The proliferation of emerging mobility technology has led to a significant increase in demand for ride-hailing services, on-demand deliveries, and micromobility services, transforming curb spaces into valuable public infrastructure for which multimodal transportation competes. However, the increasing utilization of curbs by different traffic modes has substantial societal impacts, further altering travelers’ choices and polluting the urban environment. Integrating the spatiotemporal characteristics of various behaviors related to curb utilization into general dynamic networks and exploring mobility patterns with multisource data remain a challenge. To address this issue, this study proposes a comprehensive framework of modeling curbside usage by multimodal transportation in a general dynamic network. The framework encapsulates route choices, curb space competition, and interactive effects among different curb users, and it embeds the dynamics of curb usage into a mesoscopic dynamic network model. Furthermore, a curb-aware dynamic origin-destination demand estimation framework is proposed to reveal the network-level spatiotemporal mobility patterns associated with curb usage through a physics-informed data-driven approach. The framework integrates emerging real-world curb use data in conjunction with other mobility data represented on computational graphs, which can be solved efficiently using the forward-backward algorithm on large-scale networks. The framework is examined on a small network as well as a large-scale real-world network. The estimation results on both networks are satisfactory and compelling, demonstrating the capability of the framework to estimate the spatiotemporal curb usage by multimodal transportation.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25.Funding: This material is based upon work supported by the Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy [Award DE-EE0009659].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0522 .
{"title":"Modeling Multimodal Curbside Usage in Dynamic Networks","authors":"Jiachao Liu, Sean Qian","doi":"10.1287/trsc.2024.0522","DOIUrl":"https://doi.org/10.1287/trsc.2024.0522","url":null,"abstract":"The proliferation of emerging mobility technology has led to a significant increase in demand for ride-hailing services, on-demand deliveries, and micromobility services, transforming curb spaces into valuable public infrastructure for which multimodal transportation competes. However, the increasing utilization of curbs by different traffic modes has substantial societal impacts, further altering travelers’ choices and polluting the urban environment. Integrating the spatiotemporal characteristics of various behaviors related to curb utilization into general dynamic networks and exploring mobility patterns with multisource data remain a challenge. To address this issue, this study proposes a comprehensive framework of modeling curbside usage by multimodal transportation in a general dynamic network. The framework encapsulates route choices, curb space competition, and interactive effects among different curb users, and it embeds the dynamics of curb usage into a mesoscopic dynamic network model. Furthermore, a curb-aware dynamic origin-destination demand estimation framework is proposed to reveal the network-level spatiotemporal mobility patterns associated with curb usage through a physics-informed data-driven approach. The framework integrates emerging real-world curb use data in conjunction with other mobility data represented on computational graphs, which can be solved efficiently using the forward-backward algorithm on large-scale networks. The framework is examined on a small network as well as a large-scale real-world network. The estimation results on both networks are satisfactory and compelling, demonstrating the capability of the framework to estimate the spatiotemporal curb usage by multimodal transportation.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25.Funding: This material is based upon work supported by the Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy [Award DE-EE0009659].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0522 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571812","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}
Jiayang Li, Qianni Wang, Liyang Feng, Jun Xie, Yu (Marco) Nie
The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution, is often used to address the challenge. Built on a recently proposed day-to-day discrete-time dynamical model called cumulative logit (CumLog), this study provides a new behavioral underpinning for the maximum-entropy user equilibrium (MEUE) route flow. It has been proven that CumLog can reach a UE state without presuming that travelers are perfectly rational. Here, we further establish that CumLog always converges to the MEUE route flow if (i) travelers have no prior information about routes and thus, are forced to give all routes an equal initial choice probability or if (ii) all travelers gather information from the same source such that the general proportionality condition is satisfied. Thus, CumLog may be used as a practical solution algorithm for the MEUE problem. To put this idea into practice, we propose to eliminate the route enumeration requirement of the original CumLog model through an iterative route discovery scheme. We also examine the discrete-time versions of four popular continuous-time dynamical models and compare them with CumLog. The analysis shows that the replicator dynamic is the only one that has the potential to reach the MEUE solution with some regularity. The analytical results are confirmed through numerical experiments.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This research was funded by the United States National Science Foundation’s Division of Civil, Mechanical and Manufacturing Innovation [Grant 2225087]. The work of J. Xie was funded by the National Natural Science Foundation of China [Grant 72371205].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0525 .
