Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach’s performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system. Funding: This work was supported by Türkiye Bilimsel ve Teknolojik Araştırma Kurumu [Grant 218M520]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0112 .
我们的研究介绍了考虑拥塞、容量和随机需求的枢纽网络设计问题（HNDC），该问题将经典的枢纽位置问题从几个方向推广。特别是，我们通过将容量获取决策和拥塞成本效应集成到问题中，并允许始发-目的地（OD）对的动态路由，来扩展最先进的技术。HNDC将战略和运营层面的决策联系起来，通过考虑预期的路线和拥堵成本，共同决定枢纽位置和容量收购。提出了一种基于路径的混合整数二阶锥规划（SOCP）HNDC公式。我们利用SOCP对偶结果，提出了一种基于Benders分解和列生成的精确算法来解决这个具有挑战性的问题。我们使用容量可行解的特定特征来加快求解过程，并开发了一种有效的分支和切割算法来解决主问题。我们进行了大量的计算实验，以测试所提出的方法的性能，并根据改编自文献的现实问题实例得出管理见解。特别是，我们发现，包括集线器拥塞成本、考虑需求的不确定性以及底层网络是完整的还是不完整的，都会对集线器网络设计和由此产生的系统性能产生重大影响。资金：这项工作得到了土耳其Bilmel ve Teknologik AraşTırma Kurumu的支持[拨款218M520]。补充材料：在线附录可在https://doi.org/10.1287/trsc.2022.0112。
{"title":"Hub Network Design Problem with Capacity, Congestion, and Stochastic Demand Considerations","authors":"Vedat Bayram, Barış Yıldız, M. Farham","doi":"10.1287/trsc.2022.0112","DOIUrl":"https://doi.org/10.1287/trsc.2022.0112","url":null,"abstract":"Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach’s performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system. Funding: This work was supported by Türkiye Bilimsel ve Teknolojik Araştırma Kurumu [Grant 218M520]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0112 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45191734","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}
Qi Luo, V. Nagarajan, A. Sundt, Yafeng Yin, J. Vincent, M. Shahabi
Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to substantially enhance the throughput of mobility-on-demand (MoD) systems. This paper investigates MoD systems that operate mixed fleets composed of “basic supply” and “augmented supply” vehicles. When the basic supply is insufficient to satisfy demand, augmented supply vehicles can be repositioned to serve rides at a higher operational cost. We formulate the joint vehicle repositioning and ride-pooling assignment problem as a two-stage stochastic integer program, where repositioning augmented supply vehicles precedes the realization of ride requests. Sequential ride-pooling assignments aim to maximize total utility or profit on a shareability graph: a hypergraph representing the matching compatibility between available vehicles and pending requests. Two approximation algorithms for midcapacity and high-capacity vehicles are proposed in this paper; the respective approximation ratios are [Formula: see text] and [Formula: see text], where p is the maximum vehicle capacity plus one. Our study evaluates the performance of these approximation algorithms using an MoD simulator, demonstrating that these algorithms can parallelize computations and achieve solutions with small optimality gaps (typically within 1%). These efficient algorithms pave the way for various multimodal and multiclass MoD applications. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the National Science Foundation [Grants CCF-2006778 and FW-HTF-P 2222806], the Ford Motor Company, and the Division of Civil, Mechanical, and Manufacturing Innovation [Grants CMMI-1854684, CMMI-1904575, and CMMI-1940766]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0349 .
