Pub Date : 2024-08-06DOI: 10.1287/trsc.2024.erratum.v58.n5
Said Dabia, Stefan Ropke, Tom van Woensel
The aim of this erratum is to correct an error in the computer implementation of the algorithm proposed by Dabia, Ropke, and van Woensel [Dabia S, Ropke S, van Woensel T (2013) Branch and price for the time-dependent vehicle routing problem with time windows. Transportation Sci. 47(3):295–454]. Section 6 , “Computational Results,” from the original paper is rewritten to reflect the corrected implementation, the new computational setup, and the updated results.
本勘误旨在纠正 Dabia、Ropke 和 van Woensel [Dabia S、Ropke S、van Woensel T (2013) 带时间窗的随时间变化的车辆路由问题的分支和价格算法的计算机实现中的错误。运输科学》，47（3）：295-454]。重写了原论文的第 6 节 "计算结果"，以反映修正后的实现、新的计算设置和更新的结果。
{"title":"Correction to the Paper “Branch and Price for the Time-Dependent Vehicle Routing Problem with Time Windows”","authors":"Said Dabia, Stefan Ropke, Tom van Woensel","doi":"10.1287/trsc.2024.erratum.v58.n5","DOIUrl":"https://doi.org/10.1287/trsc.2024.erratum.v58.n5","url":null,"abstract":"The aim of this erratum is to correct an error in the computer implementation of the algorithm proposed by Dabia, Ropke, and van Woensel [Dabia S, Ropke S, van Woensel T (2013) Branch and price for the time-dependent vehicle routing problem with time windows. Transportation Sci. 47(3):295–454]. Section 6 , “Computational Results,” from the original paper is rewritten to reflect the corrected implementation, the new computational setup, and the updated results.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"16 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969553","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}
This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms.Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .
{"title":"Dynamic Operations of a Mobile Charging Crowdsourcing Platform","authors":"Yiming Yan, Xi Lin, Fang He, David Z. W. Wang","doi":"10.1287/trsc.2023.0126","DOIUrl":"https://doi.org/10.1287/trsc.2023.0126","url":null,"abstract":"This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms.Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"30 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969633","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}
Connected automated vehicles (CAVs) have the potential to improve the efficiency of vehicular traffic. In this paper, we discuss how CAVs can positively impact the dynamic behavior of mixed traffic systems on highways through the lens of nonlinear dynamics theory. First, we show that human-driven traffic exhibits a bistability phenomenon, in which the same drivers can both drive smoothly or cause congestion, depending on perturbations like a braking of an individual driver. As such, bistability can lead to unexpected phantom traffic jams, which are undesired. By analyzing the corresponding nonlinear dynamical model, we explain the mechanism of bistability and identify which human driver parameters may cause it. Second, we study mixed traffic that includes both human drivers and CAVs, and we analyze how CAVs affect the nonlinear dynamic behavior. We show that a large-enough penetration of CAVs in the traffic flow can eliminate bistability, and we identify the controller parameters of CAVs that are able to do so. Ultimately, this helps to achieve stable and smooth mobility on highways.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by the University of Michigan’s Center for Connected and Automated Transportation [U.S. DOT Grant 69A3551747105].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0498 .
{"title":"Destroying Phantom Jams with Connectivity and Automation: Nonlinear Dynamics and Control of Mixed Traffic","authors":"Tamas G. Molnar, Gábor Orosz","doi":"10.1287/trsc.2023.0498","DOIUrl":"https://doi.org/10.1287/trsc.2023.0498","url":null,"abstract":"Connected automated vehicles (CAVs) have the potential to improve the efficiency of vehicular traffic. In this paper, we discuss how CAVs can positively impact the dynamic behavior of mixed traffic systems on highways through the lens of nonlinear dynamics theory. First, we show that human-driven traffic exhibits a bistability phenomenon, in which the same drivers can both drive smoothly or cause congestion, depending on perturbations like a braking of an individual driver. As such, bistability can lead to unexpected phantom traffic jams, which are undesired. By analyzing the corresponding nonlinear dynamical model, we explain the mechanism of bistability and identify which human driver parameters may cause it. Second, we study mixed traffic that includes both human drivers and CAVs, and we analyze how CAVs affect the nonlinear dynamic behavior. We show that a large-enough penetration of CAVs in the traffic flow can eliminate bistability, and we identify the controller parameters of CAVs that are able to do so. Ultimately, this helps to achieve stable and smooth mobility on highways.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by the University of Michigan’s Center for Connected and Automated Transportation [U.S. DOT Grant 69A3551747105].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0498 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"17 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141773800","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 study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103 .
