Pub Date : 2024-09-30DOI: 10.1016/j.tre.2024.103789
Bing Liu , Xiaolei Ma , Wei Liu , Zhenliang Ma
The pressing need to reduce greenhouse gas emissions triggers the imperative for efficient travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which place a significant financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget issues. This study proposes a personal carbon trading travel incentive (PCTTI) mechanism, to encourage private car commuters shifting to using public transit. The incentive budget for PCTTI is sourced from the revenue generated through selling carbon emission reductions resulting from commuters’ travel mode shifts. To determine the optimal incentives, we developed an incentive scheme optimization model based on the Stackelberg game model. Numerical analysis reveals the significant potential of the PCTTI to reduce carbon emissions and travel costs across various scenarios within a multi-modal transportation system. This potential is evident amidst changes in the fixed costs of car travel, carbon trading prices, the use of different travel modes, the value of time, and the prevalence of electric vehicles. The advantages are most pronounced when the carbon trading price exceeds 40 CNY/ton, and when the usage of public transit, the value of time, and the proportion of electric vehicles each fall below 0.4, 50 CNY/hour, and 0.4, respectively.
{"title":"Designing a carbon-trading incentive scheme for mode shifts in multi-modal transport systems","authors":"Bing Liu , Xiaolei Ma , Wei Liu , Zhenliang Ma","doi":"10.1016/j.tre.2024.103789","DOIUrl":"10.1016/j.tre.2024.103789","url":null,"abstract":"<div><div>The pressing need to reduce greenhouse gas emissions triggers the imperative for efficient travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which place a significant financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget issues. This study proposes a personal carbon trading travel incentive (PCTTI) mechanism, to encourage private car commuters shifting to using public transit. The incentive budget for PCTTI is sourced from the revenue generated through selling carbon emission reductions resulting from commuters’ travel mode shifts. To determine the optimal incentives, we developed an incentive scheme optimization model based on the Stackelberg game model. Numerical analysis reveals the significant potential of the PCTTI to reduce carbon emissions and travel costs across various scenarios within a multi-modal transportation system. This potential is evident amidst changes in the fixed costs of car travel, carbon trading prices, the use of different travel modes, the value of time, and the prevalence of electric vehicles. The advantages are most pronounced when the carbon trading price exceeds 40 CNY/ton, and when the usage of public transit, the value of time, and the proportion of electric vehicles each fall below 0.4, 50 CNY/hour, and 0.4, respectively.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103789"},"PeriodicalIF":8.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358989","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}
Pub Date : 2024-09-30DOI: 10.1016/j.tre.2024.103798
Yong Wang , Zikai Wei , Siyu Luo , Jingxin Zhou , Lu Zhen
Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.
{"title":"Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows","authors":"Yong Wang , Zikai Wei , Siyu Luo , Jingxin Zhou , Lu Zhen","doi":"10.1016/j.tre.2024.103798","DOIUrl":"10.1016/j.tre.2024.103798","url":null,"abstract":"<div><div>Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103798"},"PeriodicalIF":8.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359005","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}
Pub Date : 2024-09-29DOI: 10.1016/j.tre.2024.103786
Yuhan Guo , Yiyang Wang , Yuhan Chen , Lingxiao Wu , Wengang Mao
In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.
