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

{"title":"Cold chain routing for product freshness and low carbon emissions: A target-oriented robust optimization approach","authors":"Yi Ding ,&nbsp;Linjing Zhang ,&nbsp;Yong-Hong Kuo ,&nbsp;Lianmin Zhang","doi":"10.1016/j.tre.2025.104138","DOIUrl":"10.1016/j.tre.2025.104138","url":null,"abstract":"<div><div>As consumer demand for fresh products continues to rise, the inefficiencies in cold chain logistics have emerged as a pressing issue, resulting in substantial food waste and compromised product quality. Meanwhile, logistics companies face the dual challenge of reducing costs and carbon emissions while ensuring product freshness. In response to these challenges, this paper proposes a novel target-oriented framework that leverages an underperformance riskiness index to optimize cold chain routing decisions. The primary objective is to minimize the risk of not meeting the target freshness level while accounting for costs and carbon emissions. To address the complexity that arises from stochastic arrival times, a linear decision rule is incorporated into the model. The robust counterpart of the problem is reformulated as a mixed-integer linear programming model, which is then solved efficiently using a Benders decomposition approach. Extensive computational experiments are conducted on realistic instances to evaluate the performance of our proposed approach. A comparative analysis with two benchmark models is also performed. The experimental results reveal that our target-oriented robust optimization framework generates high-quality solutions. It effectively reduces both the likelihood and magnitude of violations of the target freshness level, while maintaining relatively low costs and carbon emissions.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104138"},"PeriodicalIF":8.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878736","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":"Leveraging blockchain to optimize online strategies for remanufactured products with cannibalization","authors":"Chen Wang ,&nbsp;Yuting You ,&nbsp;Shufen Dai ,&nbsp;Jennifer Shang ,&nbsp;Wei Gu","doi":"10.1016/j.tre.2025.104149","DOIUrl":"10.1016/j.tre.2025.104149","url":null,"abstract":"<div><div>Remanufacturing faces significant challenges including negative consumer perception and the complex dynamics of cannibalization between new and remanufactured product sales. This paper explores the potential of blockchain technology to enhance consumer value perceptions of remanufactured products and presents a decision-making framework that examines both blockchain adoption strategies and online sales channel strategies. Through theoretical analysis using game theory models and numerical analysis of real-world data, the study analyzes the optimal channel strategies and blockchain implementation. The results indicate that a mixed strategy, where manufacturers and platforms collaborate, is highly helpful for promoting the remanufactured market and usually has greater profits. Additionally, an agency–agency strategy, where the manufacturer and platform both leverage blockchain, can help mitigate the cannibalization effects. However, we find that only when consumers’ initial environmental awareness reaches a certain threshold can blockchain effectively promote the remanufactured market. To demonstrate the practical implications, we apply the framework to the remanufactured products on the <span><span>JD.com</span><svg><path></path></svg></span> and Amazon platforms. The results show that by transitioning to a mixed strategy, the overall profits for the brands can increase. Furthermore, the application of blockchain strategy can lead to further increases in the sales volume of remanufactured products and overall profitability. The paper concludes with managerial insights for fostering sustainable remanufacturing operations through leveraging blockchain technology and optimizing online sales channel strategies.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104149"},"PeriodicalIF":8.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882781","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":"Modeling and analysis of the platoon size of Connected Autonomous Vehicles in a mixed traffic environment","authors":"Peilin Zhao,&nbsp;Yiik Diew Wong,&nbsp;Feng Zhu","doi":"10.1016/j.tre.2025.104130","DOIUrl":"10.1016/j.tre.2025.104130","url":null,"abstract":"<div><div>In a mixed traffic environment that consists of both Connected Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), the platoon sizes of CAVs play a significant role in traffic flow analysis. However, the statistical properties of these platoon sizes have not been thoroughly addressed in existing research. This study aims to fill this critical gap by modeling CAV platoon sizes as a random variable, analyzing scenarios both with and without a Maximum Platoon Size (MPS) constraint. Specifically, the frequencies and corresponding probability distributions of CAV platoon sizes under these conditions are derived. Furthermore, the distribution derivations are extended by incorporating platooning willingness. Through numerical analysis, the results reveal that the proposed probability distributions align closely with numerical observations, demonstrating the consistency and reliability of the model. The study also explores the characteristics of these distributions, as well as the effects of the MPS constraint and platooning willingness. By examining the platooning behaviors in mixed traffic and providing analytical derivations for CAV platoon size probability distributions, this research lays a robust mathematical foundation for further analysis of mixed traffic dynamics, enhancing traffic management and efficiency in increasingly automated traffic environments.