{"title":"Efficient Algorithms for Stochastic Ride-Pooling Assignment with Mixed Fleets","authors":"Qi Luo, V. Nagarajan, A. Sundt, Yafeng Yin, J. Vincent, M. Shahabi","doi":"10.1287/trsc.2021.0349","DOIUrl":"https://doi.org/10.1287/trsc.2021.0349","url":null,"abstract":"Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to substantially enhance the throughput of mobility-on-demand (MoD) systems. This paper investigates MoD systems that operate mixed fleets composed of “basic supply” and “augmented supply” vehicles. When the basic supply is insufficient to satisfy demand, augmented supply vehicles can be repositioned to serve rides at a higher operational cost. We formulate the joint vehicle repositioning and ride-pooling assignment problem as a two-stage stochastic integer program, where repositioning augmented supply vehicles precedes the realization of ride requests. Sequential ride-pooling assignments aim to maximize total utility or profit on a shareability graph: a hypergraph representing the matching compatibility between available vehicles and pending requests. Two approximation algorithms for midcapacity and high-capacity vehicles are proposed in this paper; the respective approximation ratios are [Formula: see text] and [Formula: see text], where p is the maximum vehicle capacity plus one. Our study evaluates the performance of these approximation algorithms using an MoD simulator, demonstrating that these algorithms can parallelize computations and achieve solutions with small optimality gaps (typically within 1%). These efficient algorithms pave the way for various multimodal and multiclass MoD applications. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the National Science Foundation [Grants CCF-2006778 and FW-HTF-P 2222806], the Ford Motor Company, and the Division of Civil, Mechanical, and Manufacturing Innovation [Grants CMMI-1854684, CMMI-1904575, and CMMI-1940766]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0349 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41934202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before takeoff. Such tasks must be performed within time windows and require a team of workers with different skill levels. The goal is to find a feasible plan that minimizes the sum of the tasks completion times. We formalize a variation of the workforce scheduling and routing problem, integrating team formation, hierarchical skills with downgrading, multiple trips, and different execution modes. We propose a solution approach based on branch-and-price-and-check and test it on real-world instances from a major European hub airport. We propose a model based on the Dantzig–Wolfe decomposition. In the pricing problem, we generate tours of teams as shortest paths with constrained resources in a network. In the master problem, we select an optimal set of tours that do not exceed the workforce availability. Our experiments show that the proposed algorithm can produce optimal solutions for small- and medium-sized instances and good or optimal solutions for large instances. The results also show that our approach outperforms the current airport dispatching policy. Funding: G. Dall’Olio was funded by the Deutsche Forschungsgemeinschaft [Grant Advanced Optimization in a Networked Economy Graduiertenkolleg 2201, Project 277991500]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0110 .
我们致力于优化机场地勤服务的人力资源，重点是行李装卸。技术熟练的工人队伍必须在飞机着陆后从停机坪上卸下行李，并在起飞前装载行李。这样的任务必须在时间窗口内完成，并且需要一组具有不同技能水平的工人。目标是找到一个可行的计划，使任务完成时间的总和最小化。我们形式化了劳动力调度和路由问题的一个变体，将团队形成、分层技能与降级、多次旅行和不同的执行模式集成在一起。我们提出了一种基于分支-价格-检查的解决方案，并在欧洲主要枢纽机场的实际实例中进行了测试。我们提出了一个基于dantzigg - wolfe分解的模型。在定价问题中，我们生成团队旅行作为网络中资源受限的最短路径。在主问题中，我们选择一组不超过劳动力可用性的最优旅行。我们的实验表明，所提出的算法可以对中小型实例产生最优解，对大型实例产生良好或最优解。结果还表明，我们的方法优于现行的机场调度政策。资助:G. Dall 'Olio由Deutsche Forschungsgemeinschaft资助[Grant Advanced Optimization in a Networked Economy Graduiertenkolleg 2201, Project 277991500]。补充材料:在线附录可在https://doi.org/10.1287/trsc.2022.0110上获得。
{"title":"Formation and Routing of Worker Teams for Airport Ground Handling Operations: A Branch-and-Price-and-Check Approach","authors":"Giacomo Dall’Olio, R. Kolisch","doi":"10.1287/trsc.2022.0110","DOIUrl":"https://doi.org/10.1287/trsc.2022.0110","url":null,"abstract":"We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before takeoff. Such tasks must be performed within time windows and require a team of workers with different skill levels. The goal is to find a feasible plan that minimizes the sum of the tasks completion times. We formalize a variation of the workforce scheduling and routing problem, integrating team formation, hierarchical skills with downgrading, multiple trips, and different execution modes. We propose a solution approach based on branch-and-price-and-check and test it on real-world instances from a major European hub airport. We propose a model based on the Dantzig–Wolfe decomposition. In the pricing problem, we generate tours of teams as shortest paths with constrained resources in a network. In the master problem, we select an optimal set of tours that do not exceed the workforce availability. Our experiments show that the proposed algorithm can produce optimal solutions for small- and medium-sized instances and good or optimal solutions for large instances. The results also show that our approach outperforms the current airport dispatching policy. Funding: G. Dall’Olio was funded by the Deutsche Forschungsgemeinschaft [Grant Advanced Optimization in a Networked Economy Graduiertenkolleg 2201, Project 277991500]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0110 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47873928","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}