{"title":"Learning-based Pareto-optimum routing of ships incorporating uncertain meteorological and oceanographic forecasts","authors":"Yuhan Guo , Yiyang Wang , Yuhan Chen , Lingxiao Wu , Wengang Mao","doi":"10.1016/j.tre.2024.103786","DOIUrl":"10.1016/j.tre.2024.103786","url":null,"abstract":"<div><div>In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103786"},"PeriodicalIF":8.3,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329862","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}
Pub Date : 2024-09-28DOI: 10.1016/j.tre.2024.103784
Xiaohui Yu , Tiaojun Xiao , Georges Zaccour
Global manufacturers face a pricing dilemma: setting higher prices in foreign markets to offset import taxes may lead to unauthorized cross-border channels; while narrowing price differences between domestic and foreign markets to block these channels increases the tax burden. To address this challenge, we develop Stackelberg game models to investigate the pricing and unauthorized channel strategy for a global manufacturer. Our findings indicate that an unauthorized channel can benefit the manufacturer by providing a means to avoid import taxes and potentially increasing overall demand in the foreign market. When the impact of an unauthorized channel on brand reputation is low, the manufacturer should widen the price difference between domestic and foreign markets to allow it. Conversely, when facing high brand reputation risks, the manufacturer must consider the import tax in the foreign market. If the import tax is high, the manufacturer should narrow the price difference between domestic and foreign markets to block the unauthorized channel; otherwise, simply ignore the threat of the unauthorized channel and maintain regular prices. We also examine the effects of consumer acceptance of gray market products and import tax incentives for cross-border e-commerce. We find that an increase in the two factors enhances the manufacturer’s inclination to allow an unauthorized channel. Our results remain robust across varying import tax structures, production costs, consumer valuations, and exchange rates, as well as when there are differences in market potential and consumer valuation between domestic or foreign markets.
{"title":"Pricing and unauthorized channel strategies for a global manufacturer considering import taxes","authors":"Xiaohui Yu , Tiaojun Xiao , Georges Zaccour","doi":"10.1016/j.tre.2024.103784","DOIUrl":"10.1016/j.tre.2024.103784","url":null,"abstract":"<div><div>Global manufacturers face a pricing dilemma: setting higher prices in foreign markets to offset import taxes may lead to unauthorized cross-border channels; while narrowing price differences between domestic and foreign markets to block these channels increases the tax burden. To address this challenge, we develop Stackelberg game models to investigate the pricing and unauthorized channel strategy for a global manufacturer. Our findings indicate that an unauthorized channel can benefit the manufacturer by providing a means to avoid import taxes and potentially increasing overall demand in the foreign market. When the impact of an unauthorized channel on brand reputation is low, the manufacturer should widen the price difference between domestic and foreign markets to allow it. Conversely, when facing high brand reputation risks, the manufacturer must consider the import tax in the foreign market. If the import tax is high, the manufacturer should narrow the price difference between domestic and foreign markets to block the unauthorized channel; otherwise, simply ignore the threat of the unauthorized channel and maintain regular prices. We also examine the effects of consumer acceptance of gray market products and import tax incentives for cross-border e-commerce. We find that an increase in the two factors enhances the manufacturer’s inclination to allow an unauthorized channel. Our results remain robust across varying import tax structures, production costs, consumer valuations, and exchange rates, as well as when there are differences in market potential and consumer valuation between domestic or foreign markets.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103784"},"PeriodicalIF":8.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329863","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}
Pub Date : 2024-09-27DOI: 10.1016/j.tre.2024.103780
Handong Yao , Xiaopeng Li , Qianwen Li , Chenyang Yu
Contemporary research in connected and automated vehicle (CAV) operations typically segregates trajectory prediction from planning in two segregated models. Trajectory prediction narrowly focuses on reducing prediction errors, disregarding the implications for subsequent planning. As a result, CAVs adhering to trajectories planned based on such predictions may collide with surrounding traffic. To mitigate such vulnerabilities, this study introduces a holistic safety-aware neural network (SANN) framework, representing a paradigm shift by integrating trajectory prediction and planning into a cohesive model. The SANN architecture incorporates prediction and planning layers, leveraging existing neural networks for prediction and introducing novel recurrent neural cells embedded with car-following dynamics for planning. The prediction layers are regulated by the CAV trajectory planning performance including safety, mobility, and energy efficiency. A key innovation of the SANN lies in its approach to safety regulation, which is based on actual, rather than forecasted, traffic movements. By applying time geography theory, it assesses CAV motion feasibility, setting limits on speed and acceleration for safety in line with actual traffic patterns. This feasibility analysis results are integrated into the neural loss function as a penalty factor, steering the optimization process towards safer CAV operations. The efficacy of the SANN is enhanced by employing the sequential unconstrained minimization technique, which enables the fine-tuning of penalty weights, thereby producing more robust solutions. Empirical evaluations, comparing the holistic SANN with conventional segregated models, demonstrate its superior performance. The SANN achieves substantial enhancements in safety and energy efficiency, with only a marginal compromise on mobility. This success underscores the significance of integrating machine learning with domain knowledge (operations research and traffic flow theory) for safer and more environmentally friendly CAV operations.