{"title":"A Day-to-Day Dynamical Approach to the Most Likely User Equilibrium Problem","authors":"Jiayang Li, Qianni Wang, Liyang Feng, Jun Xie, Yu (Marco) Nie","doi":"10.1287/trsc.2024.0525","DOIUrl":"https://doi.org/10.1287/trsc.2024.0525","url":null,"abstract":"The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution, is often used to address the challenge. Built on a recently proposed day-to-day discrete-time dynamical model called cumulative logit (CumLog), this study provides a new behavioral underpinning for the maximum-entropy user equilibrium (MEUE) route flow. It has been proven that CumLog can reach a UE state without presuming that travelers are perfectly rational. Here, we further establish that CumLog always converges to the MEUE route flow if (i) travelers have no prior information about routes and thus, are forced to give all routes an equal initial choice probability or if (ii) all travelers gather information from the same source such that the general proportionality condition is satisfied. Thus, CumLog may be used as a practical solution algorithm for the MEUE problem. To put this idea into practice, we propose to eliminate the route enumeration requirement of the original CumLog model through an iterative route discovery scheme. We also examine the discrete-time versions of four popular continuous-time dynamical models and compare them with CumLog. The analysis shows that the replicator dynamic is the only one that has the potential to reach the MEUE solution with some regularity. The analytical results are confirmed through numerical experiments.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This research was funded by the United States National Science Foundation’s Division of Civil, Mechanical and Manufacturing Innovation [Grant 2225087]. The work of J. Xie was funded by the National Natural Science Foundation of China [Grant 72371205].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0525 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571808","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 describes an exact branch-and-cut (B&C) algorithm for the split delivery vehicle routing problem. The underlying model is based on a previously proposed two-index vehicle flow formulation that models a relaxation of the problem. We dynamically separate two well-known classes of valid inequalities, namely capacity and connectivity cuts, and use an in-out algorithm to improve the convergence of the cutting phase. We generate no-good cuts from feasible integer solutions to the relaxation using a recently proposed single-commodity flow formulation in the literature. The exact methodology is complemented by a very effective adaptive large neighborhood search (ALNS) heuristic that provides high-quality upper bounds to initiate the B&C algorithm. Key ingredients in the design of the heuristic include the use of a tailored construction algorithm, which can exploit the situation in which the ratio of the number of customers to the minimum number of vehicles needed is low, and the use of a route-based formulation to improve the solutions found before, during, and after the ALNS procedure. An earlier version of this work was submitted to the DIMACS (Center for Discrete Mathematics and Theoretical Computer Science) implementation challenge, where it placed third. On sets of well-known benchmark instances for limited and unlimited fleet variants of the problem, we demonstrate that the heuristic provides very competitive solutions, with respective average gaps of 0.19% and 0.18% from best-known values. Furthermore, the exact B&C framework is also highly competitive with state-of-the-art methods, providing solutions with an average optimality gap of 1.82%.History: This paper has been accepted for the Transportation Science Special Section on DIMACS Implementation Challenge: Vehicle Routing Problems.Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0353 .
{"title":"Exact and Heuristic Methods for the Split Delivery Vehicle Routing Problem","authors":"Mette Gamst, Richard Martin Lusby, Stefan Ropke","doi":"10.1287/trsc.2022.0353","DOIUrl":"https://doi.org/10.1287/trsc.2022.0353","url":null,"abstract":"This paper describes an exact branch-and-cut (B&C) algorithm for the split delivery vehicle routing problem. The underlying model is based on a previously proposed two-index vehicle flow formulation that models a relaxation of the problem. We dynamically separate two well-known classes of valid inequalities, namely capacity and connectivity cuts, and use an in-out algorithm to improve the convergence of the cutting phase. We generate no-good cuts from feasible integer solutions to the relaxation using a recently proposed single-commodity flow formulation in the literature. The exact methodology is complemented by a very effective adaptive large neighborhood search (ALNS) heuristic that provides high-quality upper