Transportation Science最新文献

{"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}

{"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}

{"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":"90 1","pages":""},"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}

{"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":"24 1","pages":""},"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}

{"title":"Crew Scheduling and Routing Problem in Road Restoration via Branch-and-Price Algorithms","authors":"Alfredo Moreno, Pedro Munari, Douglas Alem","doi":"10.1287/trsc.2023.0227","DOIUrl":"https://doi.org/10.1287/trsc.2023.0227","url":null,"abstract":"This paper addresses the single crew scheduling and routing problem in the context of road network repair and restoration, which is critical in assisting complex postdisaster decisions in humanitarian logistics settings. We present three novel formulations for this problem, which are the first suitable for column generation and branch-and-price (BP) algorithms. Specifically, our first formulation is based on enumerating crew schedules and routes while explicitly defining the relief paths. The second formulation relies on enumerating the schedules, routes, and relief paths. Finally, the third formulation builds upon the second one by including additional constraints and variables related to relief path decisions. Considering each formulation, we propose BP algorithms that rely on several enhancements, including a new dynamic programming labeling algorithm to efficiently solve the subproblems. Extensive computational results based on 648 benchmark instances reveal that our BP algorithms significantly outperform existing exact approaches, solving 450 instances to optimality, and remarkably 118 instances for the first time. Our framework is also very effective in improving the lower bounds, upper bounds, and optimality gaps that have been reported in the literature.Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 15/26453-7, 16/01860-1, 16/15966-6, and 19/23596-2], the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant 313220/2020-4], and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0227 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"17 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935051","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}

{"title":"Estimating Markov Chain Mixing Times: Convergence Rate Towards Equilibrium of a Stochastic Process Traffic Assignment Model","authors":"Takamasa Iryo, David Watling, Martin Hazelton","doi":"10.1287/trsc.2024.0523","DOIUrl":"https://doi.org/10.1287/trsc.2024.0523","url":null,"abstract":"Network equilibrium models have been extensively used for decades. The rationale for using equilibrium as a predictor is essentially that (i) a unique and globally stable equilibrium point is guaranteed to exist and (ii) the transient period over which a system adapts to a change is sufficiently short in time that it can be neglected. However, we find transport problems without a unique and stable equilibrium in the literature. Even if it exists, it is not certain how long it takes for the system to reach an equilibrium point after an external shock onto the transport system, such as infrastructure improvement and damage by a disaster. The day-to-day adjustment process must be analysed to answer these questions. Among several models, the Markov chain approach has been claimed to be the most general and flexible. It is also advantageous as a unique stationary distribution is guaranteed in mild conditions, even when a unique and stable equilibrium does not exist. In the present paper, we first aim to develop a methodology for estimating the Markov chain mixing time (MCMT), a worst-case assessment of the convergence time of a Markov chain to its stationary distribution. The main tools are coupling and aggregation, which enable us to analyse MCMTs in large-scale transport systems. Our second aim is to conduct a preliminary examination of the relationships between MCMTs and critical properties of the system, such as travellers’ sensitivity to differences in travel cost and the frequency of travellers’ revisions of their choices. Through analytical and numerical analyses, we found key relationships in a few transport problems, including those without a unique and stable equilibrium. We also showed that the proposed method, combined with coupling and aggregation, can be applied to larger transport models.History: This paper has been accepted for the Transportation Science Special Issue on the 25th International Symposium on Transportation and Traffic Theory.Funding: This study was financially supported by the Japan Society for the Promotion of Science [Grant-in-Aid 20H00265].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0523 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"77 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935170","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}

{"title":"Multiagent Q-Learning Approach for the Recharging Scheduling of Electric Automated Guided Vehicles in Container Terminals","authors":"Chenhao Zhou, Aloisius Stephen, Kok Choon Tan, Ek Peng Chew, Loo Hay Lee","doi":"10.1287/trsc.2022.0113","DOIUrl":"https://doi.org/10.1287/trsc.2022.0113","url":null,"abstract":"In recent years, advancements in battery technology have led to increased adoption of electric automated guided vehicles in container terminals. Given how critical these vehicles are to terminal operations, this trend requires efficient recharging scheduling for automated guided vehicles, and the main challenges arise from limited charging station capacity and tight vehicle schedules. Motivated by the dynamic nature of the problem, the recharging scheduling problem for an entire vehicle fleet given capacitated stations is formulated as a Markov decision process model. Then, it is solved using a multiagent Q-learning (MAQL) approach to produce a recharging schedule that minimizes the delay of jobs. Numerical experiments show that under a stochastic environment in terms of vehicle travel time, MAQL enables the exploration of better scheduling by coordinating across the entire vehicle fleet and charging facilities and outperforms various benchmark approaches, with an additional improvement of 18.8% on average over the best rule-based heuristic and 5.4% over the predetermined approach.Funding: This work was supported by the National Natural Science Foundation of China [Grant 72101203], the Shaanxi Provincial Key R&D Program, China [Grant 2022KW-02], and the Singapore Maritime Institute [Grant SMI-2017-SP-002].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0113 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"46 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569406","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}

