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

{"title":"The value of Augmented Reality on platform-based supply chains: Impact of Accuracy Effect and Entertainment Effect","authors":"Qiang Zhou ,&nbsp;Xiaolong Guo ,&nbsp;Can Sun","doi":"10.1016/j.tre.2024.103953","DOIUrl":"10.1016/j.tre.2024.103953","url":null,"abstract":"<div><div>Augmented Reality (AR) has been increasingly applied in e-commerce to facilitate consumer purchasing. This paper characterizes and models two features of AR: the Accuracy Effect and Entertainment Effect. The former refers to AR mitigating consumer valuation bias, while the latter refers to the psychological entertainment consumers obtain when using AR to facilitate their purchasing. Although AR mitigates consumer valuation bias (Accuracy Effect), AR may also reduce the consumers’ prior valuation of products. In addition, despite AR increasing the consumers’ willingness to pay (through the Entertainment Effect), it exacerbates the bias between the products’ true value and the value perceived by consumers. This research investigates the question that whether the platform should adopt AR, and clarifies AR’s value to the platform, consumers, and integrated supply chain. First, we find that AR does not necessarily benefit the platform, while it can favor the platform only if the Accuracy Effect is large and the Entertainment Effect is small. Second, a large Entertainment Effect always hurts consumers. However, the impact of the Accuracy Effect on consumers is ambiguous and differentiated by a threshold influenced by the Entertainment Effect, where a larger Entertainment Effect narrows the range in which the Accuracy Effect favors consumers. Third, the integrated supply chain performance can be enhanced by adopting AR because the manufacturer can benefit from a larger Entertainment Effect while the platform can be more profitable with a larger Accuracy Effect. In extensions, we particularly find that product publicity can make AR more likely to favor the platform only when the publicity effectiveness reaches a certain level, and consumer return cost can be a driving force for the platform to adopt AR.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103953"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939701","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":"Cargo selection, route planning, and speed optimization in tramp shipping under carbon intensity indicator (CII) regulations","authors":"Liangqi Cheng,&nbsp;Lerong Xu,&nbsp;Xiwen Bai","doi":"10.1016/j.tre.2024.103948","DOIUrl":"10.1016/j.tre.2024.103948","url":null,"abstract":"<div><div>To mitigate the significant environmental impacts of the shipping industry, the International Maritime Organization (IMO) introduced the Carbon Intensity Indicator (CII), which measures CO2 emissions per unit of cargo-carrying capacity and distance traveled. While the implementation of energy-efficient technologies is crucial for meeting CII regulations, these advancements often entail substantial investment costs. Consequently, optimizing operations has become a more practical short-term approach; however, operational adjustments made solely to comply with CII regulations may also have unintended adverse effects. To address this issue, this research develops a pick up and delivery optimization model for tramp ships, which operate on irregular schedules and routes, to minimize total emissions and costs while complying with CII regulations. The model investigates the combination of cargo selection, route planning, and speed optimization, reflecting the comprehensive and unique characteristics of tramp shipping. The problem is solved using Danzig-Wolfe decomposition and a branch-and-price algorithm, with the CII regulations being met in the pricing problem through a customized heuristic. Numerical results demonstrate that the proposed approach can find optimal or near-optimal solutions within a short time. Various experiments explore the effects of CII regulations on tramp shipping operations, environmental performances, and economic benefits. The results indicate that demand-based CII and stricter CII regulations cause ships to carry fewer cargoes, sail shorter ballast distances, reduce speed, and increase load on board. This ultimately reduces CO2 emissions but also lowers total profits. The findings assist industry stakeholders in complying with stringent environmental regulations and aid policymakers in designing targeted regulatory policies, thereby promoting sustainable maritime transport.