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 .
本文以道路网络的修复和恢复为背景，探讨了单个施工人员的调度和路由问题，这对于协助人道主义物流环境中复杂的灾后决策至关重要。我们为这一问题提出了三种新的表述方法，这些方法首次适用于列生成和分支-价格（BP）算法。具体来说，我们的第一种方法是在明确定义救灾路径的同时，枚举机组人员时间表和路线。第二种方法依赖于枚举计划、路线和救援路径。最后，第三种方法在第二种方法的基础上增加了与救援路径决策相关的额外约束和变量。考虑到每种方案，我们都提出了 BP 算法，这些算法依赖于若干改进，包括一种新的动态编程标记算法，以高效解决子问题。基于 648 个基准实例的广泛计算结果显示，我们的 BP 算法明显优于现有的精确算法，其中 450 个实例达到最优解，118 个实例首次达到最优解。我们的框架还能有效改善文献中报道的下限、上限和最优性差距：本研究得到了圣保罗州研究基金[15/26453-7、16/01860-1、16/15966-6 和 19/23596-2 号基金]、国家科学与技术发展委员会[313220/2020-4 号基金]和高级研究人员奖学金委员会的资助：在线附录见 https://doi.org/10.1287/trsc.2023.0227 。
{"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":null,"pages":null},"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}
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 .
{"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":null,"pages":null},"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}
Chenhao Zhou, Aloisius Stephen, Kok Choon Tan, Ek Peng Chew, Loo Hay Lee
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 .
{"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":null,"pages":null},"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}
Lu Zhen, Dan Zhuge, Shuanglu Zhang, Shuaian Wang, H. Psaraftis
The design of emission control areas (ECAs), including ECA width and sulfur limits, plays a central role in reducing sulfur emissions from shipping. To promote sustainable shipping, we investigate an ECA design problem that considers the response of liner shipping companies to ECA designs. We propose a mathematical programming model from the regulator’s perspective to optimize the ECA width and sulfur limit, with the aim of minimizing the total sulfur emissions. Embedded within this regulator’s model, we develop an internal model from the shipping liner’s perspective to determine the detoured voyage, sailing speed, and cargo transport volume with the aim of maximizing the liner’s profit. Then, we develop a tailored hybrid algorithm to solve the proposed models based on the variable neighborhood search meta-heuristic and a proposition. We validate the effectiveness of the proposed methodology through extensive numerical experiments and conduct sensitivity analyses to investigate the effect of important ECA design parameters on the final performance. The proposed methodology is then extended to incorporate heterogeneous settings for sulfur limits, which can help regulators to improve ECA design in the future. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72025103, 71831008, 72201163, 72071173, 72371221, 72394360, 72394362, 72361137001 and HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0278 .
{"title":"Optimizing Sulfur Emission Control Areas for Shipping","authors":"Lu Zhen, Dan Zhuge, Shuanglu Zhang, Shuaian Wang, H. Psaraftis","doi":"10.1287/trsc.2023.0278","DOIUrl":"https://doi.org/10.1287/trsc.2023.0278","url":null,"abstract":"The design of emission control areas (ECAs), including ECA width and sulfur limits, plays a central role in reducing sulfur emissions from shipping. To promote sustainable shipping, we investigate an ECA design problem that considers the response of liner shipping companies to ECA designs. We propose a mathematical programming model from the regulator’s perspective to optimize the ECA width and sulfur limit, with the aim of minimizing the total sulfur emissions. Embedded within this regulator’s model, we develop an internal model from the shipping liner’s perspective to determine the detoured voyage, sailing speed, and cargo transport volume with the aim of maximizing the liner’s profit. Then, we develop a tailored hybrid algorithm to solve the proposed models based on the variable neighborhood search meta-heuristic and a proposition. We validate the effectiveness of the proposed methodology through extensive numerical experiments and conduct sensitivity analyses to investigate the effect of important ECA design parameters on the final performance. The proposed methodology is then extended to incorporate heterogeneous settings for sulfur limits, which can help regulators to improve ECA design in the future. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72025103, 71831008, 72201163, 72071173, 72371221, 72394360, 72394362, 72361137001 and HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0278 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140759689","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}
Pengli Mo, Zhiyuan Liu, Zhijia Tan, Wen Yi, Pan Liu
Typically, governments subcontract the operation of urban bus systems to several bus operators. In particular, the government aims to promote the service quality for passengers by introducing competition among bus operators and subsidizes bus operations to ensure affordable fares. However, most existing studies about subsidy allocation typically do not account for the competitive factors among bus operators and thus may underestimate the associated benefits. In this study, we investigate how the government allocates subsidies to minimize social costs, taking into account the competition among bus operators and passenger route decisions. We describe this problem as a trilevel optimization model and use a game-theoretic approach to characterize the market equilibrium of bus operators. Next, we transform the trilevel model into a mixed-integer programming problem with quadratic constraints and solve it using an exact algorithm with acceleration techniques. The results of numerical experiments demonstrate the computational efficiency of the proposed algorithm. Several valuable insights are derived: First, lines served by competing bus operators typically do not require subsidies. Second, competitive behavior decreases social costs (including bus operating costs and passenger travel costs) more effectively in cities in which the passengers assign higher value to time. Third, the competitive behavior may be guided by exogenous parameters, such as ticket prices, to approximate the optimum of urban bus systems. Funding: This work was supported by the Key Project [Grant 52131203], Youth Program [Grant 72301065], and Project of International Cooperation and Exchanges [Grant 72361137006] of the National Natural Science Foundation of China.