{"title":"Safety aware neural network for connected and automated vehicle operations","authors":"Handong Yao , Xiaopeng Li , Qianwen Li , Chenyang Yu","doi":"10.1016/j.tre.2024.103780","DOIUrl":"10.1016/j.tre.2024.103780","url":null,"abstract":"<div><div>Contemporary research in connected and automated vehicle (CAV) operations typically segregates trajectory prediction from planning in two segregated models. Trajectory prediction narrowly focuses on reducing prediction errors, disregarding the implications for subsequent planning. As a result, CAVs adhering to trajectories planned based on such predictions may collide with surrounding traffic. To mitigate such vulnerabilities, this study introduces a holistic safety-aware neural network (SANN) framework, representing a paradigm shift by integrating trajectory prediction and planning into a cohesive model. The SANN architecture incorporates prediction and planning layers, leveraging existing neural networks for prediction and introducing novel recurrent neural cells embedded with car-following dynamics for planning. The prediction layers are regulated by the CAV trajectory planning performance including safety, mobility, and energy efficiency. A key innovation of the SANN lies in its approach to safety regulation, which is based on actual, rather than forecasted, traffic movements. By applying time geography theory, it assesses CAV motion feasibility, setting limits on speed and acceleration for safety in line with actual traffic patterns. This feasibility analysis results are integrated into the neural loss function as a penalty factor, steering the optimization process towards safer CAV operations. The efficacy of the SANN is enhanced by employing the sequential unconstrained minimization technique, which enables the fine-tuning of penalty weights, thereby producing more robust solutions. Empirical evaluations, comparing the holistic SANN with conventional segregated models, demonstrate its superior performance. The SANN achieves substantial enhancements in safety and energy efficiency, with only a marginal compromise on mobility. This success underscores the significance of integrating machine learning with domain knowledge (operations research and traffic flow theory) for safer and more environmentally friendly CAV operations.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103780"},"PeriodicalIF":8.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324122","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}
Pub Date : 2024-09-26DOI: 10.1016/j.tre.2024.103782
Umut Ermağan , Barış Yıldız , F. Sibel Salman
Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort.
日益发展的城市化、爆炸式增长的电子商务、不断提高的客户期望以及减少运输对环境影响的需要，都要求采用创新的 "最后一英里 "递送解决方案。本文探讨了一种将公共交通与自主机器人（AR）相结合的新型快运模式，并研究了其实时管理。在短交货期承诺的动态需求到达情况下，我们提出了一个滚动视野框架，并设计了一种机器学习增强型列生成（CG）方法来解决实时 AR 调度问题。我们利用真实世界的配送需求数据进行的数值实验结果表明，所提出的系统在减少旅行时间、车辆流量、废气排放和噪音方面潜力巨大。我们的结果还揭示了基于学习的 CG 方法的功效，它能以更少的计算量提供与经典 CG 方法几乎相同质量的解决方案。
{"title":"Express shipments with autonomous robots and public transportation","authors":"Umut Ermağan , Barış Yıldız , F. Sibel Salman","doi":"10.1016/j.tre.2024.103782","DOIUrl":"10.1016/j.tre.2024.103782","url":null,"abstract":"<div><div>Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103782"},"PeriodicalIF":8.3,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324121","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}
In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.
