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On the Efficiency Impacts of Berthing Priority Provision 停泊优先权对效率的影响
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-29 DOI: 10.1287/trsc.2022.0411
Xi Lin, Xinyue Pu, Xiwen Bai
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 .
面对全球集装箱航运市场日益加剧的港口间竞争,越来越多的港口选择为承运商提供优先靠泊权,以增加其吸引力。本研究首次从理论上分析了这种优先权对效率的影响。具体来说,本研究将位码头港口中每个码头的稳态动态模拟为一个优先排队系统。我们探讨了提供和不提供优先权时的平衡航运流量分布和由此产生的系统总成本(即燃料消耗成本和等待时间成本),以及它们的主要分析特性。然后,我们研究了这些优先权方案对准时到达(JIT)的 "二阶 "影响,准时到达是一种日益流行的绿色港口管理工具。具体来说,我们研究了与 JIT 抵达相关的均衡状态如何随着优先靠泊而改变。这些分析得出了一些有趣的结果,其中包括:(1)在对称港口、两个码头服务能力相等的情况下实施优先方案时,系统总成本会增加或保持不变;(2)然而,在非对称码头的情况下,泊位优先也有可能降低系统总成本,而且这种现象只有在提供优先的码头拥有更大的服务能力时才会出现;(3)结果表明,在港口运营中,"优先的价格 "可能达到[公式]:见正文],这意味着提供优先权可能会严重损害运营效率;以及(4)最后,在对称港口中,提供优先权对 JIT 战略具有二阶负效应,这种负效应可能会抵消正效应。这些理论结果得到了数值实验的验证,其中一些还得到了经验数据的支持。这些结果为港口(或码头)机构的决策提供了重要的现实意义:本研究得到了国家自然科学基金[72371143 和 72188101]的资助:在线附录见 https://doi.org/10.1287/trsc.2022.0411 。
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引用次数: 0
Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences 利用轨迹偏好进行空中交通流量管理的数据驱动优化
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-27 DOI: 10.1287/trsc.2022.0309
Luigi De Giovanni, Carlo Lancia, Guglielmo Lulli
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.
本文针对基于轨迹的空中交通流量管理(ATFM)提出了一种新颖的数据驱动优化方法。所提方法的一个关键方面是纳入了空域用户的轨迹偏好,通过结合聚类和分类技术,从交通数据中计算出用户的轨迹偏好。机器学习也用于提取一致的轨迹选项,而优化则通过数学编程模型来解决需求-容量不平衡的问题,该模型可明智地为每个航班分配可行的四维轨迹和可能的地面延误。该方法已在欧洲控制中心数据存储库中提取的实例上进行了测试。我们考虑了超过 32,000 个航班,在较短的计算时间内解决了文献中最大的 ATFM 问题实例,从实用角度来看是合理的。作为副产品,我们强调了偏好和延迟之间的权衡以及潜在的好处。事实上,计算问题的有效解决方案有助于网络管理员和空域用户达成共识。鉴于解决方案的准确性和出色的计算性能,我们乐观地认为,所提出的方法可为下一代空中交通流量管理工具的开发做出重大贡献。
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引用次数: 0
Optimal Service Time Windows 最佳服务时间窗口
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-27 DOI: 10.1287/trsc.2023.0004
Marlin W. Ulmer, Justin C. Goodson, Barrett W. Thomas
Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several uncertainties: future customer requests are unknown, final service plans are not yet determined, and when fulfillment is outsourced to a third party, the firm has limited control over routing procedures and eventual fulfillment times. Even when routing is performed in-house, time windows often do not receive explicit consideration. In this paper, we show how companies can communicate reliable and narrow time windows to customers in the face of arrival time uncertainty when time window decisions are decoupled from routing procedures. Under assumptions on the shape of arrival time distributions, our main result characterizes the optimal policy, identifying structure that reduces a high-dimensional stochastic nonlinear optimization problem to a root-finding problem in one dimension. The result inspires a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service. Our results show that time windows should be tailored to individual customers, time window sizes should be proportional to the service level, larger time windows should be assigned to earlier requests and smaller time windows to later requests, larger time windows should be assigned to customers further from the depot of operation and smaller time windows to closer customers, high quality time windows can be identified even with limited data, and cost savings afforded by routing efficiency should be measured against potential losses to customer convenience.Funding: M. W. Ulmer’s work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Emmy Noether Programme, [project 444657906].Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0004 .
