For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques from the decision diagram literature to generate and strengthen novel route relaxations for obtaining dual bounds without using column generation. Our approach is generic and can be applied to various vehicle routing problems in which corresponding dynamic programming models are available. We apply our framework to the traveling salesman with drone problem and show that it produces dual bounds competitive to those from the state of the art. Applied to larger problem instances in which the state-of-the-art approach does not scale, our method outperforms other bounding techniques from the literature.Funding: This work was supported by the National Science Foundation [Grant 1918102] and the Office of Naval Research [Grants N00014-18-1-2129 and N00014-21-1-2240].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0170 .
{"title":"Dual Bounds from Decision Diagram-Based Route Relaxations: An Application to Truck-Drone Routing","authors":"Ziye Tang, Willem-Jan van Hoeve","doi":"10.1287/trsc.2021.0170","DOIUrl":"https://doi.org/10.1287/trsc.2021.0170","url":null,"abstract":"For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques from the decision diagram literature to generate and strengthen novel route relaxations for obtaining dual bounds without using column generation. Our approach is generic and can be applied to various vehicle routing problems in which corresponding dynamic programming models are available. We apply our framework to the traveling salesman with drone problem and show that it produces dual bounds competitive to those from the state of the art. Applied to larger problem instances in which the state-of-the-art approach does not scale, our method outperforms other bounding techniques from the literature.Funding: This work was supported by the National Science Foundation [Grant 1918102] and the Office of Naval Research [Grants N00014-18-1-2129 and N00014-21-1-2240].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0170 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"15 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ning Guo, Wai Wong, Rui Jiang, S. C. Wong, Qing-Yi Hao, Chao-Yun Wu
Cycling has emerged as one of the most important green transport modes in recent years, with cities increasingly prioritizing cycling in their sustainable policy agenda. However, the associated traffic dynamics, especially the evolution of bicycle flow at bottlenecks, have not been extensively studied. In this study, real-world experiments were conducted to investigate the dynamics of bicycle flow at bottlenecks under various cycling demands generated by the cyclist unloading and loading processes. Upon the activation of the bottleneck, its capacity remained largely constant. For the same physical system, the bottleneck capacity of the cyclist loading process exceeded that of the unloading process, indicating the occurrence of capacity drop and hysteresis. Statistical analyses demonstrated that the capacity drop was attributable to the difference in speeds of the two processes for the same cycling demands after the bottleneck activation. These findings could potentially be explained by behavioral inertia. Further analysis revealed that, compared with the unloading process, the cyclist loading process was associated with higher cycling speeds owing to the higher overtaking rates. The outcomes of this study can advance our understanding of the physics of bicycle flow dynamics and provide valuable insights for transport planning professionals involved in facility planning and control of existing networks. Funding: This work was supported by National Natural Science Foundation of China [Grants 71931002 and 72288101], the University of Hong Kong [Francis S Y Bong Professorship to S. C. Wong], the Guangdong-Hong Kong-Macau Joint Laboratory Program of the 2020 Guangdong New Innovative Strategic Research Fund, Guangdong Science and Technology Department [Grant 2020B1212030009], and Fundamental Research Funds for the Central Universities [Grant JZ2023YQTD0073]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0193 .