bounds to initiate the B&C algorithm. Key ingredients in the design of the heuristic include the use of a tailored construction algorithm, which can exploit the situation in which the ratio of the number of customers to the minimum number of vehicles needed is low, and the use of a route-based formulation to improve the solutions found before, during, and after the ALNS procedure. An earlier version of this work was submitted to the DIMACS (Center for Discrete Mathematics and Theoretical Computer Science) implementation challenge, where it placed third. On sets of well-known benchmark instances for limited and unlimited fleet variants of the problem, we demonstrate that the heuristic provides very competitive solutions, with respective average gaps of 0.19% and 0.18% from best-known values. Furthermore, the exact B&C framework is also highly competitive with state-of-the-art methods, providing solutions with an average optimality gap of 1.82%.History: This paper has been accepted for the Transportation Science Special Section on DIMACS Implementation Challenge: Vehicle Routing Problems.Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0353 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571710","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 presents a modeling framework for optimizing operational protocols of extra-long trains (XLTs) in metro systems (i.e., trains longer than station platforms). With the rising travel demand in megacities, metro systems face challenges such as overcrowded stations, delays, and passenger anxieties. XLTs have been proposed as a promising solution to increase metro line capacity without additional infrastructure construction. The study explores the trade-offs between the additional capacity gained through complex protocols, the potential benefits of protocols with inline transfers, and the importance of effective passenger information systems from both passengers’ and operators’ perspectives. Mathematical programs are proposed to optimize protocols for a given demand distribution and to estimate the maximum line capacity of an XLT system. The benefits of implementing XLTs are evaluated in hypothetical and real-world cases with varying demand distributions and network sizes. The results demonstrate significant capacity increases ranging from 24% to 126% as compared with regular train operations, depending on system parameters and demand distribution. These findings demonstrate promise for using such systems to improve metro line capacity in the real world. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT 25 Conference. Funding: The work was supported in part by the University of Illinois, Urbana Champaign [Grant Grainger STII Seed Fund] and the Zhejiang University-University of Illinois Urbana-Champaign Institute [Grant DREMES-202001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0527 .
{"title":"Planning Service Protocols for Extra-Long Trains with Transfers","authors":"Jesus Osorio, S. Shen, Yanfeng Ouyang","doi":"10.1287/trsc.2024.0527","DOIUrl":"https://doi.org/10.1287/trsc.2024.0527","url":null,"abstract":"This paper presents a modeling framework for optimizing operational protocols of extra-long trains (XLTs) in metro systems (i.e., trains longer than station platforms). With the rising travel demand in megacities, metro systems face challenges such as overcrowded stations, delays, and passenger anxieties. XLTs have been proposed as a promising solution to increase metro line capacity without additional infrastructure construction. The study explores the trade-offs between the additional capacity gained through complex protocols, the potential benefits of protocols with inline transfers, and the importance of effective passenger information systems from both passengers’ and operators’ perspectives. Mathematical programs are proposed to optimize protocols for a given demand distribution and to estimate the maximum line capacity of an XLT system. The benefits of implementing XLTs are evaluated in hypothetical and real-world cases with varying demand distributions and network sizes. The results demonstrate significant capacity increases ranging from 24% to 126% as compared with regular train operations, depending on system parameters and demand distribution. These findings demonstrate promise for using such systems to improve metro line capacity in the real world. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT 25 Conference. Funding: The work was supported in part by the University of Illinois, Urbana Champaign [Grant Grainger STII Seed Fund] and the Zhejiang University-University of Illinois Urbana-Champaign Institute [Grant DREMES-202001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0527 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352196","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}
When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110 .