{"title":"Subsidy Allocation Problem with Bus Frequency Setting Game: A Trilevel Formulation and Exact Algorithm","authors":"Pengli Mo, Zhiyuan Liu, Zhijia Tan, Wen Yi, Pan Liu","doi":"10.1287/trsc.2023.0037","DOIUrl":"https://doi.org/10.1287/trsc.2023.0037","url":null,"abstract":"Typically, governments subcontract the operation of urban bus systems to several bus operators. In particular, the government aims to promote the service quality for passengers by introducing competition among bus operators and subsidizes bus operations to ensure affordable fares. However, most existing studies about subsidy allocation typically do not account for the competitive factors among bus operators and thus may underestimate the associated benefits. In this study, we investigate how the government allocates subsidies to minimize social costs, taking into account the competition among bus operators and passenger route decisions. We describe this problem as a trilevel optimization model and use a game-theoretic approach to characterize the market equilibrium of bus operators. Next, we transform the trilevel model into a mixed-integer programming problem with quadratic constraints and solve it using an exact algorithm with acceleration techniques. The results of numerical experiments demonstrate the computational efficiency of the proposed algorithm. Several valuable insights are derived: First, lines served by competing bus operators typically do not require subsidies. Second, competitive behavior decreases social costs (including bus operating costs and passenger travel costs) more effectively in cities in which the passengers assign higher value to time. Third, the competitive behavior may be guided by exogenous parameters, such as ticket prices, to approximate the optimum of urban bus systems. Funding: This work was supported by the Key Project [Grant 52131203], Youth Program [Grant 72301065], and Project of International Cooperation and Exchanges [Grant 72361137006] of the National Natural Science Foundation of China.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366084","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}
Nour ElHouda Tellache, Frédéric Meunier, Axel Parmentier
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 .
{"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":null,"pages":null},"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}
Leon Lan, Jasper M. H. van Doorn, Niels A. Wouda, Arpan Rijal, Sandjai Bhulai
A challenge in same-day delivery operations is that delivery requests are typically not known beforehand, but are instead revealed dynamically during the day. This uncertainty introduces a trade-off between dispatching vehicles to serve requests as soon as they are revealed to ensure timely delivery and delaying the dispatching decision to consolidate routing decisions with future, currently unknown requests. In this paper, we study the dynamic dispatch waves problem, a same-day delivery problem in which vehicles are dispatched at fixed decision moments. At each decision moment, the system operator must decide which of the known requests to dispatch and how to route these dispatched requests. The operator’s goal is to minimize the total routing cost while ensuring that all requests are served on time. We propose iterative conditional dispatch (ICD), an iterative solution construction procedure based on a sample scenario approach. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until a final dispatching decision is made in the last iteration. We develop two variants of ICD: one variant based on thresholds, and another variant based on similarity. A significant strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: through rigorous numerical experiments, we show that both variants efficiently navigate the large state and action spaces of the dynamic dispatch waves problem and quickly converge to a high-quality solution. Finally, we demonstrate that the threshold-based ICD variant achieves excellent results on instances from the EURO Meets NeurIPS 2022 Vehicle Routing Competition, nearly matching the performance of the winning machine learning–based strategy.History: This paper has been accepted for the Transportation Science Special Issue on DIMACS Implementation Challenge: Vehicle Routing Problems.Funding: This work was supported by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0111 .