{"title":"Bicycle Flow Dynamics of Cyclist Loading and Unloading Processes at Bottlenecks","authors":"Ning Guo, Wai Wong, Rui Jiang, S. C. Wong, Qing-Yi Hao, Chao-Yun Wu","doi":"10.1287/trsc.2023.0193","DOIUrl":"https://doi.org/10.1287/trsc.2023.0193","url":null,"abstract":"Cycling has emerged as one of the most important green transport modes in recent years, with cities increasingly prioritizing cycling in their sustainable policy agenda. However, the associated traffic dynamics, especially the evolution of bicycle flow at bottlenecks, have not been extensively studied. In this study, real-world experiments were conducted to investigate the dynamics of bicycle flow at bottlenecks under various cycling demands generated by the cyclist unloading and loading processes. Upon the activation of the bottleneck, its capacity remained largely constant. For the same physical system, the bottleneck capacity of the cyclist loading process exceeded that of the unloading process, indicating the occurrence of capacity drop and hysteresis. Statistical analyses demonstrated that the capacity drop was attributable to the difference in speeds of the two processes for the same cycling demands after the bottleneck activation. These findings could potentially be explained by behavioral inertia. Further analysis revealed that, compared with the unloading process, the cyclist loading process was associated with higher cycling speeds owing to the higher overtaking rates. The outcomes of this study can advance our understanding of the physics of bicycle flow dynamics and provide valuable insights for transport planning professionals involved in facility planning and control of existing networks. Funding: This work was supported by National Natural Science Foundation of China [Grants 71931002 and 72288101], the University of Hong Kong [Francis S Y Bong Professorship to S. C. Wong], the Guangdong-Hong Kong-Macau Joint Laboratory Program of the 2020 Guangdong New Innovative Strategic Research Fund, Guangdong Science and Technology Department [Grant 2020B1212030009], and Fundamental Research Funds for the Central Universities [Grant JZ2023YQTD0073]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0193 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"71 2","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2023-12-12DOI: 10.1287/trsc.2023.intro.v58.n1
Matthias Winkenbach, Stefan Spinler, Julian Pachon, Karthik Konduri
In this paper, we introduce the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems, which draws its inspiration from the academic community’s positive reception of the 2021 Amazon Last Mile Routing Research Challenge. We provide a structured overview of the papers featured in this special issue, and briefly discuss other noteworthy contributions to the research challenge. Further, we point the reader to a number of peer-reviewed publications outside of this special issue that directly or indirectly emerged from the research challenge. We conclude by highlighting a number of important priorities for future research into applications of machine learning to real-world route planning problems.
{"title":"Introduction to the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems","authors":"Matthias Winkenbach, Stefan Spinler, Julian Pachon, Karthik Konduri","doi":"10.1287/trsc.2023.intro.v58.n1","DOIUrl":"https://doi.org/10.1287/trsc.2023.intro.v58.n1","url":null,"abstract":"In this paper, we introduce the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems, which draws its inspiration from the academic community’s positive reception of the 2021 Amazon Last Mile Routing Research Challenge. We provide a structured overview of the papers featured in this special issue, and briefly discuss other noteworthy contributions to the research challenge. Further, we point the reader to a number of peer-reviewed publications outside of this special issue that directly or indirectly emerged from the research challenge. We conclude by highlighting a number of important priorities for future research into applications of machine learning to real-world route planning problems.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"29 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marelot H. de Vos, Rolf N. van Lieshout, Twan Dollevoet
This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling problem as a path-based binary program, whose linear relaxation we solve using column generation. We find integer feasible solutions using two heuristics: price-and-branch and a diving heuristic, including acceleration strategies. We test the approach using data from the concession Gooi en Vechtstreek in the Netherlands, containing up to 816 trips. The diving heuristic outperforms the other heuristic and solves the entire concession within seven hours of computation time with an optimality gap of less than 3%.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0253 .
{"title":"Electric Vehicle Scheduling in Public Transit with Capacitated Charging Stations","authors":"Marelot H. de Vos, Rolf N. van Lieshout, Twan Dollevoet","doi":"10.1287/trsc.2022.0253","DOIUrl":"https://doi.org/10.1287/trsc.2022.0253","url":null,"abstract":"This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling problem as a path-based binary program, whose linear relaxation we solve using column generation. We find integer feasible solutions using two heuristics: price-and-branch and a diving heuristic, including acceleration strategies. We test the approach using data from the concession Gooi en Vechtstreek in the Netherlands, containing up to 816 trips. The diving heuristic outperforms the other heuristic and solves the entire concession within seven hours of computation time with an optimality gap of less than 3%.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0253 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"37 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive-scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method.History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0015 .