{"title":"A time-driven simulation–optimization framework for the dynamic heterogeneous order-courier assignment problem for instant deliveries","authors":"Diana Jorge, Tomás Rocha, Tânia Rodrigues Pereira Ramos","doi":"10.1016/j.tre.2024.103783","DOIUrl":"10.1016/j.tre.2024.103783","url":null,"abstract":"<div><div>In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103783"},"PeriodicalIF":8.3,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324120","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}
Pub Date : 2024-09-25DOI: 10.1016/j.tre.2024.103757
Lindsay K. Graff , Katherine A. Flanigan , Sean Qian
Cities aiming to improve their transportation networks are integrating emerging mobility options at a rapid pace. These modes provide commuters with greater flexibility to construct more convenient trips and reach a larger set of essential service destinations. A few open-source tools allow planners to conduct multimodal routing analysis in time-dependent networks, but they do not sufficiently capture the full set of travel mode combinations and disutility factors perceived by individual travelers. To this end, we introduce NOMAD: Network Optimization for Multimodal Accessibility Decision-making. NOMAD integrates the personal vehicle, transportation network company, carshare, public transit, personal bike, bikeshare, scooter, walking, and feeder micro-transit modes into a unified routable network model. A generalized travel cost function incorporates the following disutility factors: monetary cost, day-to-day mean travel time, (un)reliability as represented by day-to-day 95th percentile travel time, crash risk, and physical discomfort. The proposed open-source tool can be used to create multimodal travel cost matrices, which may immediately serve as an input for accessibility analysis and other policy decisions related to emerging mobility options. This paper develops the network model that forms the basis of NOMAD and demonstrates four use cases in Pittsburgh, PA.
{"title":"Constructing a routable multimodal, multi-cost, time-dependent network model with all emerging mobility options: Methodology and case studies","authors":"Lindsay K. Graff , Katherine A. Flanigan , Sean Qian","doi":"10.1016/j.tre.2024.103757","DOIUrl":"10.1016/j.tre.2024.103757","url":null,"abstract":"<div><div>Cities aiming to improve their transportation networks are integrating emerging mobility options at a rapid pace. These modes provide commuters with greater flexibility to construct more convenient trips and reach a larger set of essential service destinations. A few open-source tools allow planners to conduct multimodal routing analysis in time-dependent networks, but they do not sufficiently capture the full set of travel mode combinations and disutility factors perceived by individual travelers. To this end, we introduce NOMAD: Network Optimization for Multimodal Accessibility Decision-making. NOMAD integrates the personal vehicle, transportation network company, carshare, public transit, personal bike, bikeshare, scooter, walking, and feeder micro-transit modes into a unified routable network model. A generalized travel cost function incorporates the following disutility factors: monetary cost, day-to-day mean travel time, (un)reliability as represented by day-to-day 95th percentile travel time, crash risk, and physical discomfort. The proposed open-source tool can be used to create multimodal travel cost matrices, which may immediately serve as an input for accessibility analysis and other policy decisions related to emerging mobility options. This paper develops the network model that forms the basis of NOMAD and demonstrates four use cases in Pittsburgh, PA.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103757"},"PeriodicalIF":8.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319035","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}
Pub Date : 2024-09-24DOI: 10.1016/j.tre.2024.103788
Guowei Dou , Kun Wei , Tingting Sun , Lijun Ma
Blockchain technology (BT) is widely implemented in businesses, yet its adoption within distinct channel leaderships in a supply chain has not been well studied. Following real-world practices, we build analytical models to study two strategies in which the manufacturer leads BT adoption (MLB) and the retailer leads BT adoption (RLB). Our results show that BT adoption does not necessarily create extra supply chain profits. Higher profits can be obtained when consumers show a strong preference for traceability or when the leader shares sufficient costs otherwise. Raising leaders’ cost-sharing proportions does not necessarily benefit followers, and the cost burden may motivate leaders to reduce the traceability level, thereby decreasing overall benefits. Interestingly, cost-sharing is not a “zero-sum” game for supply chain members, and sharing more costs as followers may help create mutual benefits. A comparison of the strategies of MLB and RLB reveals that the product price, traceability level, and carbon emissions in MLB can either be higher or lower than those in RLB. From an environmental perspective, we show that the carbon tax has a nonmonotonic effect on product retail prices. For the supply chain, it is possible to increase profits but simultaneously reduce emissions in each strategy, and a superior strategy that improves both economic and environmental performance exists. By modelling the regulator’s participation in BT adoption, we further show that emission taxes and BT subsidies are not concomitant, and surprisingly, we find that the emission tax may either increase or decrease with product emission intensity. Moreover, our extension shows that regular operational costs for BT may impact the economic performance of BT adoption but other key findings remain robust.
