger route choices under travel time variability, security control delays, and competition with HSR and other airlines. The model investigates coopetition between airlines and HSR operators to enhance profitability. Additionally, to address the capacity constraint, the model incorporates overbooking costs, accounting for passenger no-shows, the probability of exceeding flight capacity, and compensation costs by the operator. The proposed solution method employs decomposition, a variable neighborhood search method, and linearization techniques to address probabilistic terms and equilibrium conditions. A numerical case study focusing on the Europe-China market, considering demand from both long-haul and short-haul travel, evaluates the impact of changes in HSR travel times and fares on passenger choices. Our results show that reduced HSR travel times shift passenger demand away from flights and lead flight operators to terminate some short-haul services, while increased HSR fares boost flight demand, particularly on long-haul routes. The study further illustrates the policy of reducing flights in response to a more severe competition due to shorter flight transit time. The optimal pricing strategy produces similar fares for the connecting itinerary through a local transfer hub and direct itinerary to the transfer hub.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104219"},"PeriodicalIF":8.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147278","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 three-stage matheuristic for the blood stochastic inventory routing problem","authors":"Vincent F. Yu ,&nbsp;Nabila Yuraisyah Salsabila ,&nbsp;Aldy Gunawan ,&nbsp;Nurhadi Siswanto","doi":"10.1016/j.tre.2025.104143","DOIUrl":"10.1016/j.tre.2025.104143","url":null,"abstract":"<div><div>This research introduces a blood distribution system under vendor-managed inventory that considers uncertain supply and demand. We present it as the Blood Stochastic Inventory Routing Problem, formulating it as a two-stage stochastic programming model. To solve this problem, this study proposes a three-stage matheuristic that combines a perturbation heuristic, Adaptive Large Neighborhood Search, and an exact approach. From historical data of Surabaya Blood Center in Indonesia, six sets of new instances are generated under different settings. Computational results show that our proposed three-stage matheuristic outperforms CPLEX and a two-stage matheuristic by gaining optimal or better solutions within a significantly shorter computational time. Moreover, it is robust for solving large problems, as evidenced by its ability to find high-quality solutions within a reasonable time. Finally, managerial insights are derived by evaluating performance matrices under different uncertainty levels and scenarios. According to these insights, some practical strategies are suggested with respect to the decision-maker’s risk preferences and demand characteristics.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104143"},"PeriodicalIF":8.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146707","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":"Gig work: A bibliometric systematic literature review and content analysis","authors":"Sihui Li ,&nbsp;Lanxia Zhang ,&nbsp;Shoufeng Hu ,&nbsp;Fu Jia ,&nbsp;Xiaolan Fu","doi":"10.1016/j.tre.2025.104216","DOIUrl":"10.1016/j.tre.2025.104216","url":null,"abstract":"<div><div>The internet has entered the 3.0 era, significantly boosting the development of the gig economy. Gig work, as a flexible form of employment, is increasingly recognized as a vital component of the modern economy. However, research on the gig economy remains in its early stages, highlighting the need for a comprehensive conceptual framework to define the boundaries of this field and to identify potential future research directions. This study utilizes a bibliometric systematic literature review (B-SLR) method to systematically examine 145 papers related to gig work, conducting a detailed content analysis of 88 selected studies out of the 145 papers. It outlines the current state and evolutionary trajectory of gig work research, identifying five key themes: what is gig work, the drivers and challenges of gig work, predominant research methods, the well-being of gig workers, and platform management and algorithms. Additionally, five future research directions are proposed. This study provides a clear roadmap for the advancement of the gig work field and serves as a valuable reference for scholars employing the B-SLR research method. Overall, our article offers a comprehensive overview of the development of the gig economy, synthesizing key themes from current research and proposing potential future directions based on rigorous scientific methods.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104216"},"PeriodicalIF":8.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138473","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":"Large language model-enhanced reinforcement learning for generic bus holding control strategies","authors":"Jiajie Yu,&nbsp;Yuhong Wang,&nbsp;Wei Ma","doi":"10.1016/j.tre.2025.104142","DOIUrl":"10.1016/j.tre.2025.104142","url":null,"abstract":"<div><div>Bus holding control is a widely-adopted strategy for maintaining stability and improving the operational efficiency of bus systems. Traditional model-based methods often face challenges with the low accuracy of bus state prediction and passenger demand estimation. In contrast, Reinforcement Learning (RL), as a data-driven approach, has demonstrated great potential in formulating bus holding strategies. RL determines the optimal control strategies in order to maximize the cumulative reward, which reflects the overall control goals. However, translating sparse and delayed control goals in real-world tasks into dense and real-time rewards for RL is challenging, normally requiring extensive manual trial-and-error. In view of this, this study introduces an automatic reward generation paradigm by leveraging the in-context learning and reasoning capabilities of Large Language Models (LLMs). This new paradigm, termed the LLM-enhanced RL, comprises several LLM-based modules: reward initializer, reward modifier, performance analyzer, and reward refiner. These modules cooperate to initialize and iteratively improve the reward function according to the feedback from training and test results for the specified RL-based task. Ineffective reward functions generated by the LLM are filtered out to ensure the stable evolution of the RL agents’ performance over iterations. To evaluate the feasibility of the proposed LLM-enhanced RL paradigm, it is applied to extensive bus holding control scenarios that vary in the number of bus lines, stops, and passenger demand. The results demonstrate the superiority, generalization capability, and robustness of the proposed paradigm compared to vanilla RL strategies, the LLM-based controller, physics-based feedback controllers, and optimization-based controllers. This study sheds light on the great potential of utilizing LLMs in various smart mobility applications.