解决复杂的路由问题对于提高物流运营效率至关重要。然而，由于其复杂性，这些问题通常采用集群优先、路线次之的框架顺序解决。遗憾的是，由于初始阶段的原因，这种两阶段框架可能存在次优化问题。为了解决这个问题，我们建议利用潜在集群的最佳旅行长度信息作为初始步骤，将两阶段方法转化为不那么近视的解决方案框架。为此，我们引入了基于神经网络（NN）的快速、高精度旅行推销员问题（TSP）游程长度估算器。我们的方法将神经网络的强大功能与路由领域的理论知识相结合，利用包括节点级、实例级和解决方案级特征的新颖特征集。这种数据与领域知识的混合使我们的预测结果与最优结果的平均偏差小于 0.7%。与以往研究不同的是，我们设计并采用了新的实例来复制现实生活中的物流网络和形态。这些实例具有引入大量计算成本的特点，因此更具挑战性。为了应对这些挑战，我们开发了一种新颖高效的方法，用于获取 TSP 的下限和部分解，随后将其用作解级预测器。我们的计算研究表明，在训练实例上，预测误差比最好的机器学习（ML）方法低六倍，在分布外测试实例上，预测误差低 100 倍。此外，我们还将所提出的 ML 模型与元启发式方法相结合，创建了一个类似于枚举的解决方案框架，从而改进了大规模路由问题的解决方案。在求解时间和质量方面，我们的方法明显优于最先进的求解器，证明了我们的特征、模型和所提方法的潜力：本文已被交通科学特刊《大规模路线规划问题中的机器学习方法与应用》录用：在线附录请访问 https://doi.org/10.1287/trsc.2022.0015 。
{"title":"Neural Network Estimators for Optimal Tour Lengths of Traveling Salesperson Problem Instances with Arbitrary Node Distributions","authors":"Taha Varol, Okan Örsan Özener, Erinç Albey","doi":"10.1287/trsc.2022.0015","DOIUrl":"https://doi.org/10.1287/trsc.2022.0015","url":null,"abstract":"It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive-scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method.History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0015 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"80 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Several major companies operate large online order fulfillment systems to ship goods from fulfillment centers through a distribution network to customer destinations in response to purchase orders. These networks make several types of decisions in real-time to serve customers. First, when a customer places an order, when and where (which fulfillment center) is it fulfilled from? Second, once an order has been packaged, how is it moved through the network to get to the customer? Making optimal decisions can yield significant cost savings or improvements in customer service. Unfortunately, these are large optimization problems, and are furthermore subject to uncertainty in the products and destinations of customer orders and the inventory replenishment of the fulfillment centers. This uncertainty makes the problem difficult to solve to optimality. Although the problem can be modeled as a Markov decision process, solving it exactly using standard computational methods is not possible due to the curse of dimensionality. We propose an alternative approach to this problem. We define a relatively simple real-time control policy and prove that it serves all customer demand if at all possible. This proof is achieved using Lyapunov drift techniques to relate the real-time control performance to the average performance necessary to serve all customers on average. Correspondingly, we characterize the average network performance, which may be used for network topology design while the control policy adapts to real-time stochasticity. We demonstrate the performance and stability properties on a numerical example based on hundreds of Amazon facility locations in the United States. The max-pressure control and greedy policies perform similarly at low demands, but at higher demand the throughput properties of the max-pressure control manifest as improvements in throughput and customer service metrics.Funding: Financial support from the National Science Foundation [Grant 1935514] is gratefully acknowledged.Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0096 .
{"title":"A Real-Time Control Policy to Achieve Maximum Throughput of an Online Order Fulfillment Network","authors":"Michael Levin","doi":"10.1287/trsc.2023.0096","DOIUrl":"https://doi.org/10.1287/trsc.2023.0096","url":null,"abstract":"Several major companies operate large online order fulfillment systems to ship goods from fulfillment centers through a distribution network to customer destinations in response to purchase orders. These networks make several types of decisions in real-time to serve customers. First, when a customer places an order, when and where (which fulfillment center) is it fulfilled from? Second, once an order has been packaged, how is it moved through the network to get to the customer? Making optimal decisions can yield significant cost savings or improvements in customer service. Unfortunately, these are large optimization problems, and are furthermore subject to uncertainty in the products and destinations of customer orders and the inventory replenishment of the fulfillment centers. This uncertainty makes the problem difficult to solve to optimality. Although the problem can be modeled as a Markov decision process, solving it exactly using standard computational methods is not possible due to the curse of dimensionality. We propose an alternative approach to this problem. We define a relatively simple real-time control policy and prove that it serves all customer demand if at all possible. This proof is achieved using Lyapunov drift techniques to relate the real-time control performance to the average performance necessary to serve all customers on average. Correspondingly, we characterize the average network performance, which may be used for network topology design while the control policy adapts to real-time stochasticity. We demonstrate the performance and stability properties on a numerical example based on hundreds of Amazon facility locations in the United States. The max-pressure control and greedy policies perform similarly at low demands, but at higher demand the throughput properties of the max-pressure control manifest as improvements in throughput and customer service metrics.Funding: Financial support from the National Science Foundation [Grant 1935514] is gratefully acknowledged.Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0096 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"118 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanlu Zhao, Diego Cattaruzza, Ningxuan Kang, Roberto Roberti
Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call the traveling salesman problem with bike and robot (TSPBR), where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.