{"title":"Facility Location Problem: Modeling Joint Disruptions Using Subordination","authors":"Vishwakant Malladi, Kumar Muthuraman","doi":"10.1287/trsc.2023.0103","DOIUrl":"https://doi.org/10.1287/trsc.2023.0103","url":null,"abstract":"We study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"65 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754114","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 study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520 .
{"title":"A Paradox of Telecommuting and Staggered Work Hours in the Bottleneck Model","authors":"Takara Sakai, Takashi Akamatsu, Koki Satsukawa","doi":"10.1287/trsc.2024.0520","DOIUrl":"https://doi.org/10.1287/trsc.2024.0520","url":null,"abstract":"We study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"1 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754141","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}
As one of the most fundamental concepts in transportation science, Wardrop equilibrium (WE) has always had a relatively weak behavioral underpinning. To strengthen this foundation, one must reckon with bounded rationality in human decision-making processes, such as the lack of accurate information, limited computing power, and suboptimal choices. This retreat from behavioral perfectionism in the literature, however, was typically accompanied by a conceptual modification of WE. Here, we show that giving up perfect rationality need not force a departure from WE. On the contrary, WE can be reached with global stability in a routing game played by boundedly rational travelers. We achieve this result by developing a day-to-day (DTD) dynamical model that mimics how travelers gradually adjust their route valuations, hence choice probabilities, based on past experiences. Our model, called cumulative logit (CumLog), resembles the classical DTD models but makes a crucial change; whereas the classical models assume that routes are valued based on the cost averaged over historical data, our model values the routes based on the cost accumulated. To describe route choice behaviors, the CumLog model only uses two parameters, one accounting for the rate at which the future route cost is discounted in the valuation relative to the past ones and the other describing the sensitivity of route choice probabilities to valuation differences. We prove that the CumLog model always converges to WE, regardless of the initial point, as long as the behavioral parameters satisfy certain mild conditions. Our theory thus upholds WE’s role as a benchmark in transportation systems analysis. It also explains why equally good routes at equilibrium may be selected with different probabilities, which solves the instability problem posed by Harsanyi.Funding: This research is funded by the National Science Foundation [Grants CMMI #2225087 and ECCS #2048075].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0132 .
作为交通科学中最基本的概念之一，沃德普平衡（Wardrop equilibrium，WE）的行为基础一直相对薄弱。为了加强这一基础，我们必须考虑到人类决策过程中的有限理性，如缺乏准确信息、计算能力有限以及次优选择等。然而，文献中对行为完美主义的退却通常伴随着对 WE 概念的修改。在这里，我们表明，放弃完美理性并不一定要背离 WE。相反，在有界理性旅行者的路线博弈中，WE 可以达到全局稳定。我们通过建立一个日复一日（DTD）的动态模型来实现这一结果，该模型模仿了旅行者如何根据过去的经验逐步调整他们的路线估值，从而调整选择概率。我们的模型被称为累积 logit（CumLog），它与经典的 DTD 模型相似，但有一个关键的变化；经典模型假定路线的估值是基于历史数据的平均成本，而我们的模型则是基于累积的成本。为了描述路线选择行为，CumLog 模型只使用了两个参数，一个是未来路线成本相对于过去路线成本的估值贴现率，另一个是路线选择概率对估值差异的敏感度。我们证明，只要行为参数满足某些温和条件，无论初始点如何，CumLog 模型总是收敛于 WE。因此，我们的理论维护了 WE 在交通系统分析中的基准地位。它还解释了为什么在平衡状态下，同样好的路线可能会以不同的概率被选择，这就解决了哈桑尼提出的不稳定性问题：本研究由美国国家科学基金会资助[Grants CMMI #2225087 and ECCS #2048075]：在线附录见 https://doi.org/10.1287/trsc.2023.0132 。
{"title":"Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choice","authors":"Jiayang Li, Zhaoran Wang, Yu (Marco) Nie","doi":"10.1287/trsc.2023.0132","DOIUrl":"https://doi.org/10.1287/trsc.2023.0132","url":null,"abstract":"As one of the most fundamental concepts in transportation science, Wardrop equilibrium (WE) has always had a relatively weak behavioral underpinning. To strengthen this foundation, one must reckon with bounded rationality in human decision-making processes, such as the lack of accurate information, limited computing power, and suboptimal choices. This retreat from behavioral perfectionism in the literature, however, was typically accompanied by a conceptual modification of WE. Here, we show that giving up perfect rationality need not force a departure from WE. On the contrary, WE can be reached with global stability in a routing game played by boundedly rational travelers. We achieve this result by developing a day-to-day (DTD) dynamical model that mimics how travelers gradually adjust their route valuations, hence choice probabilities, based on past experiences. Our model, called cumulative logit (CumLog), resembles the classical DTD models but makes a crucial change; whereas the classical models assume that routes are valued based on the cost averaged over historical data, our model values the routes based on the cost accumulated. To describe route choice behaviors, the CumLog model only uses two parameters, one accounting for the rate at which the future route cost is discounted in the valuation relative to the past ones and the other describing the sensitivity of route choice probabilities to valuation differences. We prove that the CumLog model always converges to WE, regardless of the initial point, as long as the behavioral parameters satisfy certain mild conditions. Our theory thus upholds WE’s role as a benchmark in transportation systems analysis. It also explains why equally good routes at equilibrium may be selected with different probabilities, which solves the instability problem posed by Harsanyi.Funding: This research is funded by the National Science Foundation [Grants CMMI #2225087 and ECCS #2048075].