Knowledge of the connected vehicle (CV) penetration rate is crucial for realizing numerous beneficial applications during the prolonged transition period to full CV deployment. A recent study described a novel single-source data penetration rate estimator (SSDPRE) for estimating the CV penetration rate solely from CV data. However, despite the unbiasedness of the SSDPRE, it is only a point estimator. Consequently, given the typically nonlinear nature of transportation systems, model estimations or system optimizations conducted with the SSDPRE without considering its variability can generate biased models or suboptimal solutions. Thus, this study proposes a probabilistic penetration rate model for estimating the variability of the results generated by the SSDPRE. An essential input for this model is the constrained queue length distribution, which is the distribution of the number of stopping vehicles in a signal cycle. An exact probabilistic dissipation time model and a simplified constant dissipation time model are developed for estimating this distribution. In addition, to improve the estimation accuracy in real-world situations, the braking and start-up motions of vehicles are considered by constructing a constant time loss model for use in calibrating the dissipation time models. VISSIM simulation demonstrates that the calibrated models accurately describe constrained queue length distributions and estimate the variability of the results generated by the SSDPRE. Furthermore, applications of the calibrated models to the next-generation simulation data set and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations. Funding: This work was supported by The University of Hong Kong [Francis S Y Bong Professorship in Engineering and Postgraduate Scholarship] and by the Council of the Hong Kong Special Administrative Region, China [Grants 17204919 and 17205822]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.1209 .
在向全面部署车联网的漫长过渡期间，了解车联网普及率对于实现众多有益应用至关重要。最近的一项研究描述了一种新的单源数据渗透率估计器(SSDPRE)，用于仅从CV数据估计CV渗透率。然而，尽管SSDPRE具有无偏性，但它只是一个点估计器。因此，考虑到运输系统的典型非线性性质，使用SSDPRE进行的模型估计或系统优化而不考虑其可变性可能会产生有偏差的模型或次优解。因此，本研究提出了一个概率渗透率模型来估计SSDPRE产生的结果的可变性。该模型的一个重要输入是约束队列长度分布，即一个信号周期内停车车辆数量的分布。建立了精确的概率耗散时间模型和简化的常数耗散时间模型来估计这种分布。此外，为了提高实际情况下的估计精度，考虑了车辆的制动和启动运动，构建了一个常数时间损失模型，用于校准耗散时间模型。VISSIM仿真结果表明，校正后的模型能够准确地描述受约束的队列长度分布，并估计出由SSDPRE生成的结果的可变性。此外，将校准后的模型应用于下一代仿真数据集和简单的基于cv的自适应信号控制方案，证明了该模型可用于实际情况，并具有改善系统优化的潜力。资助:本研究由香港大学[Francis S Y Bong工程学教授及研究生奖学金]及中国香港特别行政区政府[拨款17204919及17205822]资助。补充材料:在线附录可在https://doi.org/10.1287/trsc.2023.1209上获得。
{"title":"Uncertainty Estimation of Connected Vehicle Penetration Rate","authors":"Shaocheng Jia, S. Wong, W. Wong","doi":"10.1287/trsc.2023.1209","DOIUrl":"https://doi.org/10.1287/trsc.2023.1209","url":null,"abstract":"Knowledge of the connected vehicle (CV) penetration rate is crucial for realizing numerous beneficial applications during the prolonged transition period to full CV deployment. A recent study described a novel single-source data penetration rate estimator (SSDPRE) for estimating the CV penetration rate solely from CV data. However, despite the unbiasedness of the SSDPRE, it is only a point estimator. Consequently, given the typically nonlinear nature of transportation systems, model estimations or system optimizations conducted with the SSDPRE without considering its variability can generate biased models or suboptimal solutions. Thus, this study proposes a probabilistic penetration rate model for estimating the variability of the results generated by the SSDPRE. An essential input for this model is the constrained queue length distribution, which is the distribution of the number of stopping vehicles in a signal cycle. An exact probabilistic dissipation time model and a simplified constant dissipation time model are developed for estimating this distribution. In addition, to improve the estimation accuracy in real-world situations, the braking and start-up motions of vehicles are considered by constructing a constant time loss model for use in calibrating the dissipation time models. VISSIM simulation demonstrates that the calibrated models accurately describe constrained queue length distributions and estimate the variability of the results generated by the SSDPRE. Furthermore, applications of the calibrated models to the next-generation simulation data set and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations. Funding: This work was supported by The University of Hong Kong [Francis S Y Bong Professorship in Engineering and Postgraduate Scholarship] and by the Council of the Hong Kong Special Administrative Region, China [Grants 17204919 and 17205822]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.1209 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41507878","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 the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This article is part of a special issue: Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207 .
