Transportation Research Part E-Logistics and Transportation Review最新文献_第9页

Inventory-constrained online learning for revenue management with delayed feedback 具有延迟反馈的收入管理的库存约束在线学习

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-06 DOI: 10.1016/j.tre.2025.104649

Sheng Ji

Delayed feedback is a prevalent challenge in modern logistics and transportation systems, especially on digital retail platforms. This paper investigates an online learning and pricing problem characterized by aggregated and anonymous delays. In this setting, neither demand nor revenue is immediately observable following a pricing decision; instead, these metrics become available to the retailer only after some stochastic delay. The retailer also faces an initial inventory constraint, creating a complex exploration-exploitation trade-off among learning demand, generating revenue, and managing inventory. To address this challenge, we propose a novel batch-based learning algorithm, referred to as Bandits with Dual Mirror Descent (BUD for short), which integrates mirror descent with bandit control. The algorithm employs a carefully designed batch structure to isolate the impact of delayed feedback, while combining Upper Confidence Bound (UCB) for pricing with dual updates for inventory management. Our theoretical analysis shows that the regret (defined as the revenue gap between the optimal policy and the learning algorithm) of BUD grows sublinearly with the selling horizon and matches the known lower bounds in both bandit with delays and online pricing problems. We conducted numerical experiments to demonstrate that the regret of BUD converges to 0 in various scenarios.

延迟反馈是现代物流和运输系统中普遍存在的挑战，特别是在数字零售平台上。本文研究了一个以聚合和匿名延迟为特征的在线学习和定价问题。在这种情况下，定价决定后，需求和收入都无法立即观察到；相反，这些指标只有在经过一些随机延迟后才对零售商可用。零售商还面临最初的库存约束，在了解需求、产生收入和管理库存之间产生了复杂的探索-开发权衡。为了解决这一挑战，我们提出了一种新的基于批处理的学习算法，称为具有双镜像下降的强盗（简称BUD），它将镜像下降与强盗控制相结合。该算法采用精心设计的批处理结构来隔离延迟反馈的影响，同时将定价的上置信限（UCB）与库存管理的双更新相结合。我们的理论分析表明，BUD的后悔（定义为最优策略与学习算法之间的收入差距）随着销售水平的次线性增长，并且在具有延迟和在线定价问题的强盗中都匹配已知的下界。我们通过数值实验证明了在各种情况下BUD的后悔收敛于0。

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引用次数: 0

Joint optimization of flood water routing and congestion-aware evacuation scheduling 洪水路径与拥挤感知疏散调度的联合优化

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-03 DOI: 10.1016/j.tre.2025.104645

Sina Bahrami , Mehdi Nourinejad , Matthew J. Roorda , Yafeng Yin

Urban flood emergencies pose significant risks to human safety and infrastructure operability, particularly in smart cities with interdependent systems. This study proposes an integrated optimization model for coordinating water and transportation networks during flood evacuations. The model simultaneously determines optimal reservoir discharge rates and dynamic vehicular evacuation schedules to maximize the number of evacuees within the limited warning time. Water flow is modeled using the Muskingum-Cunge flood-routing method to simulate flood propagation through a river-reservoir system, while traffic flow is captured via the Cell Transmission Model, which accounts for congestion dynamics and road capacities. The problem is formulated as a nonlinear program and solved through a linear relaxation using generalized Benders decomposition. A case study of the Town of High River, Canada, illustrates the model’s practical utility. Results show that the integrated strategy extends warning times, reduces congestion, and lowers the number of individuals exposed to flood risks compared to uncoordinated approaches. By enabling real-time, infrastructure-aware evacuation planning, the proposed framework offers a scalable decision-support tool for emergency managers. This work contributes to the growing body of research on the management of city infrastructures under disruption and supports the development of resilient and coordinated evacuation strategies in smart urban environments.

城市突发洪水事件对人类安全和基础设施的可操作性构成重大风险，特别是在具有相互依存系统的智慧城市。本研究提出洪水疏散过程中水运网络协调的综合优化模型。该模型同时确定最优水库流量和动态车辆疏散计划，在有限的预警时间内实现疏散人数最大化。通过Muskingum-Cunge洪水路径方法模拟洪水在河流-水库系统中的传播，而通过细胞传输模型（Cell Transmission Model）捕获交通流量，该模型考虑了拥堵动态和道路容量。该问题被表述为一个非线性规划，并通过广义Benders分解的线性松弛来求解。以加拿大High River镇为例，说明了该模型的实用性。结果表明，与不协调的方法相比，综合策略延长了预警时间，减少了拥堵，降低了暴露于洪水风险的个体数量。通过实现实时、感知基础设施的疏散规划，提议的框架为应急管理人员提供了可扩展的决策支持工具。这项工作有助于对城市基础设施在中断下的管理进行越来越多的研究，并支持在智能城市环境中制定有弹性和协调的疏散策略。