在处理高维、基于连续模拟的优化（SO）问题时，平衡探索与利用之间的关系非常重要。以往的 SO 研究大多集中在开发技术的改进上。SO 算法的探索技术通常被定义为通用的采样分布，如均匀分布，而均匀分布在搜索高维空间时效率较低。这项工作的动机是制定适用于大规模交通网络问题和高维优化问题的探索技术。我们制定了一种采样机制，将反向累积分布函数采样与底层交通问题的特定问题结构信息相结合。根据分析网络模型的定义，提议的采样分布会为预期性能更好的点分配更大的采样概率。在一个玩具网络上进行的验证实验表明，所提出的采样分布与模拟器的基本且通常未知的真实采样分布具有重要的共性。我们研究了纽约曼哈顿中城的高维交通信号控制案例。结果表明，作为 SO 框架的一部分，使用所提出的采样机制有助于高效地找出性能良好的解决方案。使用分析信息进行探索，无论是否用于开发，都优于不使用分析信息的基准，包括标准贝叶斯优化。仅在探索中使用分析信息所得到的解决方案与仅在利用中使用分析信息所得到的解决方案性能相似，总计算时间减少了 65%。本文揭示了开发合适的探索技术对于提高通用 SO 算法的可扩展性和计算效率的重要性。资助：T. Tay 感谢新加坡科技研究局（A*STAR）对其工作的资助。补充材料：在线附录见 https://doi.org/10.1287/trsc.2023.0110 。
{"title":"A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems","authors":"Timothy Tay, Carolina Osorio","doi":"10.1287/trsc.2023.0110","DOIUrl":"https://doi.org/10.1287/trsc.2023.0110","url":null,"abstract":"When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363192","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}
The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can help meet the growing demand for curb space more effectively and promote user turnover. To model curbside operations, we formulate a Stackelberg leader-follower game between a leader operating curbside spaces, who sets space allocation and pricing of each curbside usage, and multi-followers, one for each type of curbside usage, who accept the proposed prices or reject them in favor of outside options. The proposed model offers flexible adaptability to manage curb space usages characterized by high turnover rates, such as parking and ride-sourcing pickup and drop-off, alongside accommodating usages that require more permanent infrastructure allocation, such as micromobility stations. Furthermore, the proposed model is able to capture the sensitivity of users to both prices, which are determined solely by the operator, and the occupancy levels of the curb space, which are determined by the complex interactions between the curbside operator and the users. We model a Stackelberg leader-follower game as a bilevel nonlinear optimization problem and reconstruct the problem into a single-level convex program by applying the Karush-Kuhn-Tucker conditions, objective function transformation, and constraint linearization. Then, we develop a solution algorithm that leverages valid inequalities produced via Benders decomposition. We validate the practicability of the model and draw insights into curbside management using numerical experiments. History: This paper has been accepted for the Transportation Sci. Special Issue on the ISTTT25 Conference. Funding: This work was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046372]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0507 .
{"title":"Dynamic Usage Allocation and Pricing for Curb Space Operation","authors":"Jisoon Lim, Neda Masoud","doi":"10.1287/trsc.2024.0507","DOIUrl":"https://doi.org/10.1287/trsc.2024.0507","url":null,"abstract":"The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can help meet the growing demand for curb space more effectively and promote user turnover. To model curbside operations, we formulate a Stackelberg leader-follower game between a leader operating curbside spaces, who sets space allocation and pricing of each curbside usage, and multi-followers, one for each type of curbside usage, who accept the proposed prices or reject them in favor of outside options. The proposed model offers flexible adaptability to manage curb space usages characterized by high turnover rates, such as parking and ride-sourcing pickup and drop-off, alongside accommodating usages that require more permanent infrastructure allocation, such as micromobility stations. Furthermore, the proposed model is able to capture the sensitivity of users to both prices, which are determined solely by the operator, and the occupancy levels of the curb space, which are determined by the complex interactions between the curbside operator and the users. We model a Stackelberg leader-follower game as a bilevel nonlinear optimization problem and reconstruct the problem into a single-level convex program by applying the Karush-Kuhn-Tucker conditions, objective function transformation, and constraint linearization. Then, we develop a solution algorithm that leverages valid inequalities produced via Benders decomposition. We validate the practicability of the model and draw insights into curbside management using numerical experiments. History: This paper has been accepted for the Transportation Sci. Special Issue on the ISTTT25 Conference. Funding: This work was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046372]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0507 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378202","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}
Urban logistic applications that involve pickup and distribution of goods require making routing and allocation decisions with respect to a set of sites. In cases where the supply quantities and the time in which they become available are unknown in advance, these decisions must be determined in real time based on information that arrives gradually. Furthermore, in many applications that satisfy the described setting, fair allocation is desired in addition to system effectiveness. In this paper, we consider the problem of determining a vehicle route that visits two types of sites in any order: pickup points (PPs), from which the vehicle collects supplies, and demand points (DPs), to which these supplies are delivered. The supply quantities offered by each PP are uncertain, and the information on their value arrives gradually over time. We model this problem as a stochastic dynamic routing and resource allocation problem, with the aim of delivering as many goods as possible while obtaining equitable allocations to DPs. We present a Markov decision process formulation for the problem; however, it suffers from the curse of dimensionality. Therefore, we develop a heuristic framework that presents a novel combination of operations research and machine learning and is applicable for many dynamic stochastic combinatorial optimization problems. Specifically, we use a large neighborhood search (LNS) to explore possible decisions combined with a neural network (NN) model that approximates the future value given any state and action. We present a new reinforcement learning method to train the NN when the decision space is too large to enumerate. A numerical experiment with 38 to 180 site instances, based on data from the Berlin Foodbank and randomly generated data sets, confirms that the heuristic obtains solutions that are on average approximately 28.2%, 41.6%, and 57.9% better than three benchmark solutions.Funding: This research was partially supported by the Israel Science Foundation [Grant 463/15], by the Shlomo Shmeltzer Institute for Smart Transportation at Tel Aviv University, by the Israeli Smart Transportation Research Center (ISTRC), and by the Council for Higher Education in Israel (VATAT).Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0228 .