由于客户通常必须安排好自己的时间才能参加上门服务,因此他们希望能准确预估服务时间。然而,即使公司报出了较大的服务时间窗口,也经常会错过,导致客户不满。之所以会出现时间窗口过大和经常失败的情况,是因为必须在几个不确定因素面前向客户传达时间窗口:客户未来的要求是未知的,最终的服务计划尚未确定,而且当服务外包给第三方时,公司对路由程序和最终服务时间的控制是有限的。即使在内部进行路由选择时,时间窗口也往往得不到明确的考虑。在本文中,我们展示了当时间窗口决策与路由程序脱钩时,企业如何在到达时间不确定的情况下向客户传达可靠且较窄的时间窗口。在假设到达时间分布形状的情况下,我们的主要结果描述了最优策略的特征,确定了将高维随机非线性优化问题简化为一维寻根问题的结构。这一结果为更一般的情况提供了一个可用于实践的启发式方法。与向所有客户传达统一时间窗口的行业标准以及其他实际应用的政策相比,我们的客户特定时间窗口报价方法在不牺牲服务可靠性的前提下,大大增加了客户便利性。我们的研究结果表明,时间窗口应针对每个客户量身定制,时间窗口的大小应与服务水平成正比,应为较早的请求分配较大的时间窗口,为较晚的请求分配较小的时间窗口,应为距离运营站较远的客户分配较大的时间窗口,为距离较近的客户分配较小的时间窗口,即使数据有限也能识别高质量的时间窗口,路由效率带来的成本节约应与客户便利性的潜在损失进行衡量:M. W. Ulmer 的工作由德国研究基金会 (DFG) 埃米-诺特计划 [项目 444657906] 资助:电子版可在 https://doi.org/10.1287/trsc.2023.0004 上查阅。
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引用次数: 0
Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows 用时间窗口解决动态车辆路由问题的组合优化增强型机器学习方法
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-14 DOI: 10.1287/trsc.2023.0107
Léo Baty, Kai Jungel, Patrick S. Klein, Axel Parmentier, Maximilian Schiffer
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same-day deliveries. Existing multistage stochastic optimization approaches that allow solving the underlying dynamic vehicle routing problem either are computationally too expensive for an application in online settings or—in the case of reinforcement learning—struggle to perform well on high-dimensional combinatorial problems. To mitigate these drawbacks, we propose a novel machine learning pipeline that incorporates a combinatorial optimization layer. We apply this general pipeline to a dynamic vehicle routing problem with dispatching waves, which was recently promoted in the EURO Meets NeurIPS Vehicle Routing Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem. With this work, we provide a comprehensive numerical study that further highlights the efficacy and benefits of the proposed pipeline beyond the results achieved in the competition, for example, by showcasing the robustness of the encoded policy against unseen instances and scenarios.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 Deutsche Forschungsgemeinschaft [Grant 449261765].
随着电子商务的兴起和客户要求的不断提高,物流服务提供商的日常规划工作面临着新的复杂性,这主要是由于要有效地处理当日交付问题。现有的多阶段随机优化方法可以解决基本的动态车辆路由问题,但这些方法要么计算成本过高,无法应用于在线环境,要么--就强化学习而言--在高维组合问题上表现不佳。为了缓解这些弊端,我们提出了一种包含组合优化层的新型机器学习管道。我们将这一通用管道应用于带有调度波的动态车辆路由问题,该问题最近在 2022 年 NeurIPS 欧洲会议上的 NeurIPS 车辆路由竞赛中得到推广。我们的方法在这次比赛中排名第一,在解决所提出的动态车辆路由问题方面优于所有其他方法。通过这项工作,我们提供了一项全面的数值研究,进一步突出了拟议管道的功效和优势,而不仅仅是在比赛中取得的成绩,例如,通过展示编码策略对未知实例和场景的鲁棒性:本文已被 DIMACS Implementation Challenge 运输科学特刊录用:资助:这项工作得到了德国科学基金会 [资助号 449261765] 的支持。
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引用次数: 0
A New Simheuristic Approach for Stochastic Runway Scheduling 随机跑道调度的新模拟方法
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-13 DOI: 10.1287/trsc.2022.0400
Rob Shone, Kevin Glazebrook, Konstantinos G. Zografos
We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang distributions and are affected by weather conditions, which also evolve continuously over time. The resulting multistage optimization problem is intractable using exact methods, and we propose a novel simheuristic approach based on the application of methods analogous to variable neighborhood search in a high-dimensional stochastic environment. Our model is calibrated using flight tracking data for over 98,000 arrivals at Heathrow Airport. Results from numerical experiments indicate that our proposed simheuristic algorithm outperforms an alternative based on deterministic forecasts under a wide range of parameter values, with the largest benefits seen when the underlying stochastic processes become more volatile and also when the on-time requirements of individual flights are given greater weight in the objective function.Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/M020258/1].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/trsc.2022.0400 .