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103948"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939702","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 real-time prediction framework for energy consumption of electric buses using integrated Machine learning algorithms","authors":"Changyin Dong ,&nbsp;Zhuozhi Xiong ,&nbsp;Ni Li ,&nbsp;Xinlian Yu ,&nbsp;Mingzhang Liang ,&nbsp;Chu Zhang ,&nbsp;Ye Li ,&nbsp;Hao Wang","doi":"10.1016/j.tre.2024.103884","DOIUrl":"10.1016/j.tre.2024.103884","url":null,"abstract":"<div><div>An accurate prediction of energy consumption in electric buses (EBs) can effectively reduce driving range anxiety and facilitate bus scheduling. Existing studies have not provided real-time predictions based on distance traveled using integrated machine learning methods. This study proposes a framework for predicting EB energy consumption, which is primarily divided into energy consumption estimation, kinematic feature prediction, and energy consumption prediction. The framework begins by fusing high-resolution real-world EB data with weather and road information, from which five types of influencing factors are extracted for different driving distances. An eXtreme Gradient Boosting (XGBoost) model is developed to evaluate feature importance and estimate the energy consumption rate (ECR). The SHapley Additive explanation (SHAP) method is then used to analyze the factors affecting the ECR. To predict important kinematic characteristics, spatial and temporal characteristics are captured using Long Short-Term Memory (LSTM) and a fully connected neural network. Finally, the predicted kinematic characteristics and the XGBoost model are combined to enable real-time prediction of the ECR. The results indicate that estimation and prediction accuracies gradually improve with increased driving distance. The mean absolute error of average ECR decreases from 43.9 % for 100 m to 7.5 % for 16 km. Temperature, bus stop density, and peak periods emerge as the most significant external factors after 8 km. This framework shows an improvement of over 10 % in most scenarios compared with other models in the literature, enabling individual forecasts of energy consumption currently in transit and aiding in the calculation of remaining battery-supported distance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103884"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788854","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 hub location problem with comparisons of compact formulations: A note","authors":"Fran Setiawan ,&nbsp;Tolga Bektaş ,&nbsp;Çağatay Iris","doi":"10.1016/j.tre.2024.103902","DOIUrl":"10.1016/j.tre.2024.103902","url":null,"abstract":"<div><div>Hub location is a planning problem that involves choosing, from a set of nodes, a subset to designate as hub facilities, linking the hubs to the remaining nodes using a hub-and-spoke structure, and routing of flows on the resulting network. This paper presents theoretical and computational comparisons of the fundamental compact formulations of the <span><math><mi>p</mi></math></span>-hub location problem (<span><math><mi>p</mi></math></span>-HLP) for three allocation strategies, namely single, multiple and <span><math><mi>r</mi></math></span>-allocation. Our theoretical results show that path-based formulations offer the strongest linear programming relaxation. The computational experiments, run on three prominent datasets using a state-of-the-art commercial solver indicate that, flow-based formulations generally solve the largest number of instances to optimality and require the shortest solution time, especially for large-scale instances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103902"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867658","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":"A multi-objective reinforcement learning-based velocity optimization approach for electric trucks considering battery degradation mitigation","authors":"Ruo Jia ,&nbsp;Kun Gao ,&nbsp;Shaohua Cui ,&nbsp;Jing Chen ,&nbsp;Jelena Andric","doi":"10.1016/j.tre.2024.103885","DOIUrl":"10.1016/j.tre.2024.103885","url":null,"abstract":"<div><div>Electrification of commercial vehicles for more sustainable logistic systems has been promoted in the past decades. This study proposes a deep reinforcement learning method for velocity optimization and battery degradation minimization during operation for battery-powered electric trucks (BETs), aiming to achieve a safe, efficient, and comfortable driving control policy for BETs. To obtain an optimal solution considering both calendar and cyclic battery degradation, Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy Gradient (TD3) approaches are integrated within a simulation environment. To optimize overall BET velocity performance, a trade-off among safety, efficiency, comfort, and battery degradation is incorporated into the reward function of reinforcement learning using Mixture of Experts (MoE) model. The results indicate that the proposed TD3-MoE model achieves safe, efficient, and comfortable car-following control while optimizing total battery degradation. Specifically, the model achieves reductions in total battery capacity loss ranging from 2.4% to 8.3% at different states of charge (SoC) of battery compared to human-driven scenarios. Moreover, despite calendar battery degradation being inevitable, the cyclic battery degradation is effectively mitigated by 27.7% to 29.6% compared to the same SoCs in human-driving data. Furthermore, the TD3-MoE model achieves significant energy consumption reductions, ranging from 35.3% to 39.8% compared to real car-following trajectories.