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104142"},"PeriodicalIF":8.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135096","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":"Scheduling intelligent charging robots for electric vehicle: A deep reinforcement learning approach","authors":"Yi Ding ,&nbsp;Ming Deng ,&nbsp;Ginger Y. Ke ,&nbsp;Yingjun Shen ,&nbsp;Lianmin Zhang","doi":"10.1016/j.tre.2025.104090","DOIUrl":"10.1016/j.tre.2025.104090","url":null,"abstract":"<div><div>The surge in popularity of electric vehicles (EVs) has created a need for adaptable and flexible charging infrastructure. Intelligent Charging Robots (ICRs) have emerged as a promising solution to overcome issues faced by fixed charging stations, such as insufficient coverage, station occupancy, spatial constraints, and strain on the power grid. Nonetheless, optimizing the operational efficiency of ICRs presents a significant challenge. This study focuses on optimizing the scheduling of ICRs in a public parking facility through Deep Reinforcement Learning (DRL) methods. We first introduce the Intelligent Charging Robots Scheduling Problem (ICRSP) that maximizes either the number of EVs served (MN) or the total output electricity of ICRs (ME), and establish the corresponding mathematical model. Then, a DRL framework based on the Transformer structure is proposed to tackle ICRSP by integrating decisions of ICR assignment and EV sequencing to enhance solution quality. Furthermore, we devise a masking mechanism in the decoder to manage ICRs’ self-charging behavior during the charging service. Finally, experimental results validate the effectiveness of the proposed DRL approach in providing efficient scheduling solutions for large-scale ICRSP instances. The comparative analysis of MN-ICRSP and ME-ICRSP models offers valuable insights for ICRs operation scheduling, aiding in balancing operator revenue and customer satisfaction.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104090"},"PeriodicalIF":8.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135083","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":"Retailer hoarding in emergency situations: A game-theoretic analysis","authors":"Jie Xiang ,&nbsp;Xiaozhou He ,&nbsp;T.C.E. Cheng","doi":"10.1016/j.tre.2025.104187","DOIUrl":"10.1016/j.tre.2025.104187","url":null,"abstract":"<div><div>Despite current policies, retailer hoarding continues to occur frequently during specific emergency situations, highlighting the significance of understanding the underlying mechanism. This study employs Stackelberg game models within a two-tier supply chain consisting of a supplier and a retailer to analyse their decisions during persistent emergencies. The models incorporate the interactions between retailer hoarding, consumer panic buying, and incomplete information regarding future conditions. The equilibrium solutions of the models yield the following findings: First, the retailer’s profit-driven hoarding can not only stem from the anticipation of high future prices, but may also be a consequence of price gouging through withholding goods and intensifying consumer panic at present, while insufficient supply does not necessarily imply this behaviour. Second, even with sufficient supply, consumer panic buying may also prompt the retailer to engage in price gouging by reducing the order quantity, which in turn amplifies consumer panic and exacerbates the challenges within the supply chain. Finally, the negative impact of incomplete information in the supply chain is emphasized. In such cases, both parties are advised to adopt conservative estimates of the future price for profit, which, however, can lead to lower sales and impair consumers. These results enrich the theoretical knowledge of retailer hoarding and elucidate the drivers of this irrational and illegal behaviour. They also provide valuable insights, highlighting the importance for regulators to manage consumer panic buying to reduce retailer hoarding as well as promoting precise information disclosure during emergencies.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104187"},"PeriodicalIF":8.3,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130876","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":"Real-time scheduling optimization for autonomous public transport vehicles to meet booking demands","authors":"Zhichao Cao ,&nbsp;Avishai (Avi) Ceder ,&nbsp;Silin Zhang","doi":"10.1016/j.tre.2025.104202","DOIUrl":"10.1016/j.tre.2025.104202","url":null,"abstract":"<div><div>The booking service, a key feature of autonomous public transport vehicle (APTV) systems, has been designed to introduce a new, real-time, on-demand, and reliable element to service improvement, similar to ride-hailing. However, the current APTV system has yet to fully realize the potential of a smart public transport service in optimizing the balance between supply and demand. This study proposes a real-time, multi-objective programming model that aims to minimize three key factors: passenger waiting times, timetable deviations, and fleet size. Recognized as an NP-hard problem, the model is linearized to reduce computational complexity, with real-time demands tracked through a rolling horizon method. A predict-then-optimize approach is introduced to enable timely responses to new bookings. A customized two-phase algorithm incorporating three enhancements − valid cuts, Monte Carlo simulation, and neighborhood and local search − significantly improves solution efficiency. A case study in Auckland, New Zealand, evaluates the proposed approach. The findings reveal significant improvements in booking service performance, with two scenarios achieving a 35 % and 27 % reduction in passenger waiting time and a 13 % and 12 % decrease in fleet size compared to the current conventional bus line. These results were attained with minimal deviations from the original schedule, validating the effectiveness of the developed methodology.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"200 ","pages":"Article 104202"},"PeriodicalIF":8.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124206","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}