{"title":"The Dynamic Pickup and Allocation with Fairness Problem","authors":"Gal Neria, Michal Tzur","doi":"10.1287/trsc.2023.0228","DOIUrl":"https://doi.org/10.1287/trsc.2023.0228","url":null,"abstract":"Urban logistic applications that involve pickup and distribution of goods require making routing and allocation decisions with respect to a set of sites. In cases where the supply quantities and the time in which they become available are unknown in advance, these decisions must be determined in real time based on information that arrives gradually. Furthermore, in many applications that satisfy the described setting, fair allocation is desired in addition to system effectiveness. In this paper, we consider the problem of determining a vehicle route that visits two types of sites in any order: pickup points (PPs), from which the vehicle collects supplies, and demand points (DPs), to which these supplies are delivered. The supply quantities offered by each PP are uncertain, and the information on their value arrives gradually over time. We model this problem as a stochastic dynamic routing and resource allocation problem, with the aim of delivering as many goods as possible while obtaining equitable allocations to DPs. We present a Markov decision process formulation for the problem; however, it suffers from the curse of dimensionality. Therefore, we develop a heuristic framework that presents a novel combination of operations research and machine learning and is applicable for many dynamic stochastic combinatorial optimization problems. Specifically, we use a large neighborhood search (LNS) to explore possible decisions combined with a neural network (NN) model that approximates the future value given any state and action. We present a new reinforcement learning method to train the NN when the decision space is too large to enumerate. A numerical experiment with 38 to 180 site instances, based on data from the Berlin Foodbank and randomly generated data sets, confirms that the heuristic obtains solutions that are on average approximately 28.2%, 41.6%, and 57.9% better than three benchmark solutions.Funding: This research was partially supported by the Israel Science Foundation [Grant 463/15], by the Shlomo Shmeltzer Institute for Smart Transportation at Tel Aviv University, by the Israeli Smart Transportation Research Center (ISTRC), and by the Council for Higher Education in Israel (VATAT).Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0228 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190721","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}
Zhizhen Qin, Peng Yang, Yeming Gong, R. D. de Koster
Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .
{"title":"Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems","authors":"Zhizhen Qin, Peng Yang, Yeming Gong, R. D. de Koster","doi":"10.1287/trsc.2023.0397","DOIUrl":"https://doi.org/10.1287/trsc.2023.0397","url":null,"abstract":"Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100337","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}
Yinhu Wang, Ye Chen, I. Ryzhov, Xiaoyue Cathy Liu, Nikola Marković
We consider a two-stage planning problem where a fleet of snowplow trucks is divided among a set of independent regions, each of which then designs routes for efficient snow removal. The central authority wishes to allocate trucks to improve service quality across the regions. Stochasticity is introduced by uncertain weather conditions and unforeseen failures of snowplow trucks. We study two versions of this problem. The first aims to minimize the maximum turnaround time (across all regions) that can be sustained with a user-specified probability. The second seeks to minimize the total expected workload that has not been completed within a user-specified time frame. We develop algorithms that solve these problems effectively and demonstrate their practical value through a case application to snowplowing operations in Utah, obtaining solutions that significantly outperform the allocation currently used in practice. Funding: Financial support from the Utah Department of Transportation [Grant 218138]; the National Science Foundation [Grant CMMI-2112758]; and the Mountain-Plains Consortium [Grant 637] is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0024 .