{"title":"Linear Lexicographic Optimization and Preferential Bidding System","authors":"Nour ElHouda Tellache, Frédéric Meunier, Axel Parmentier","doi":"10.1287/trsc.2022.0372","DOIUrl":"https://doi.org/10.1287/trsc.2022.0372","url":null,"abstract":"Some airlines use the preferential bidding system to construct the schedules of their pilots. In this system, the pilots bid on the different activities and the schedules that lexicographically maximize the scores of the pilots according to their seniority are selected. A sequential approach to solve this maximization problem is natural: The problem is first solved with the bids of the most senior pilot, and then it is solved with those of the second most senior without decreasing the score of the most senior, and so on. The literature admits that the structure of the problem somehow imposes such an approach. The problem can be modeled as an integer linear lexicographic program. We propose a new efficient method, which relies on column generation for solving its continuous relaxation and returns proven optimality gaps. To design this column generation, we prove that bounded linear lexicographic programs admit “primal-dual” feasible bases, and we show how to compute such bases efficiently. Another contribution on which our method relies is the extension of standard tools for resource-constrained longest path problems to their lexicographic versions. This is useful in our context because the generation of new columns is modeled as a lexicographic resource-constrained longest path problem. Numerical experiments show that this new method is already able to solve to proven optimality industrial instances provided by Air France, with up to 150 pilots. By adding a last ingredient in the resolution of the longest path problems, which exploits the specificity of the preferential bidding system, the method achieves for these instances computational times that are compatible with operational constraints.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0372 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"28 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197562","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}

{"title":"On the Efficiency Impacts of Berthing Priority Provision","authors":"Xi Lin, Xinyue Pu, Xiwen Bai","doi":"10.1287/trsc.2022.0411","DOIUrl":"https://doi.org/10.1287/trsc.2022.0411","url":null,"abstract":"Facing intensified interport competition in the global container shipping market, an increasing number of ports choose to offer berthing priority for carriers to increase their attractiveness. This study is the first to theoretically analyze the efficiency impacts of such prioritization. Specifically, this study models the steady-state dynamics for each terminal in a biterminal port as a prioritized queuing system. We explore the equilibrated shipping flow distribution and resulting total system cost (i.e., bunker consumption cost and waiting time cost) with and without priority provision, along with their major analytical properties. Then, we examine the “second-order” effects of these priority schemes on just-in-time (JIT) arrivals, an increasingly popular green port management tool. Specifically, we investigate how the equilibrium state associated with JIT arrivals could change with priority berthing. These analyses generate some interesting results, including (1) the total system cost increases or remains unchanged when a priority scheme is implemented under a symmetric port with equal service capacities for both terminals; (2) under the asymmetric biterminal case, however, it is also possible that berth prioritization could reduce the total system cost, and such phenomenon occurs only if the terminal which offers prioritization owns larger service capacity; (3) the results indicate that the “price of prioritization” could reach [Formula: see text] in port operation when the berth loading is heavy, implying that priority provision may significantly harm the operational efficiency; and (4) lastly, priority provision has a negative second-order effect on JIT strategies in a symmetric port, and such negative effect may neutralize the positive ones. Those theoretical results are validated by numerical experiments, and some of them are also supported by empirical data. The results provide important practical implications for the decision making of the port (or terminal) agencies.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371143 and 72188101].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0411 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"18 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019239","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}

{"title":"Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences","authors":"Luigi De Giovanni, Carlo Lancia, Guglielmo Lulli","doi":"10.1287/trsc.2022.0309","DOIUrl":"https://doi.org/10.1287/trsc.2022.0309","url":null,"abstract":"In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"83 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025104","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}