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103885"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867661","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":"Green design and information sharing in a horizontally competitive supply chain","authors":"Mengfan Li ,&nbsp;Zhaofu Hong ,&nbsp;Xiaolong Guo ,&nbsp;Yugang Yu","doi":"10.1016/j.tre.2024.103858","DOIUrl":"10.1016/j.tre.2024.103858","url":null,"abstract":"<div><div>This study explores the impact of information sharing on green supply chains with horizontal competition, addressing an important yet underexplored issue. Using a Stackelberg game framework, we examine green product design and information-sharing dynamics in a supply chain consisting of a common retailer and two competing manufacturers. The retailer decides whether to share green demand information with manufacturers, who then determine product greenness, while the retailer sets the retail price. The findings reveal that information sharing can have mixed effects on manufacturers’ profitability, depending on competitive dynamics and demand sensitivity to product greenness. Additionally, the interplay between information sharing and green design strategies emphasizes the importance of aligning product design decisions with optimal information-sharing practices. While information sharing consistently improves environmental performance in non-competitive settings, it can lead to negative environmental outcomes in competitive scenarios. Extending the analysis to cases where manufacturers control wholesale pricing and information is shared sequentially, the findings remain robust. This study highlights the dual role of information sharing as a driver of supply chain efficiency and a potential source of environmental inefficiencies in competitive markets. The results offer actionable insights for crafting green supply chain strategies that balance economic and environmental objectives.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103858"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788832","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":"String stability under general topologies for CAVs: A coupled sliding surface-based distributed TMPC approach","authors":"Xuan Wang ,&nbsp;Hongmao Qin ,&nbsp;Yougang Bian ,&nbsp;Dezong Zhao ,&nbsp;Nan Zheng","doi":"10.1016/j.tre.2024.103937","DOIUrl":"10.1016/j.tre.2024.103937","url":null,"abstract":"<div><div>Connected and Automated Vehicles (CAVs) are critical components of Intelligent Transportation Systems (ITS) to address traffic accidents and congestion and improve fuel economy. It remains an unsolved challenge to guarantee string stability for cooperative control of CAVs under general topologies. This study proposes a distributed tube-based model predictive control (TMPC) approach for string stable cooperative control of CAVs under general topologies by incorporating a novel string stable coupled sliding surface (CSS) as the terminal. This method is less restrictive and has a wider application range than existing string stable methods. First, a novel string stable CSS based on average spacing error and in-neighbor’s information is designed to guarantee string stability. Then, a TMPC controller is proposed to enforce the system dynamics to retain on the string stable CSS in a receding horizon manner. Specifically, the cost function, terminal constraints, and terminal assumed control input are appropriately designed to combine the string stable CSS with TMPC. Moreover, feasibility, closed-loop stability, and string stability are theoretically proved. Simulation results show that the proposed controller outperforms the state-of-the-art distributed model predictive control (DMPC) and TMPC methods in tracking performance and robustness.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103937"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887897","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":"Heterogeneous vessel fleet co-management for liner alliances under profit-sharing agreement and weekly-dependent demand","authors":"Yadong Wang ,&nbsp;Huming Zhang ,&nbsp;Tingsong Wang ,&nbsp;Jinping Liu","doi":"10.1016/j.tre.2024.103880","DOIUrl":"10.1016/j.tre.2024.103880","url":null,"abstract":"<div><div>As the oversupply of shipping capacity and the competition within the shipping industry intensifies, liner alliances have emerged as the prevailing mode of cooperation. The members in a liner alliance often have different shipping resources, indicating that they have to coordinate the shipping resources with each other, in order to achieve smooth cooperation. During the coordination, the fairness of members cannot be ignored as it forms the foundation of cooperation stability. Meanwhile, the vessel fleet is often heterogeneous in practice, not homogeneous assumed in most of existing studies. Therefore, this research explores the joint optimization problem of heterogeneous vessel fleet co-management (including fleet co-deployment, vessel co-scheduling, vessel co-sequencing, slot co-chartering and slot co-allocation), taking into account profit-sharing agreement and weekly-dependent demand. In response to this problem, a mixed-integer nonlinear program is first formulated with the goal of maximizing the overall profit reached by the alliance. We then linearize this nonlinear program, and subsequently develop a solution method to identify the optimal solution for the linearized model. Various numerical tests are performed to examine the validity of the proposed model. Furthermore, several managerial insights that support the operation of liner alliance are delivered.