{"title":"Synchronized Deliveries with a Bike and a Self-Driving Robot","authors":"Yanlu Zhao, Diego Cattaruzza, Ningxuan Kang, Roberto Roberti","doi":"10.1287/trsc.2023.0169","DOIUrl":"https://doi.org/10.1287/trsc.2023.0169","url":null,"abstract":"Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call the traveling salesman problem with bike and robot (TSPBR), where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"11 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return to the station later on without too much range anxiety. However, because of the time-varying nature of the customer arrival and the complicated user behaviors, the battery quantity at each station is altered to guarantee the designated service level. However, little research has been done on the operations management of the e-bike battery-swapping system. To bridge the gap, we propose a nonstationary queueing network model to characterize the customer behaviors during the battery-swapping service. Then we develop a closed-form delayed infinite-server fluid approximation for the battery deployment of the one-time-loop scenario under various quality-of-service targets. In addition, we handle the infinite-time-loop scenario with the simulation-based iterative staffing algorithm. In the simulation study, we observe that the proposed battery deployment algorithms can help stabilize the system performance in terms of abandonment probability and expected delay in the face of time-varying demand and complex customer behaviors. Moreover, we reveal that the number of return loops correlates with the service level targets on the battery deployment decision. Furthermore, a time gap exists between the demand and the optimal battery deployment, making proactive battery management in the system possible.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271222, 71871203, 71872093, 72271137, L1924063], and the National Social Science Fund of China [Grant 21&ZD128].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0132 .
{"title":"Empowering the Capillary of the Urban Daily Commute: Battery Deployment Analysis for the Locker-Based E-bike Battery Swapping","authors":"Xiaolei Xie, Xu Dai, Zhi Pei","doi":"10.1287/trsc.2022.0132","DOIUrl":"https://doi.org/10.1287/trsc.2022.0132","url":null,"abstract":"In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return to the station later on without too much range anxiety. However, because of the time-varying nature of the customer arrival and the complicated user behaviors, the battery quantity at each station is altered to guarantee the designated service level. However, little research has been done on the operations management of the e-bike battery-swapping system. To bridge the gap, we propose a nonstationary queueing network model to characterize the customer behaviors during the battery-swapping service. Then we develop a closed-form delayed infinite-server fluid approximation for the battery deployment of the one-time-loop scenario under various quality-of-service targets. In addition, we handle the infinite-time-loop scenario with the simulation-based iterative staffing algorithm. In the simulation study, we observe that the proposed battery deployment algorithms can help stabilize the system performance in terms of abandonment probability and expected delay in the face of time-varying demand and complex customer behaviors. Moreover, we reveal that the number of return loops correlates with the service level targets on the battery deployment decision. Furthermore, a time gap exists between the demand and the optimal battery deployment, making proactive battery management in the system possible.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271222, 71871203, 71872093, 72271137, L1924063], and the National Social Science Fund of China [Grant 21&ZD128].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0132 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"67 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138546779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of couriers that continuously perform pickups and deliveries. Operating such a rapid delivery system is very challenging, primarily because of the high service expectations and the considerable uncertainty in both demand and delivery capacity. Delivery providers typically plan courier shifts for an operating period based on a demand forecast. However, because of the high demand volatility, it may at times during the operating period be necessary to adjust and dynamically add couriers. We study the problem of dynamically adding courier capacity in a rapid delivery system and propose a deep reinforcement-learning approach to obtain a policy that balances the cost of adding couriers and the cost-of-service quality degradation because of insufficient delivery capacity. Specifically, we seek to ensure that a high fraction of orders is delivered on time with a small number of courier hours. A computational study in the meal delivery space shows that a learned policy outperforms policies representing current practice and demonstrates the potential of deep learning for solving operational problems in highly stochastic logistic settings.History: This paper has been accepted for the Transportation Science Special Issue on Machine-Learning Methods and Applications in Large-Scale Route Planning Problems.Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo [72180404].Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0042 .