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104130"},"PeriodicalIF":8.3,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874600","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":"Enhanced Benders decomposition approach for shared vacant private parking spaces allocation method considering uncertain parking duration of demanders","authors":"Yanping Jiang,&nbsp;Zhan Gao,&nbsp;Tingwen Zheng,&nbsp;Yan Zhang","doi":"10.1016/j.tre.2025.104150","DOIUrl":"10.1016/j.tre.2025.104150","url":null,"abstract":"<div><div>We study a shared vacant private parking spaces allocation problem that considers the uncertain parking duration of demanders. To solve the problem, we first formulate a stochastic programming model (P model). The objective is to maximize the weighted sum of the total expected profits from the platform parking revenue, overload cost and idle cost. On this basis, we reformulate the P model into the UPDA model based on the sample average approximation. Unlike the traditional construction of Benders cut using the dual problem, we construct a new Benders cut based on the lower bound of the subproblem, and then propose an efficient enhanced Benders decomposition (EBD) algorithm for solving the UPDA model. Finally, the performance of the algorithm is verified by numerical experiments. The experimental results show that the enhanced Benders decomposition algorithm outperforms both the Benders decomposition algorithm and commercial solver, and can effectively solve large-scale problems with high complexity. The experimental results also show that the uncertainty in the parking duration of the demander has negative impact on the system performance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104150"},"PeriodicalIF":8.3,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877562","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":"Firms’ strategic responses to rising uncertainty amid ongoing geopolitical tensions: The synergistic mediating role of network capability and innovation ambidexterity","authors":"Anas Iftikhar ,&nbsp;Imran Ali ,&nbsp;Yuanzhu Zhan ,&nbsp;Mark Stevenson ,&nbsp;Shlomo Y. Tarba","doi":"10.1016/j.tre.2025.104146","DOIUrl":"10.1016/j.tre.2025.104146","url":null,"abstract":"<div><div>With our study, we aimed to enrich the discourse on supply chain disruptions by exploring the strategic responses of firms to supply chain uncertainty (SCUn) that enhance supply chain resilience (SCRes). Drawing on the dynamic capabilities view (DCV), we investigated whether and how firms utilise uncertainty amid geopolitical turmoil as a catalyst to enhance SCRes. This contrasts with the predominant focus found in the existing literature on the detrimental impacts of uncertainty amid rising geopolitical tensions. Using survey data drawn from 242 firms across multiple industries in Pakistan, we employed structural equation modelling (SEM) to test our proposed model, introducing network capabilities (NCs) and innovation ambidexterity (IA) as mediators to elucidate their differential roles in the SCUn-SCRes relationship. Our findings reveal that SCUn triggers strategic responses aimed at building SCRes, with NCs emerging as a significant mediator that enhances SCRes. However, IA has an insignificant mediating effect. Notably, our study uncovers a sequential mediation pathway from NCs to IA, highlighting the dynamic interplay between these capabilities in translating SCUn into enhanced SCRes amid global crises. Our study provides actionable insights for logistics and supply chain managers who navigate uncertain environments amid geopolitical tensions, emphasizing the importance of NCs in driving IA towards achieving SCRes. Our research, which makes a novel contribution by going beyond the conventional perspectives on SCUn and SCRes, advances a new stream of literature on how SCUn influences SCRes through the mediating roles of NCs and IA.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104146"},"PeriodicalIF":8.3,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874599","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":"SPPformer: A transformer-based model with a sparse attention mechanism for comprehensive and interpretable ship price analysis","authors":"Wenyang Wang ,&nbsp;Yuping Luo ,&nbsp;Yuqiang Xu ,&nbsp;Danzhu Liu ,&nbsp;Jibin Zhou ,&nbsp;Peng Shao","doi":"10.1016/j.tre.2025.104136","DOIUrl":"10.1016/j.tre.2025.104136","url":null,"abstract":"<div><div>Accurate prediction of vessel prices across multiple ship types is crucial for providing scientific decision-making support to shipping enterprises, investment institutions, and policymakers. However, traditional statistical methods and machine learning models face significant limitations in accuracy, generalization, applicability, interpretability, and data coverage, rendering them inadequate for high-precision