我们考虑的是一个随机动态跑道调度问题,涉及飞机在一条跑道上的降落。排序决策是在了解所有即将到达机场的飞机的预计到达时间(ETA)的基础上做出的,这些 ETA 根据连续时间随机过程变化。连续跑道着陆之间的时间间隔通过依赖于序列的厄朗分布建模,并受到天气条件的影响,而天气条件也会随时间不断变化。由此产生的多阶段优化问题使用精确方法难以解决,因此我们提出了一种新颖的模拟方法,其基础是在高维随机环境中应用类似于变量邻域搜索的方法。我们使用希思罗机场 98,000 多架次到达航班的航班跟踪数据对模型进行了校准。数值实验结果表明,我们提出的模拟算法在各种参数值下都优于基于确定性预测的替代算法,当基本随机过程变得更加不稳定,以及目标函数中单个航班的准点要求权重更大时,模拟算法的优势最大:本研究得到了工程与物理科学研究委员会[Grant EP/M020258/1]的支持:在线附录和数据文件见 https://doi.org/10.1287/trsc.2022.0400 。
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引用次数: 0
Robust Charging Network Planning for Metropolitan Taxi Fleets 大都市出租车队的稳健充电网络规划
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-09 DOI: 10.1287/trsc.2022.0207
Gregor Godbersen, Rainer Kolisch, Maximilian Schiffer
We study the robust charging station location problem for a large-scale commercial taxi fleet. Vehicles within the fleet coordinate on charging operations but not on customer acquisition. We decide on a set of charging stations to open to ensure operational feasibility. To make this decision, we propose a novel solution method situated between the location routing problems with intraroute facilities and flow refueling location problems. Additionally, we introduce a problem variant that makes a station sizing decision. Using our exact approach, charging stations for a robust operation of citywide taxi fleets can be planned. We develop a deterministic core problem employing a cutting plane method for the strategic problem and a branch-and-price decomposition for the operational problem. We embed this problem into a robust solution framework based on adversarial sampling, which allows for planner-selectable risk tolerance. We solve instances derived from real-world data of the metropolitan area of Munich containing 1,000 vehicles and 60 potential charging station locations. Our investigation of the sensitivity of technological developments shows that increasing battery capacities shows a more favorable impact on vehicle feasibility of up to 10 percentage points compared with increasing charging speeds. Allowing for depot charging dominates both of these options. Finally, we show that allowing just 1% of operational infeasibility risk lowers infrastructure costs by 20%.Funding: This work was partially funded by the Deutsche Forschungsgemeinschaft [Project 277991500].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0207 .