{"title":"Human-Robot Cooperation: Coordinating Autonomous Mobile Robots and Human Order Pickers","authors":"Maximilian Löffler, N. Boysen, Michael Schneider","doi":"10.1287/trsc.2023.1207","DOIUrl":"https://doi.org/10.1287/trsc.2023.1207","url":null,"abstract":"In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This article is part of a special issue: Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"35 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66548570","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}
Airlines are known to compete for passengers, and airline profitability heavily depends on the ability to estimate passenger demand, which in turn depends on flight schedules, fares, and the number of seats available at each fare, across all airlines. Interestingly, such competitive interactions and passenger substitution effects may not be limited to the planning stages. Existing regulations in some countries and regions impose monetary compensations to passengers in case of disruptions, altering the way they perceive the utility of other travel alternatives after the disruption starts. These passenger rights regulations may act as catalysts of passengers’ response to recovered schedules. Ignoring such passenger response behavior under operational disruptions may lead airlines to develop subpar recovery schedules. We develop a passenger response model and embed it into a novel integrated optimization approach that recovers airline schedules, aircraft, and passenger itineraries while endogenizing the impacts of airlines' decisions on passenger compensation and passenger response. We also develop an original solution approach, involving exact linearization of the nonlinear passenger cost terms, combined with delayed constraint generation for ensuring aircraft maintenance feasibility and an acceleration technique that penalizes deviations from planned schedules. Computational results on real-world problem instances from two major European airlines are reported, for scenarios involving disruptions, such as delayed flights, airport closures, and unexpected grounding of aircraft. Our approach is found to be tractable and scalable, producing solutions that are superior to airline’s actual decisions and highly robust in the face of passenger response uncertainty. Of particular relevance to the practitioners, our simulation results highlight that accounting for passengers’ disruption response behaviors, even in a highly approximate manner, yields significant benefits to the airline compared with not accounting for them at all, which is the current state-of-the-art. Funding: This work was supported by the Agencia Estatal de Investigación [Grant PID2020-112967GB-C33], the Ministerio de Economía y Competitividad, Spain [Grant TRA2016-76914-C3-3-P], and the Ministerio de Ciencia, Innovación y Universidades, Spain [Grant CAS19/00036].
众所周知，航空公司会争夺乘客，而航空公司的盈利能力在很大程度上取决于估计乘客需求的能力，而乘客需求又取决于所有航空公司的航班时刻表、票价和每种票价的可用座位数量。有趣的是，这种竞争互动和乘客替代效应可能并不局限于规划阶段。一些国家和地区的现行法规规定，在交通中断的情况下向乘客提供金钱补偿，这改变了他们在交通中断开始后对其他出行选择效用的看法。这些乘客权利规定可以作为催化剂，促进乘客对恢复的时间表作出反应。在运营中断的情况下，忽视乘客的这种反应行为可能会导致航空公司制定出低于标准的恢复计划。我们开发了一个乘客响应模型，并将其嵌入到一个新的集成优化方法中，该方法可以恢复航空公司的航班时刻表、飞机和乘客行程，同时内化航空公司决策对乘客补偿和乘客响应的影响。我们还开发了一种原始的解决方法，包括非线性乘客成本项的精确线性化，结合延迟约束生成以确保飞机维护的可行性，以及惩罚偏离计划时间表的加速技术。本文报告了来自欧洲两家主要航空公司的实际问题实例的计算结果，这些实例涉及航班延误、机场关闭和飞机意外停飞等中断情况。我们的方法易于处理且可扩展，产生的解决方案优于航空公司的实际决策，并且在面对乘客反应的不确定性时非常稳健。与从业人员特别相关的是，我们的模拟结果强调，考虑乘客的中断响应行为，即使是以高度近似的方式，也会给航空公司带来显著的好处，而不是考虑他们，这是目前最先进的。本工作得到了西班牙Estatal de Investigación [Grant PID2020-112967GB-C33]、西班牙Ministerio de Competitividad [Grant TRA2016-76914-C3-3-P]和西班牙Ciencia de Ciencia, Innovación y Universidades [Grant CAS19/00036]的支持。