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0132 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"39 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745355","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}
To mitigate the negative effects of freight vehicles on urban areas, many cities have implemented road accessibility restrictions, including limited traffic zones, which restrict access to specific areas during certain times of the day. Implementing these zones creates a tradeoff between the delivery cost and time, even under the assumption of equal traversal time and travel cost. Consequently, the planners in charge of vehicle routing need to work with graphs containing information on all Pareto-optimal paths. Inspired by these changes in city logistics and the resulting computational challenges, we study the vehicle routing problem with access restrictions, where some streets are closed to traffic within a given time period. We formulate this problem using workday variables and propose two branch and price algorithms based on the underlying road network and multigraph. The results of our computational experiments demonstrate the effectiveness of the proposed algorithms, solving instances with up to 100 nodes and 33 customers, and underline the importance of considering alternative paths in reducing costs.Funding: This work was supported by KU Leuven [C14/22/026].
{"title":"The Vehicle Routing Problem with Access Restrictions","authors":"Munise Kübra Şahin, Hande Yaman","doi":"10.1287/trsc.2023.0261","DOIUrl":"https://doi.org/10.1287/trsc.2023.0261","url":null,"abstract":"To mitigate the negative effects of freight vehicles on urban areas, many cities have implemented road accessibility restrictions, including limited traffic zones, which restrict access to specific areas during certain times of the day. Implementing these zones creates a tradeoff between the delivery cost and time, even under the assumption of equal traversal time and travel cost. Consequently, the planners in charge of vehicle routing need to work with graphs containing information on all Pareto-optimal paths. Inspired by these changes in city logistics and the resulting computational challenges, we study the vehicle routing problem with access restrictions, where some streets are closed to traffic within a given time period. We formulate this problem using workday variables and propose two branch and price algorithms based on the underlying road network and multigraph. The results of our computational experiments demonstrate the effectiveness of the proposed algorithms, solving instances with up to 100 nodes and 33 customers, and underline the importance of considering alternative paths in reducing costs.Funding: This work was supported by KU Leuven [C14/22/026].","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"9 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745365","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 this paper, we study a sequential decision-making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the expected number of parcels that can be delivered during service hours. We propose two reinforcement learning (RL) approaches for solving this problem. These approaches rely on a look-ahead strategy in which future release dates are sampled in a Monte Carlo fashion, and a batch approach is used to approximate future routes. Both RL approaches are based on value function approximation: One combines it with a consensus function (VFA-CF) and the other one with a two-stage stochastic integer linear programming model (VFA-2S). VFA-CF and VFA-2S do not need extensive training as they are based on very few hyperparameters and make good use of integer linear programming (ILP) and branch-and-cut–based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into VFA-CF/VFA-2S. In an empirical study, we conduct a competitive analysis using upper bounds with perfect information. We also show that VFA-CF and VFA-2S greatly outperform alternative approaches that (1) do not rely on future information (2) are based on point estimation of future information, (3) use heuristics rather than exact methods, or (4) use exact evaluations of future rewards.Funding: This work was supported by the CY Initiative of Excellence [ANR-16- IDEX-0008].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0366 .