我们研究了大规模商业出租车队的稳健充电站位置问题。车队中的车辆在充电操作上相互协调,但在客户获取上并不协调。我们决定开设一组充电站,以确保运营的可行性。为了做出这样的决定,我们提出了一种介于路线内设施位置路由问题和流动加油位置问题之间的新型求解方法。此外,我们还引入了一个问题变体,以做出充电站规模决策。利用我们的精确方法,可以为全市出租车队的稳健运营规划充电站。我们开发了一个确定性核心问题,在战略问题上采用切割平面法,在运营问题上采用分支-价格分解法。我们将这一问题嵌入到基于对抗抽样的稳健解决方案框架中,该框架允许规划者选择风险容忍度。我们解决的实例来自慕尼黑大都市区的真实数据,其中包含 1,000 辆汽车和 60 个潜在充电站位置。我们对技术发展敏感性的研究表明,与提高充电速度相比,增加电池容量对车辆可行性的影响更大,最多可达 10 个百分点。允许车厂充电在这两种方案中都占主导地位。最后,我们表明,只要允许 1%的运营不可行性风险,就能将基础设施成本降低 20%:本研究部分经费来自德国科学基金会 [项目 277991500]:在线附录见 https://doi.org/10.1287/trsc.2022.0207 。
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引用次数: 0
Reliable Routing Strategies on Urban Transportation Networks 城市交通网络的可靠路由策略
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-02-08 DOI: 10.1287/trsc.2023.0013
Daniel Yamín, Andrés L. Medaglia, Arun Prakash Akkinepally
The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination given an arrival time threshold. The problem is defined on a stochastic and time-dependent network that captures real-world transportation systems’ inherent uncertainty and dynamism. To solve this problem, we present a dynamic programming–based algorithm that benefits from a node-time pairs queue implementation. In addition to improving the computational running time in most cases, this implementation supports different queue disciplines, leading to different algorithmic approaches: label-correcting and label-setting methods. We prove the correctness of the algorithm and derive its worst case time complexity. We present computational experiments over real-world, large-scale transportation networks with up to [Formula: see text] nodes, showing that the algorithm is a viable alternative to existing state-of-the-art methods. It can be four times faster for relatively tight arrival time thresholds and is competitive for looser ones. We also present experiments assessing the different queue disciplines used within the algorithm, the gains of the node–time pairs queue implementation, and comparing optimal strategies obtained from reliability and travel time objectives.
在城市交通网络中寻找最可靠路由策略的问题,是指在给定到达时间阈值的情况下,确定最大化准时到达目的地概率的时间适应性路由策略。该问题是在一个随机和随时间变化的网络上定义的,它捕捉到了现实世界中交通系统固有的不确定性和动态性。为了解决这个问题,我们提出了一种基于动态编程的算法,该算法得益于节点时间对队列的实现。除了在大多数情况下提高计算运行时间外,这种实现还支持不同的队列规则,从而产生了不同的算法方法:标签校正法和标签设置法。我们证明了算法的正确性,并推导出其最坏情况下的时间复杂度。我们介绍了在真实世界中节点数最多为[公式:见正文]的大规模运输网络上进行的计算实验,结果表明该算法是现有最先进方法的可行替代方案。对于相对严格的到达时间阈值,它可以快四倍,而对于较宽的阈值,它则具有竞争力。我们还通过实验评估了算法中使用的不同队列规则、节点时间对队列实施的收益,并比较了从可靠性和旅行时间目标中获得的最优策略。
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引用次数: 0
Hybrid Value Function Approximation for Solving the Technician Routing Problem with Stochastic Repair Requests 解决具有随机维修请求的技术人员路由问题的混合值函数近似法
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-01-24 DOI: 10.1287/trsc.2022.0434
Dai T. Pham, Gudrun P. Kiesmüller
We investigate the combined planning problem involving the routing of technicians and the stocking of spare parts for servicing geographically distributed repair tasks. The problem incorporates many operational uncertainties, such as future repair requests and the required spare parts to replace malfunctioned components. We model the problem as a sequential decision problem where decisions are made at the end of each day about the next day’s technician route and spare part inventory in the van. We show that exact methods are intractable because of the inherent high-dimensional state, decision, and transition spaces involved. To overcome these challenges, we present two novel algorithmic techniques. First, we suggest a hybrid value function approximation method that combines a genetic search with a graph neural network capable of reasoning, learning, and decision making in high-dimensional, discrete decision spaces. Second, we introduce a unique state-encoding method that employs multiattribute graphs and spatial markers, eliminating the need for manually designed basis functions and allowing efficient learning. We illustrate the general adaptive learning capacity by solving a variety of instance settings without instance-specific hyperparameter tuning. An extensive numerical study demonstrates that our hybrid learning technique outperforms other benchmark policies and adapts well to changes in the environment. We also generate a wide range of insights that not only shed light on the algorithmic components but also offer guidance on how to execute on-site repair tasks more efficiently. The techniques showcased are versatile and hold potential for application in other dynamic and stochastic problems, particularly in the realm of transportation planning.Funding: This work was supported by Deutsche Forschungsgemeinschaft (DFG). The Research Training Group 2201 [Grant 277991500], “Advanced Optimization in a Networked Economy,” funded by the DFG, has provided partial support for this work.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0434 .