随着最近零工经济的蓬勃发展，城市运输系统的需求大幅增长。在这样的系统中，订单根据交付时间承诺从本地配送点交付给客户。一个重要的例子是餐厅的送餐系统，其送餐时间预计在下单后几分钟内完成。该系统通过利用快递员不断地进行取货和送货来服务订单。操作这样一个快速交付系统是非常具有挑战性的，主要是因为服务期望很高，需求和交付能力都有很大的不确定性。送货商通常根据需求预测来计划送货班次。然而，由于需求的高波动性，在运营期间有时可能需要调整和动态地增加快递员。本文研究了快速配送系统中动态增加快递员容量的问题，并提出了一种深度强化学习方法，以获得一种平衡增加快递员成本和由于配送能力不足而导致的服务质量成本下降的策略。具体来说，我们力求确保高比例的订单在少量的快递时间内按时交付。在送餐领域的一项计算研究表明，学习策略优于代表当前实践的策略，并展示了深度学习在解决高度随机逻辑设置中的操作问题方面的潜力。历史:本文已被《交通科学》专刊《机器学习方法及其在大规模路线规划问题中的应用》接受。资助:本研究得到了国家机构Investigación y Desarrollo[72180404]的支持。补充材料:电子伴侣可在https://doi.org/10.1287/trsc.2022.0042上获得。
{"title":"Dynamic Courier Capacity Acquisition in Rapid Delivery Systems: A Deep Q-Learning Approach","authors":"Ramon Auad, Alan Erera, Martin Savelsbergh","doi":"10.1287/trsc.2022.0042","DOIUrl":"https://doi.org/10.1287/trsc.2022.0042","url":null,"abstract":"With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of couriers that continuously perform pickups and deliveries. Operating such a rapid delivery system is very challenging, primarily because of the high service expectations and the considerable uncertainty in both demand and delivery capacity. Delivery providers typically plan courier shifts for an operating period based on a demand forecast. However, because of the high demand volatility, it may at times during the operating period be necessary to adjust and dynamically add couriers. We study the problem of dynamically adding courier capacity in a rapid delivery system and propose a deep reinforcement-learning approach to obtain a policy that balances the cost of adding couriers and the cost-of-service quality degradation because of insufficient delivery capacity. Specifically, we seek to ensure that a high fraction of orders is delivered on time with a small number of courier hours. A computational study in the meal delivery space shows that a learned policy outperforms policies representing current practice and demonstrates the potential of deep learning for solving operational problems in highly stochastic logistic settings.History: This paper has been accepted for the Transportation Science Special Issue on Machine-Learning Methods and Applications in Large-Scale Route Planning Problems.Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo [72180404].Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0042 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" 32","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138493964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights that must start and end at different customer locations. A drone serves exactly one customer per flight. Moreover, the truck and the drones must wait for each other at the landing locations. A customer prize can be collected at most once, either upon visiting it by the truck or upon serving it by a drone. Similarly to the OP, we maximize the total collected prize under the condition that the truck and the drones return to the depot within a given amount of time. We provide a mixed-integer linear programming formulation for the OP-mD and devise a tailored branch-and-cut algorithm based on a novel decomposition of the problem. We solve instances of the OP-mD with up to 50 nodes within one hour of CPU time with a standard computational setup. Finally, we adapt our framework to solve closely related problems in the literature and compare the resulting computational performance with that of previous studies.
{"title":"The Orienteering Problem with Drones","authors":"Nicola Morandi, Roel Leus, Hande Yaman","doi":"10.1287/trsc.2023.0003","DOIUrl":"https://doi.org/10.1287/trsc.2023.0003","url":null,"abstract":"We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights that must start and end at different customer locations. A drone serves exactly one customer per flight. Moreover, the truck and the drones must wait for each other at the landing locations. A customer prize can be collected at most once, either upon visiting it by the truck or upon serving it by a drone. Similarly to the OP, we maximize the total collected prize under the condition that the truck and the drones return to the depot within a given amount of time. We provide a mixed-integer linear programming formulation for the OP-mD and devise a tailored branch-and-cut algorithm based on a novel decomposition of the problem. We solve instances of the OP-mD with up to 50 nodes within one hour of CPU time with a standard computational setup. Finally, we adapt our framework to solve closely related problems in the literature and compare the resulting computational performance with that of previous studies.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"80 7","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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