{"title":"Resource Allocation in an Uncertain Environment: Application to Snowplowing Operations in Utah","authors":"Yinhu Wang, Ye Chen, I. Ryzhov, Xiaoyue Cathy Liu, Nikola Marković","doi":"10.1287/trsc.2023.0024","DOIUrl":"https://doi.org/10.1287/trsc.2023.0024","url":null,"abstract":"We consider a two-stage planning problem where a fleet of snowplow trucks is divided among a set of independent regions, each of which then designs routes for efficient snow removal. The central authority wishes to allocate trucks to improve service quality across the regions. Stochasticity is introduced by uncertain weather conditions and unforeseen failures of snowplow trucks. We study two versions of this problem. The first aims to minimize the maximum turnaround time (across all regions) that can be sustained with a user-specified probability. The second seeks to minimize the total expected workload that has not been completed within a user-specified time frame. We develop algorithms that solve these problems effectively and demonstrate their practical value through a case application to snowplowing operations in Utah, obtaining solutions that significantly outperform the allocation currently used in practice. Funding: Financial support from the Utah Department of Transportation [Grant 218138]; the National Science Foundation [Grant CMMI-2112758]; and the Mountain-Plains Consortium [Grant 637] is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0024 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110213","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}
Danial Khorasanian, Jonathan Patrick, Antoine Sauré
Despite the rapid growth of the home care industry, research on the scheduling and routing of home care visits in the presence of uncertainty is still limited. This paper investigates a dynamic version of this problem in which the number of referrals and their required number of visits are uncertain. We develop a Markov decision process (MDP) model for the single-nurse problem to minimize the expected weighted sum of the rejection, diversion, overtime, and travel time costs. Because optimally solving the MDP is intractable, we employ an approximate linear program (ALP) to obtain a feasible policy. The typical ALP approach can only solve very small-scale instances of the problem. We derive an intuitively explainable closed-form solution for the optimal ALP parameters in a special case of the problem. Inspired by this form, we provide two heuristic reduction techniques for the ALP model in the general problem to solve large-scale instances in an acceptable time. Numerical results show that the ALP policy outperforms a myopic policy that reflects current practice, and is better than a scenario-based policy in most instances considered.Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-05225 and RGPIN-2020-210524] and by the Telfer School of Management SMRG Postdoctoral Research Fellowship Support [Grant 2020].Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2023.0120 .
尽管家庭护理行业发展迅速，但对存在不确定性的家庭护理访问安排和路由的研究仍然有限。本文研究了这一问题的动态版本，其中转介人数及其所需的访问次数是不确定的。我们为单护士问题开发了一个马尔可夫决策过程（MDP）模型，以最小化拒绝、分流、加班和旅行时间成本的预期加权和。由于马尔可夫决策过程的优化求解难以实现，我们采用了近似线性程序 (ALP) 来获得可行的策略。典型的 ALP 方法只能解决非常小规模的问题实例。在该问题的一个特例中，我们推导出了一个可直观解释的闭式最优 ALP 参数解。受这种形式的启发，我们为一般问题中的 ALP 模型提供了两种启发式简化技术，以在可接受的时间内解决大规模实例。数值结果表明，ALP 政策优于反映当前实践的近视政策，并且在考虑的大多数实例中优于基于情景的政策：这项工作得到了加拿大自然科学与工程研究理事会 [RGPIN-2018-05225 和 RGPIN-2020-210524] 以及特尔弗管理学院 SMRG 博士后研究奖学金 [2020] 的支持：电子版附录见 https://doi.org/10.1287/trsc.2023.0120 。
{"title":"Dynamic Home Care Routing and Scheduling with Uncertain Number of Visits per Referral","authors":"Danial Khorasanian, Jonathan Patrick, Antoine Sauré","doi":"10.1287/trsc.2023.0120","DOIUrl":"https://doi.org/10.1287/trsc.2023.0120","url":null,"abstract":"Despite the rapid growth of the home care industry, research on the scheduling and routing of home care visits in the presence of uncertainty is still limited. This paper investigates a dynamic version of this problem in which the number of referrals and their required number of visits are uncertain. We develop a Markov decision process (MDP) model for the single-nurse problem to minimize the expected weighted sum of the rejection, diversion, overtime, and travel time costs. Because optimally solving the MDP is intractable, we employ an approximate linear program (ALP) to obtain a feasible policy. The typical ALP approach can only solve very small-scale instances of the problem. We derive an intuitively explainable closed-form solution for the optimal ALP parameters in a special case of the problem. Inspired by this form, we provide two heuristic reduction techniques for the ALP model in the general problem to solve large-scale instances in an acceptable time. Numerical results show that the ALP policy outperforms a myopic policy that reflects current practice, and is better than a scenario-based policy in most instances considered.Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-05225 and RGPIN-2020-210524] and by the Telfer School of Management SMRG Postdoctoral Research Fellowship Support [Grant 2020].Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2023.0120 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935360","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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