{"title":"Passenger-Centric Integrated Airline Schedule and Aircraft Recovery","authors":"Luis Cadarso, Vikrant Vaze","doi":"10.1287/trsc.2022.1174","DOIUrl":"https://doi.org/10.1287/trsc.2022.1174","url":null,"abstract":"Airlines are known to compete for passengers, and airline profitability heavily depends on the ability to estimate passenger demand, which in turn depends on flight schedules, fares, and the number of seats available at each fare, across all airlines. Interestingly, such competitive interactions and passenger substitution effects may not be limited to the planning stages. Existing regulations in some countries and regions impose monetary compensations to passengers in case of disruptions, altering the way they perceive the utility of other travel alternatives after the disruption starts. These passenger rights regulations may act as catalysts of passengers’ response to recovered schedules. Ignoring such passenger response behavior under operational disruptions may lead airlines to develop subpar recovery schedules. We develop a passenger response model and embed it into a novel integrated optimization approach that recovers airline schedules, aircraft, and passenger itineraries while endogenizing the impacts of airlines' decisions on passenger compensation and passenger response. We also develop an original solution approach, involving exact linearization of the nonlinear passenger cost terms, combined with delayed constraint generation for ensuring aircraft maintenance feasibility and an acceleration technique that penalizes deviations from planned schedules. Computational results on real-world problem instances from two major European airlines are reported, for scenarios involving disruptions, such as delayed flights, airport closures, and unexpected grounding of aircraft. Our approach is found to be tractable and scalable, producing solutions that are superior to airline’s actual decisions and highly robust in the face of passenger response uncertainty. Of particular relevance to the practitioners, our simulation results highlight that accounting for passengers’ disruption response behaviors, even in a highly approximate manner, yields significant benefits to the airline compared with not accounting for them at all, which is the current state-of-the-art. Funding: This work was supported by the Agencia Estatal de Investigación [Grant PID2020-112967GB-C33], the Ministerio de Economía y Competitividad, Spain [Grant TRA2016-76914-C3-3-P], and the Ministerio de Ciencia, Innovación y Universidades, Spain [Grant CAS19/00036].","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135399717","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}
Nilankur Dutta, Thibault Charlottin, Alexandre Nicolas
Parking plays a central role in transport policies and has wide-ranging consequences: While the average time spent searching for parking exceeds dozens of hours per driver every year in many Western cities, the associated cruising traffic generates major externalities, by emitting pollutants and contributing to congestion. However, the laws governing the parking search time remain opaque in many regards, which hinders any general understanding of the problem and its determinants. Here, we frame the problem of parking search in a very generic, but mathematically compact formulation that puts the focus on the role of the street network and the unequal attractiveness of parking spaces. This problem is solved in two independent ways, valid in any street network and for a wide range of drivers’ behaviours. Numerically, this is done by means of a computationally efficient and versatile agent-based model. Analytically, we leverage the machinery of Statistical Physics and Graph Theory to derive a generic mean-field relation giving the parking search time as a function of the occupancy of parking spaces; an expression for the latter is obtained in the stationary regime. We show that these theoretical results are applicable in toy networks as well as in complex, realistic cases such as the large-scale street network of the city of Lyon, France. Taken as a whole, these findings clarify the parameters that directly control the search time and provide transport engineers with a quantitative grasp of the parking problem. Besides, they establish formal connections between the parking issue in realistic settings and physical problems. Funding: This work was supported by IDEXLYON (IDEXLYON 2020–2021); Institut Rhonalpin des Systèmes Complexes (IXXI) (Vulnerabilite). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1206 .