forecasting in the complex shipping market. Inspired by the successful application of Transformer-based models like ChatGPT across various domains, this study proposes a novel ship price prediction model based on the Transformer architecture—SPPformer. The model integrates architectural optimization, pre-training, and fine-tuning techniques to enable comprehensive price forecasting for multiple ship types. On the data front, this paper consolidated over one million data variables from 12 maritime-related domains to pre-train the model, forming a Basic Model with foundational time-series processing capabilities. Subsequently, domain-specific maritime data were incorporated through fine-tuning to develop the Sectional Model, enhancing its specialization in the shipping sector. For interpretability, the SHAP method was embedded into the SPPformer prediction framework to visualize the impact of feature variables on target variables during the forecasting process. In terms of efficiency, a sparse attention mechanism was introduced by combining Atrous Self-Attention and Local Self-Attention, replacing the global attention mechanism of traditional Transformer, thereby significantly improving training efficiency and solving overfitting issues. The empirical study focused on predicting newbuilding and secondhand vessel prices for dry bulk, container, tanker, and the overall shipping market. The results demonstrate that the SPPformer model outperforms traditional ones in accuracy and interpretability. At the same time, the introduction of sparse attention reduces training time and memory usage by 22.91 % and 26.12 %, respectively, compared to global attention mechanisms. This research provides essential references for shipping enterprises to enhance economic efficiency and for financial institutions to manage risks. It also offers data-driven support and analytical frameworks for governments to regulate market order and promote the stable development of the shipping industry.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104136"},"PeriodicalIF":8.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869169","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 day-ahead EV scheduling across multiple charging stations with an interrupted-charging scheme","authors":"Hyojin Kim ,&nbsp;Jongheon Lee ,&nbsp;Siyoung Lee","doi":"10.1016/j.tre.2025.104111","DOIUrl":"10.1016/j.tre.2025.104111","url":null,"abstract":"<div><div>Globally, the expansion of charging infrastructure has lagged behind the rapid adoption of electric vehicles (EVs), causing inconvenience to users due to limited charging station availability. To address this issue, this study proposes a day-ahead scheduling strategy for charging service providers (CSPs) managing multiple stations within their jurisdictions. The strategy optimally assigns charging demands to stations and establishes charging schedules while considering battery degradation and existing infrastructure through an interrupted-charging scheme. We formulate this problem as a mixed-integer programming model to minimize operational costs consisting of charging, shortage, and assignment costs. To enhance scalability, a decomposition method based on column generation is developed to obtain high-quality solutions efficiently. A case study inspired by the regional characteristics of South Korea validates the strategy’s effectiveness. Compared with benchmark strategies, the proposed approach achieves lower operational costs while maintaining high charging completion rates in both day-ahead scheduling and even in real-time operation where actual demand deviates from day-ahead estimates. Furthermore, the results demonstrated the effectiveness of the proposed decomposition method for large-scale instances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104111"},"PeriodicalIF":8.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864977","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":"Prescriptive analytics for freeway traffic state estimation by multi-source data fusion","authors":"Di Huang ,&nbsp;Jinyu Zhang ,&nbsp;Zhiyuan Liu ,&nbsp;Ronghui Liu","doi":"10.1016/j.tre.2025.104105","DOIUrl":"10.1016/j.tre.2025.104105","url":null,"abstract":"<div><div>In the context of freeway traffic state estimation, this study introduces prescriptive analytics—also known as “predict-then-optimize”—for integrating data from Electronic Toll Collection (ETC) systems and traffic sensors. Traditional single-method data fusion techniques are constrained by inherent limitations. For instance, optimization-based methods are generally predicated on prior assumptions that may induce systematic biases, whereas machine learning approaches are frequently criticized for their lack of interpretability and their inability to elucidate underlying traffic mechanisms. To address these limitations, a novel “retrieval and matching” algorithm is proposed that integrates machine learning with optimization. First, the concept of the “state gene” is introduced to encapsulate traffic structural knowledge representing frequently occurring traffic patterns. In the retrieval phase, a heterogeneous graph conventional network is employed to predict potential state genes for a given scenario. In the matching phase, the predicted state genes are utilized to minimize the discrepancy with the current traffic state. This integration not only enhances the interpretability of the estimation process but also endows the optimization component with reverse inference capability through the incorporation of machine learning. Validation using real-world data from the G92 Freeway in Zhejiang, China, demonstrates high accuracy, yielding Mean Absolute Percentage Errors (MAPE) of 1.12 − 1.65 % during peak periods and 1.28 − 1.67 % during off-peak periods.