我们研究了涉及技术人员路线安排和备件储备的组合规划问题,以便为地理上分散的维修任务提供服务。该问题包含许多操作上的不确定性,如未来的维修请求和更换故障部件所需的备件。我们将该问题建模为一个连续决策问题,每天结束时对第二天的技术人员路线和货车上的备件库存做出决策。我们的研究表明,由于涉及固有的高维状态、决策和转换空间,精确方法是难以解决的。为了克服这些挑战,我们提出了两种新颖的算法技术。首先,我们提出了一种混合值函数近似方法,该方法结合了遗传搜索和图神经网络,能够在高维、离散的决策空间中进行推理、学习和决策。其次,我们引入了一种独特的状态编码方法,该方法采用多属性图和空间标记,无需手动设计基础函数,从而实现高效学习。我们通过解决各种实例设置来说明通用自适应学习能力,而无需针对特定实例进行超参数调整。广泛的数值研究表明,我们的混合学习技术优于其他基准策略,并能很好地适应环境变化。我们还提出了一系列见解,这些见解不仅揭示了算法的组成部分,还为如何更高效地执行现场修复任务提供了指导。所展示的技术用途广泛,有望应用于其他动态和随机问题,尤其是交通规划领域:本研究得到了德国科学基金会(DFG)的支持。由 DFG 资助的 2201 研究培训小组 [Grant 277991500]"网络经济中的高级优化 "为本研究提供了部分支持:在线附录请访问 https://doi.org/10.1287/trsc.2022.0434 。
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引用次数: 0
Demand Steering in a Last-Mile Delivery Problem with Home and Pickup Point Delivery Options 最后一英里送货问题中的需求引导与送货上门和取货点送货选择
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-01-19 DOI: 10.1287/trsc.2023.0287
Albina Galiullina, Nevin Mutlu, Joris Kinable, Tom Van Woensel
To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated incentive and routing decisions under uncertainty. We model this problem as a two-stage stochastic program with decision-dependent uncertainty. In the first stage, a retailer decides which customers to incentivize. However, customers’ reaction to the incentive is stochastic: they may accept the offer and switch to pickup point delivery, or they may decline the offer and stick with home delivery. In the second stage, after customers’ final delivery choices are revealed, a vehicle route is planned to serve customers via the delivery option of their choice. We develop an exact branch-and-bound algorithm and propose several heuristics to improve the algorithm’s scalability. Our algorithm solves instances with up to 50 customers, realizing on average 4%–8% lower last-mile delivery costs compared with the commonly applied approaches in the industry that do not use incentives or offer incentives to all customers. We also develop a benchmark policy that gives very fast solutions with a 2% average optimality gap for small instances and up to 2% average cost increase compared with the heuristic solutions.Funding: This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [Grant 765395].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0287 .