{"title":"Parking Search in the Physical World: Calculating the Search Time by Leveraging Physical and Graph Theoretical Methods","authors":"Nilankur Dutta, Thibault Charlottin, Alexandre Nicolas","doi":"10.1287/trsc.2023.1206","DOIUrl":"https://doi.org/10.1287/trsc.2023.1206","url":null,"abstract":"Parking plays a central role in transport policies and has wide-ranging consequences: While the average time spent searching for parking exceeds dozens of hours per driver every year in many Western cities, the associated cruising traffic generates major externalities, by emitting pollutants and contributing to congestion. However, the laws governing the parking search time remain opaque in many regards, which hinders any general understanding of the problem and its determinants. Here, we frame the problem of parking search in a very generic, but mathematically compact formulation that puts the focus on the role of the street network and the unequal attractiveness of parking spaces. This problem is solved in two independent ways, valid in any street network and for a wide range of drivers’ behaviours. Numerically, this is done by means of a computationally efficient and versatile agent-based model. Analytically, we leverage the machinery of Statistical Physics and Graph Theory to derive a generic mean-field relation giving the parking search time as a function of the occupancy of parking spaces; an expression for the latter is obtained in the stationary regime. We show that these theoretical results are applicable in toy networks as well as in complex, realistic cases such as the large-scale street network of the city of Lyon, France. Taken as a whole, these findings clarify the parameters that directly control the search time and provide transport engineers with a quantitative grasp of the parking problem. Besides, they establish formal connections between the parking issue in realistic settings and physical problems. Funding: This work was supported by IDEXLYON (IDEXLYON 2020–2021); Institut Rhonalpin des Systèmes Complexes (IXXI) (Vulnerabilite). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1206 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135504456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We wish to thank the following individuals who acted as referees for Transportation Science in 2022. We express our apologies to those whose names we may have missed.
我们要感谢以下在2022年担任交通科学裁判的个人。我们向那些我们可能漏掉名字的人表示歉意。
{"title":"Acknowledgment to Referees (2022)","authors":"","doi":"10.1287/trsc.2023.1203","DOIUrl":"https://doi.org/10.1287/trsc.2023.1203","url":null,"abstract":"We wish to thank the following individuals who acted as referees for Transportation Science in 2022. We express our apologies to those whose names we may have missed.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135289832","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}
Yves Molenbruch, Kris Braekers, Ohad Eisenhandler, M. Kaspi
Shared mobility services involving electric autonomous shuttles have increasingly been implemented in recent years. Because of various restrictions, these services are currently offered on fixed circuits and operated with fixed schedules. This study introduces a service variant with flexible stopping patterns and schedules. Specifically, in the electric dial-a-ride problem on a fixed circuit (eDARP-FC), a fleet of capacitated electric shuttles operates on a given circuit consisting of a recharging depot and a sequence of stations where passengers can be picked up and dropped off. The shuttles may perform multiple laps, between which they may need to recharge. The goal of the problem is to determine the vehicles’ stopping sequences and schedules, including recharging plans, so as to minimize a weighted sum of the total passenger excess time and the total number of laps. The eDARP-FC is formulated as a nonstandard lap-based mixed integer linear programming and is shown to be NP-Hard. Efficient polynomial time algorithms are devised for two special scheduling subproblems. These algorithms and several heuristics are then applied as subroutines within a large neighborhood search metaheuristic. Experiments on instances derived from a real-life system demonstrate that the flexible service results in a 32%–75% decrease in the excess time at the same operational costs. Funding: This work was supported by the Fonds Wetenschappelijk Onderzoek [Project Data-Driven Logistics: Grant S007318N; Project Optimizing the Design of a Hybrid Urban Mobility System: Grant G020222N; and Grant OR4Logistics]. Y. Molenbruch is partially funded by the Fonds Wetenschappelijk Onderzoek [Grant 1202719N]. The computational resources and services used in this work were provided by the Flemish Supercomputer Center funded by the Fonds Wetenschappelijk Onderzoek and the Flemish Government. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2023.1208 .