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104105"},"PeriodicalIF":8.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864976","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 carbon–neutral strategies in the healthcare system: A three-stage Stackelberg game model","authors":"Haoran Li ,&nbsp;Yiwei Wu ,&nbsp;Baogui Xin ,&nbsp;Min Xu ,&nbsp;Shining Wu","doi":"10.1016/j.tre.2025.104128","DOIUrl":"10.1016/j.tre.2025.104128","url":null,"abstract":"<div><div>The healthcare sector is a major contributor to global carbon emissions, accounting for approximately 4%–5% of greenhouse gases. Despite growing concerns about environmental impacts, existing research largely isolates carbon reduction strategies specific to certain medical processes or supply chain components, neglecting the integrated nature of decision-making among key healthcare stakeholders. This paper bridges this gap by developing a comprehensive decision framework for optimal carbon–neutral strategies within a four-tier healthcare system comprising the government, non-profit hospitals, for-profit hospitals, and patients. Government carbon policies significantly influence hospital investments in carbon–neutral operations, which subsequently affect patient choices based on pricing and service quality. To optimize this process, a three-stage Stackelberg game model is formulated that captures the hierarchical and interdependent decision-making processes characteristic of healthcare operations. An incorporated algorithm consisting of four sub-algorithms is proposed, combining two methods: (1) discretization of medical service pricing and quality on hospital-level operations, and (2) a grey wolf optimizer-based heuristic method to determine optimal government policies regarding carbon tax, emission reduction subsidies, and carbon absorption subsidies. To verify the model and validate the algorithm, numerical experiments are conducted using data from a non-profit hospital in Canada and a for-profit hospital in the UK. This study introduces a novel, multi-tier decision model that links carbon policy governance with hospital operational decisions and patient behavior. The results provide practical insights for policymakers and hospital administrators, laying the groundwork for future empirical studies in sustainable healthcare management.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104128"},"PeriodicalIF":8.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860651","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":"Capturing traffic state variation process: An analytical modeling approach","authors":"Qixiu Cheng ,&nbsp;Qiyuan Song ,&nbsp;Zelin Wang ,&nbsp;Yuqian Lin ,&nbsp;Zhiyuan Liu","doi":"10.1016/j.tre.2025.104119","DOIUrl":"10.1016/j.tre.2025.104119","url":null,"abstract":"<div><div>Precise and dependable identification of traffic states is crucial for optimizing traffic system, which forms the foundation for mitigating congestion and enhancing the overall efficiency and stability of traffic operations. Existing research has mainly adopted methods such as signal processing methods and traffic fundamental diagrams, but each has its own shortcomings. Therefore, this study introduces a Bayesian online changepoint detection method, which can dynamically detect changepoints in traffic flow observation sequences to explain the progression of traffic state variation including traffic flow breakdown. This method is more flexible and adaptable compared to traditional methods. We use this method for empirical analysis. Moreover, this study proposes an adaptive multi-state traffic fundamental diagram model to identify changes in traffic states based on a modified s-shaped three-parameter (S3) fundamental diagram. Our proposed traffic state identification approach is highly interpretable, and can be used to capture traffic state features consisted of at most five different states with four density reference values. We use the method for theoretical analysis. Furthermore, this study applies the above two methods to a high-resolution vehicle trajectory dataset, achieving a comprehensive analysis of the process of variations in traffic state. The findings indicate a strong alignment in the traffic states detected by both techniques, thereby validating the enhanced efficacy of our methodologies for the recognition and analysis of traffic flow dynamics. By comparing the findings of field data analysis and theoretical analysis, a deeper understanding of the traffic state dynamics is achieved, which encompasses the transition from a free-flow condition to a congested one, along with the features of various traffic states such as the stable, metastable, and unstable phases.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104119"},"PeriodicalIF":8.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844409","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}