为了提高最后一英里配送的效率,在线零售商可以在其运营中采用取货点。然后,零售商可以激励客户将送货上门改为取货点送货,以降低成本。然而,顾客是否接受这种鼓励,转而使用取货点送货,通常是不确定的。在这种情况下,就产生了一个新的 "最后一英里 "配送问题,即在不确定的情况下,激励机制和路线选择决策相结合的问题。我们将这一问题建模为一个两阶段随机程序,具有决策依赖的不确定性。在第一阶段,零售商决定激励哪些客户。然而,顾客对激励措施的反应是随机的:他们可能会接受激励措施并转向自提点送货,也可能会拒绝激励措施并坚持送货上门。在第二阶段,当客户的最终送货选择揭晓后,我们会规划一条车辆路线,通过客户选择的送货方式为其提供服务。我们开发了一种精确的分支与边界算法,并提出了几种启发式方法来提高算法的可扩展性。我们的算法可以解决多达 50 个客户的实例,与业内普遍采用的不使用激励措施或向所有客户提供激励措施的方法相比,最后一英里配送成本平均降低了 4%-8%。我们还开发了一种基准策略,它能提供非常快速的解决方案,与启发式解决方案相比,小实例的平均优化差距为 2%,平均成本增加最多为 2%:本项目得到了欧盟 "地平线 2020 "研究与创新计划玛丽-斯克沃多夫斯卡-居里基金协议[Grant 765395]的资助:在线附录见 https://doi.org/10.1287/trsc.2023.0287 。
{"title":"Demand Steering in a Last-Mile Delivery Problem with Home and Pickup Point Delivery Options","authors":"Albina Galiullina, Nevin Mutlu, Joris Kinable, Tom Van Woensel","doi":"10.1287/trsc.2023.0287","DOIUrl":"https://doi.org/10.1287/trsc.2023.0287","url":null,"abstract":"To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated incentive and routing decisions under uncertainty. We model this problem as a two-stage stochastic program with decision-dependent uncertainty. In the first stage, a retailer decides which customers to incentivize. However, customers’ reaction to the incentive is stochastic: they may accept the offer and switch to pickup point delivery, or they may decline the offer and stick with home delivery. In the second stage, after customers’ final delivery choices are revealed, a vehicle route is planned to serve customers via the delivery option of their choice. We develop an exact branch-and-bound algorithm and propose several heuristics to improve the algorithm’s scalability. Our algorithm solves instances with up to 50 customers, realizing on average 4%–8% lower last-mile delivery costs compared with the commonly applied approaches in the industry that do not use incentives or offer incentives to all customers. We also develop a benchmark policy that gives very fast solutions with a 2% average optimality gap for small instances and up to 2% average cost increase compared with the heuristic solutions.Funding: This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [Grant 765395].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0287 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"12 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139516911","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}
引用次数: 0
Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics 具有供应不确定性的随机循环库存路由:绿色氢气物流案例
IF 4.6 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2024-01-04 DOI: 10.1287/trsc.2022.0435
Umur Hasturk, Albert H. Schrotenboer, Evrim Ursavas, Kees Jan Roodbergen
Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies.Funding: This project received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under [Grant Agreement 875090]. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research. A. H. Schrotenboer received support from the Dutch Science Foundation (Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO) through [Grant VI.Veni.211E.043].
利用电力可以从水中生产氢气。与电力本身不同,氢气随后可以大量储存。这使得太阳能和风能的生产与使用不同步。因此,氢有望成为实现气候中和经济的关键要素。然而,氢的物流非常复杂。必须确定网络中多个地点的库存政策,还必须安排从生产地点到客户的氢气运输。同时,氢气的生产模式是间歇性的,这影响了实现计划运输和库存水平的可能性。为了提供高效运输和储存氢气的策略,本文针对随机循环库存路由问题提出了一种参数化成本函数近似方法。首先,我们的方法包括一个参数化混合整数编程(MIP)模型,该模型可生成氢气车辆运输的固定和重复时间表。其次,考虑到生产量和需求量的不确定性,通过马尔可夫决策过程(MDP)模型进一步优化生产不足或生产过剩情况下的买卖决策。为了联合优化参数化 MIP 和 MDP 模型,我们的方法包括一种算法,通过迭代求解 MIP 和 MDP 模型来搜索参数空间。我们进行了计算实验,在各种问题设置中验证了我们的模型,并证明它能提供接近最优的解决方案。此外,我们还在荷兰两个氢气生产基地的专家评审案例研究中测试了我们的方法。我们为该地区的利益相关者提供了见解,并分析了这些案例研究中各种问题要素的影响:本项目得到了燃料电池和氢气 2 联合企业(现为清洁氢伙伴关系)的资助,资助协议为[875090]。该联合事业得到了欧盟地平线 2020 研究与创新计划、欧洲氢能计划和欧洲氢能研究计划的支持。A. H. Schrotenboer 通过[VI.Veni.211E.043号拨款]获得了荷兰科学基金会(Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO)的支持。
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Transportation Science
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