{"title":"Blockchain technology adoption in a supply chain: Channel leaderships and environmental implications","authors":"Guowei Dou , Kun Wei , Tingting Sun , Lijun Ma","doi":"10.1016/j.tre.2024.103788","DOIUrl":"10.1016/j.tre.2024.103788","url":null,"abstract":"<div><div>Blockchain technology (BT) is widely implemented in businesses, yet its adoption within distinct channel leaderships in a supply chain has not been well studied. Following real-world practices, we build analytical models to study two strategies in which the manufacturer leads BT adoption (MLB) and the retailer leads BT adoption (RLB). Our results show that BT adoption does not necessarily create extra supply chain profits. Higher profits can be obtained when consumers show a strong preference for traceability or when the leader shares sufficient costs otherwise. Raising leaders’ cost-sharing proportions does not necessarily benefit followers, and the cost burden may motivate leaders to reduce the traceability level, thereby decreasing overall benefits. Interestingly, cost-sharing is not a “zero-sum” game for supply chain members, and sharing more costs as followers may help create mutual benefits. A comparison of the strategies of MLB and RLB reveals that the product price, traceability level, and carbon emissions in MLB can either be higher or lower than those in RLB. From an environmental perspective, we show that the carbon tax has a nonmonotonic effect on product retail prices. For the supply chain, it is possible to increase profits but simultaneously reduce emissions in each strategy, and a superior strategy that improves both economic and environmental performance exists. By modelling the regulator’s participation in BT adoption, we further show that emission taxes and BT subsidies are not concomitant, and surprisingly, we find that the emission tax may either increase or decrease with product emission intensity. Moreover, our extension shows that regular operational costs for BT may impact the economic performance of BT adoption but other key findings remain robust.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103788"},"PeriodicalIF":8.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312121","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}
Our research is inspired by the subcontracting problem at a major oil field services company in North America. The company’s supply chain consists of suppliers bringing raw materials to a Free Trade Zone (FTZ). The FTZ receives raw materials in full containers from various suppliers, and then the company ships them to various plants (e.g. oil excavation sites) frequently via subcontractors. This allows the company to focus on managing only the inbound transportation and inventory at the FTZ. The demand for each raw material is stochastic. We derive an algorithm running at polynomial time for the stochastic programming formulation and perform regret Robust Optimization to handle the demand uncertainty. We also use a Sample Average Approximation method to alleviate the high computational requirement of the robust optimization model. The modeling approach demonstrated by this paper not only meets the needs of this specific company and industry but also can be applied to other industries with similar supply chain structures.
{"title":"Supply chain planning with free trade zone and uncertain demand","authors":"Haoying Sun , Manoj Vanajakumari , Chelliah Sriskandarajah , Subodha Kumar","doi":"10.1016/j.tre.2024.103771","DOIUrl":"10.1016/j.tre.2024.103771","url":null,"abstract":"<div><div>Our research is inspired by the subcontracting problem at a major oil field services company in North America. The company’s supply chain consists of suppliers bringing raw materials to a Free Trade Zone (FTZ). The FTZ receives raw materials in full containers from various suppliers, and then the company ships them to various plants (e.g. oil excavation sites) frequently via subcontractors. This allows the company to focus on managing only the inbound transportation and inventory at the FTZ. The demand for each raw material is stochastic. We derive an algorithm running at polynomial time for the stochastic programming formulation and perform <span><math><mrow><mi>μ</mi><mo>−</mo></mrow></math></span> regret Robust Optimization to handle the demand uncertainty. We also use a Sample Average Approximation method to alleviate the high computational requirement of the robust optimization model. The modeling approach demonstrated by this paper not only meets the needs of this specific company and industry but also can be applied to other industries with similar supply chain structures.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103771"},"PeriodicalIF":8.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314526","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}
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