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103880"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816524","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":"Crowd-shipping systems with public transport passengers: Operational planning","authors":"Seyed Sina Mohri ,&nbsp;Neema Nassir ,&nbsp;Russell G. Thompson ,&nbsp;Patricia Sauri Lavieri ,&nbsp;Hadi Ghaderi","doi":"10.1016/j.tre.2024.103916","DOIUrl":"10.1016/j.tre.2024.103916","url":null,"abstract":"<div><div>This study designs a crowdshipping (CS) delivery system with public transport (PT) passengers at the operational decision-making level. In this system, parcel lockers (PLs) are positioned in PT stations, through which small and light parcels are allocated to passengers for delivery to their final delivery addresses (i.e., performing the last-mile delivery). A probabilistic mathematical model is formulated with behavioural constraints to estimate the probabilities of accepting CS tasks by passengers. The probability is estimated based on a logit function, sensitive to the parcel’s weight, reimbursement amount, and the walking detour required to deliver the parcel to its final destination. The logit model is constructed based on survey data collected from the Greater Sydney (GS) area, Australia. The mathematical model optimises the allocation of delivery tasks to the CS system and PLs, subsequently, incentivising CS-allocated tasks for participating passengers. Furthermore, the model performs the routing of vehicles to deliver non-allocated parcels, including heavy parcels. A heuristic solution algorithm is then proposed to optimise decisions related to allocation, routing, and incentivisation, which was tested on a real case study. By conducting sensitivity analysis on various model parameters, results show that for a small carrier, utilising a PT-based CS system could minimise daily delivery costs by up to 36%, depending on passengers’ rate of familiarity with the CS initiative and the number of PT stations equipped with PLs. Vehicle delivery cost in the CS-integrated delivery system is also reduced between 50% and 65%, in comparison to the conventional vehicle-only system. Our study reveals that a CS system should offer higher incentives at the beginning, and as CS familiarity grows, figures could be reduced depending on other market and operational conditions. Furthermore, simulated experiments suggest that denser PL networks enable carriers to reduce incentives even at earlier stages with lower familiarity rates.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103916"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816527","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":"Operating policies for robotic cellular warehousing systems","authors":"Benedict Jun Ma ,&nbsp;Shenle Pan ,&nbsp;Bipan Zou ,&nbsp;Yong-Hong Kuo ,&nbsp;George Q. Huang","doi":"10.1016/j.tre.2024.103875","DOIUrl":"10.1016/j.tre.2024.103875","url":null,"abstract":"<div><div>Robotic Cellular Warehousing Systems provide an innovative robot-to-goods picking approach designed to improve robot transportation efficiency, where robots move to pick items and transport the picked items to workstations. In this study, we investigate the optimal operating policies for such a system by comparing two picking strategies (pick-while-sort and pick-then-sort) and three robot-to-workstation assignment rules (random, closest, and dedicated). Specifically, we develop dedicated closed queuing networks to model robot-to-goods picking and estimate warehouse throughput under different policies through single-class and multi-class models. The effectiveness of these analytical models is validated through numerical simulations, with an average gap of 5.53% between simulation and analytical results. Additionally, we conduct a series of numerical experiments to examine the impact of various factors on warehouse performance, including the numbers of robots and workstations, robot capacity, order size, and sorting efficiency. Based on the experimental findings, we provide managerial implications that offer insights into optimizing resource allocation and system configuration. These insights enable warehouse managers to improve operational efficiency and overall performance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"194 ","pages":"Article 103875"},"PeriodicalIF":8.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788859","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}