{"title":"An Iterative Sample Scenario Approach for the Dynamic Dispatch Waves Problem","authors":"Leon Lan, Jasper M. H. van Doorn, Niels A. Wouda, Arpan Rijal, Sandjai Bhulai","doi":"10.1287/trsc.2023.0111","DOIUrl":"https://doi.org/10.1287/trsc.2023.0111","url":null,"abstract":"A challenge in same-day delivery operations is that delivery requests are typically not known beforehand, but are instead revealed dynamically during the day. This uncertainty introduces a trade-off between dispatching vehicles to serve requests as soon as they are revealed to ensure timely delivery and delaying the dispatching decision to consolidate routing decisions with future, currently unknown requests. In this paper, we study the dynamic dispatch waves problem, a same-day delivery problem in which vehicles are dispatched at fixed decision moments. At each decision moment, the system operator must decide which of the known requests to dispatch and how to route these dispatched requests. The operator’s goal is to minimize the total routing cost while ensuring that all requests are served on time. We propose iterative conditional dispatch (ICD), an iterative solution construction procedure based on a sample scenario approach. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until a final dispatching decision is made in the last iteration. We develop two variants of ICD: one variant based on thresholds, and another variant based on similarity. A significant strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: through rigorous numerical experiments, we show that both variants efficiently navigate the large state and action spaces of the dynamic dispatch waves problem and quickly converge to a high-quality solution. Finally, we demonstrate that the threshold-based ICD variant achieves excellent results on instances from the EURO Meets NeurIPS 2022 Vehicle Routing Competition, nearly matching the performance of the winning machine learning–based strategy.History: This paper has been accepted for the Transportation Science Special Issue on DIMACS Implementation Challenge: Vehicle Routing Problems.Funding: This work was supported by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0111 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140303277","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}
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 .
{"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":null,"pages":null},"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}
This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneously optimizing the average speed and operation mode for each road segment in the route. Two mixed-integer nonlinear programming models are proposed for the problem: one with continuous speed decision variables and one with discretized variables. Because the models are nonlinear, we propose reformulation schemes and introduce valid inequalities to strengthen them. We also describe a branch-and-cut algorithm to solve these reformulations. Extensive numerical experiments are performed to demonstrate the algorithm’s performance in terms of computing time and energy consumption costs. Specifically, the proposed solution method can efficiently solve instances with a realistic number of customers and outperforms the benchmark approaches from the literature. Integrating speed optimization into the TSP of a PHEV can lead to significant energy savings compared with the fixed-speed TSP. In addition, the proposed model is extended to investigate the impact of the presence of charging stations, which makes the problem harder to solve but has the potential to further reduce energy consumption costs. Funding: F. Wu gratefully acknowledges the support of the National Natural Science Foundation of China [Grant 72271161]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0247 .
{"title":"Modeling and Solving the Traveling Salesman Problem with Speed Optimization for a Plug-In Hybrid Electric Vehicle","authors":"Fuliang Wu, Y. Adulyasak, J-F Cordeau","doi":"10.1287/trsc.2023.0247","DOIUrl":"https://doi.org/10.1287/trsc.2023.0247","url":null,"abstract":"This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneously optimizing the average speed and operation mode for each road segment in the route. Two mixed-integer nonlinear programming models are proposed for the problem: one with continuous speed decision variables and one with discretized variables. Because the models are nonlinear, we propose reformulation schemes and introduce valid inequalities to strengthen them. We also describe a branch-and-cut algorithm to solve these reformulations. Extensive numerical experiments are performed to demonstrate the algorithm’s performance in terms of computing time and energy consumption costs. Specifically, the proposed solution method can efficiently solve instances with a realistic number of customers and outperforms the benchmark approaches from the literature. Integrating speed optimization into the TSP of a PHEV can lead to significant energy savings compared with the fixed-speed TSP. In addition, the proposed model is extended to investigate the impact of the presence of charging stations, which makes the problem harder to solve but has the potential to further reduce energy consumption costs. Funding: F. Wu gratefully acknowledges the support of the National Natural Science Foundation of China [Grant 72271161]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0247 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411880","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 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.
{"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":null,"pages":null},"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}
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