{"title":"The Electric Dial-a-Ride Problem on a Fixed Circuit","authors":"Yves Molenbruch, Kris Braekers, Ohad Eisenhandler, M. Kaspi","doi":"10.1287/trsc.2023.1208","DOIUrl":"https://doi.org/10.1287/trsc.2023.1208","url":null,"abstract":"Shared mobility services involving electric autonomous shuttles have increasingly been implemented in recent years. Because of various restrictions, these services are currently offered on fixed circuits and operated with fixed schedules. This study introduces a service variant with flexible stopping patterns and schedules. Specifically, in the electric dial-a-ride problem on a fixed circuit (eDARP-FC), a fleet of capacitated electric shuttles operates on a given circuit consisting of a recharging depot and a sequence of stations where passengers can be picked up and dropped off. The shuttles may perform multiple laps, between which they may need to recharge. The goal of the problem is to determine the vehicles’ stopping sequences and schedules, including recharging plans, so as to minimize a weighted sum of the total passenger excess time and the total number of laps. The eDARP-FC is formulated as a nonstandard lap-based mixed integer linear programming and is shown to be NP-Hard. Efficient polynomial time algorithms are devised for two special scheduling subproblems. These algorithms and several heuristics are then applied as subroutines within a large neighborhood search metaheuristic. Experiments on instances derived from a real-life system demonstrate that the flexible service results in a 32%–75% decrease in the excess time at the same operational costs. Funding: This work was supported by the Fonds Wetenschappelijk Onderzoek [Project Data-Driven Logistics: Grant S007318N; Project Optimizing the Design of a Hybrid Urban Mobility System: Grant G020222N; and Grant OR4Logistics]. Y. Molenbruch is partially funded by the Fonds Wetenschappelijk Onderzoek [Grant 1202719N]. The computational resources and services used in this work were provided by the Flemish Supercomputer Center funded by the Fonds Wetenschappelijk Onderzoek and the Flemish Government. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2023.1208 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46829859","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}
S. Enayati, Haitao Li, James F. Campbell, Deng Pan
Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution, which simultaneously determines the locations of local distribution centers, drone bases, and drone relay stations, while obeying the cold chain time limit and drone range. Two mathematical optimization models with complementary strengths are developed. The first model considers the vaccine travel time at the aggregate level with a compact formulation, but it can be too conservative in meeting the cold chain time limit. The second model is based on the layered network framework to track the vaccine flow and travel time associated with each origin-destination (OD) pair. It allows the number of transshipments and the number of drone stops in a vaccine flow path to be limited, which reflects practical operations and can be computationally advantageous. Both models are applied for vaccine distribution network design with two types of drones in Vanuatu as a case study. Solutions with drones using our parameter settings are shown to generate large savings, with differentiated roles for large and small drones. To generalize the empirical findings and examine the performance of our models, we conduct comprehensive computational experiments to assess the sensitivity of optimal solutions and performance metrics to key problem parameters. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Association for Supply Chain Management (ASCM) and the University of Missouri Research Board (UMSL Award 0059109). Supplemental Material: The online supplement is available at https://doi.org/10.1287/trsc.2023.1205 .
{"title":"Multimodal Vaccine Distribution Network Design with Drones","authors":"S. Enayati, Haitao Li, James F. Campbell, Deng Pan","doi":"10.1287/trsc.2023.1205","DOIUrl":"https://doi.org/10.1287/trsc.2023.1205","url":null,"abstract":"Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution, which simultaneously determines the locations of local distribution centers, drone bases, and drone relay stations, while obeying the cold chain time limit and drone range. Two mathematical optimization models with complementary strengths are developed. The first model considers the vaccine travel time at the aggregate level with a compact formulation, but it can be too conservative in meeting the cold chain time limit. The second model is based on the layered network framework to track the vaccine flow and travel time associated with each origin-destination (OD) pair. It allows the number of transshipments and the number of drone stops in a vaccine flow path to be limited, which reflects practical operations and can be computationally advantageous. Both models are applied for vaccine distribution network design with two types of drones in Vanuatu as a case study. Solutions with drones using our parameter settings are shown to generate large savings, with differentiated roles for large and small drones. To generalize the empirical findings and examine the performance of our models, we conduct comprehensive computational experiments to assess the sensitivity of optimal solutions and performance metrics to key problem parameters. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Association for Supply Chain Management (ASCM) and the University of Missouri Research Board (UMSL Award 0059109). Supplemental Material: The online supplement is available at https://doi.org/10.1287/trsc.2023.1205 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42229078","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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