{"title":"Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates","authors":"Yuanyuan Li, Claudia Archetti, Ivana Ljubić","doi":"10.1287/trsc.2022.0366","DOIUrl":"https://doi.org/10.1287/trsc.2022.0366","url":null,"abstract":"In this paper, we study a sequential decision-making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the expected number of parcels that can be delivered during service hours. We propose two reinforcement learning (RL) approaches for solving this problem. These approaches rely on a look-ahead strategy in which future release dates are sampled in a Monte Carlo fashion, and a batch approach is used to approximate future routes. Both RL approaches are based on value function approximation: One combines it with a consensus function (VFA-CF) and the other one with a two-stage stochastic integer linear programming model (VFA-2S). VFA-CF and VFA-2S do not need extensive training as they are based on very few hyperparameters and make good use of integer linear programming (ILP) and branch-and-cut–based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into VFA-CF/VFA-2S. In an empirical study, we conduct a competitive analysis using upper bounds with perfect information. We also show that VFA-CF and VFA-2S greatly outperform alternative approaches that (1) do not rely on future information (2) are based on point estimation of future information, (3) use heuristics rather than exact methods, or (4) use exact evaluations of future rewards.Funding: This work was supported by the CY Initiative of Excellence [ANR-16- IDEX-0008].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0366 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"52 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746420","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}
Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
We propose a method for learning decision makers’ behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers’ decisions in routing problems.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This work was supported by the European Research Council [TRUST-949796].
{"title":"Inverse Optimization for Routing Problems","authors":"Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy","doi":"10.1287/trsc.2023.0241","DOIUrl":"https://doi.org/10.1287/trsc.2023.0241","url":null,"abstract":"We propose a method for learning decision makers’ behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers’ decisions in routing problems.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This work was supported by the European Research Council [TRUST-949796].","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"93 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745351","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}
Many goods and services require the customer to be at home to receive the delivery. In the context of attended home delivery, customers can typically choose from a menu of delivery time slots. We consider the problem of dynamically managing the offered time slots and delivery bookings given the available fleet capacity. When multiple customers interact with the online booking system at the same time, this can lead to conflicts. Although managing such concurrent interactions is an important challenge in attended home delivery systems, it has not yet been addressed in the literature. We present a concurrency control strategy and several fast route planning approaches to manage time slots in real time. To combine fast response times with high quality slotting decisions, we introduce background procedures that use the time between successive order placements to improve the performance of the time slot offer and validation procedures. Our detailed computational experiments based on realistic instances provide insights into the effectiveness of our background procedures and the complex trade-offs between waiting times, valid orders, and invalid orders. We also discuss several relevant new areas of research in concurrency control for time slot management.Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 438-213-204]. It is co-funded by ORTEC B.V. and AH.nl.
许多商品和服务需要顾客在家才能收到。在上门送货的情况下，客户通常可以从送货时段菜单中进行选择。我们考虑的问题是在车队可用容量的情况下，动态管理所提供的时段和送货预订。当多个客户同时与在线预订系统交互时，可能会导致冲突。尽管管理这种并发交互是参与送货上门系统的一个重要挑战，但文献中尚未涉及这一问题。我们提出了一种并发控制策略和几种快速路线规划方法来实时管理时间段。为了将快速响应时间与高质量时隙决策结合起来，我们引入了后台程序，利用连续下单之间的时间来提高时隙报价和验证程序的性能。我们根据实际情况进行了详细的计算实验，深入了解了背景程序的有效性，以及等待时间、有效订单和无效订单之间的复杂权衡。我们还讨论了时隙管理并发控制的几个相关新研究领域：这项工作得到了 Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 438-213-204] 的支持。它由 ORTEC B.V. 和 AH.nl 共同资助。
{"title":"Managing Concurrent Interactions in Online Time Slot Booking Systems for Attended Home Delivery","authors":"Thomas R. Visser, Niels Agatz, Remy Spliet","doi":"10.1287/trsc.2022.0445","DOIUrl":"https://doi.org/10.1287/trsc.2022.0445","url":null,"abstract":"Many goods and services require the customer to be at home to receive the delivery. In the context of attended home delivery, customers can typically choose from a menu of delivery time slots. We consider the problem of dynamically managing the offered time slots and delivery bookings given the available fleet capacity. When multiple customers interact with the online booking system at the same time, this can lead to conflicts. Although managing such concurrent interactions is an important challenge in attended home delivery systems, it has not yet been addressed in the literature. We present a concurrency control strategy and several fast route planning approaches to manage time slots in real time. To combine fast response times with high quality slotting decisions, we introduce background procedures that use the time between successive order placements to improve the performance of the time slot offer and validation procedures. Our detailed computational experiments based on realistic instances provide insights into the effectiveness of our background procedures and the complex trade-offs between waiting times, valid orders, and invalid orders. We also discuss several relevant new areas of research in concurrency control for time slot management.Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 438-213-204]. It is co-funded by ORTEC B.V. and AH.nl.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"47 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745352","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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