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引用次数: 0

Offline operations strategies of bike-sharing platforms: pure profit or beyond profit? 共享单车平台的线下运营策略：纯利润还是超利润？

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-02 DOI: 10.1016/j.tre.2025.104644

Dongliang Guo , Zhi-Ping Fan , Minghe Sun

Bike-sharing platforms can adopt two different offline operations strategies, i.e., independent operations (Strategy I), where each platform independently manages its offline operations, and outsourcing (Strategy O), where one platform outsources its offline operations to another competing platform. With the rise of the “beyond profit” management doctrine, many bike-sharing platforms have begun to pursue dual purposes, i.e., both profits and consumer surpluses, instead of the single purpose, i.e., “pure profit”. Given these facts, this work examines the equilibrium offline operations strategies of two bike-sharing platforms in a duopoly market based on the Hotelling framework and analyzes the platform profits and consumer surplus when the platforms pursue a single purpose or dual purposes. Several important results are obtained. When the platforms engage in intensive competition, the equilibrium operations strategy of the two platforms is Strategy O, and pursuing dual purposes can harm their respective profits. Under weak platform competition, both the investment synergy effect and the investment efficiency of offline operations can significantly affect the platform equilibrium offline operations strategies, and the platforms can obtain higher profits when pursuing dual purposes than pursuing a single purpose if they give low attention weightings to consumer surplus. Additionally, consumer surplus can always be higher when the platforms pursue dual purposes than when pursuing a single purpose, but Pareto improvement may be achieved by the platforms and consumers regardless of the platform competition intensity and the adoption of Strategy I or O.

共享单车平台可以采用两种不同的线下运营策略，即独立运营（策略I）和外包（策略O），即一个平台将其线下运营外包给另一个竞争平台。随着“超越利润”管理理念的兴起，许多共享单车平台开始追求双重目的，即利润和消费者剩余，而不是单一目的，即“纯利润”。在此基础上，本文基于Hotelling框架，考察了双寡头市场下两个共享单车平台的均衡线下运营策略，并分析了平台追求单一目的和双重目的时的平台利润和消费者剩余。得到了几个重要的结果。当平台处于激烈竞争时，两个平台的均衡运营策略为O策略，追求双重目的会损害各自的利润。在弱平台竞争条件下，线下运营的投资协同效应和投资效率都会显著影响平台均衡的线下运营策略，且当平台对消费者剩余的关注权重较低时，追求双重目标的平台可以比追求单一目标的平台获得更高的利润。此外，当平台追求双重目标时，消费者剩余总是比追求单一目标时更高，但无论平台竞争强度和采用策略I或O，平台和消费者都可能实现帕累托改进。

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引用次数: 0

Spatiotemporal Features-Aware relocating for idle vehicles using spatial mean field deep Q network reinforcement learning 基于空间平均场深度Q网络强化学习的空闲车辆时空特征感知定位

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-02 DOI: 10.1016/j.tre.2025.104651

Zhiju Chen , Kai Liu , Jiangbo Wang , Jintao Ke

The cruising behavior of idle ride-hailing vehicles in search of passengers is a key influencing factor that restricts the spatiotemporal balance between online ride-hailing supply and passenger demands. This paper aims to simulate the strategy of transferring idle vehicles in multiple hexagonal partitions to adjacent grid partitions by proposing a spatiotemporal features-aware relocating approach (STFAR) that integrates spatiotemporal features of ride hailing into deep reinforcement learning. Specifically, spatial clustering algorithm and time series clustering algorithm are used to identify the spatiotemporal pattern of ride-hailing demand in each hexagonal partition. In addition, the direction of central hot spot is determined by accurately predicting the future short-term travel demand of each hexagonal partition. Finally, a spatial mean field deep Q network (SMFDQN) reinforcement learning method which regards the hexagonal partition as limited and fixed numbers spatial multi-agents is proposed to optimize the efficiency of idle vehicle transfer. STFAR improves the SMFDQN method by integrating the above spatiotemporal features into state space and action space designs and effectively improves the supply and demand balance in the entire region. Experiments based on Didi Chuxing order data during a certain time period in Chengdu showed that STFAR increases the cumulative order revenue by 3.64%, increases the completion rate of demand by 4.03%, and increases the dispatched rate of idle vehicles by 2.98% compared with the state-of-the-art algorithms.

空闲网约车寻客巡航行为是制约网约车供需时空平衡的关键影响因素。本文提出了一种时空特征感知的重新定位方法（STFAR），该方法将网约车的时空特征集成到深度强化学习中，旨在模拟将多个六边形分区中的闲置车辆转移到相邻网格分区的策略。具体而言，利用空间聚类算法和时间序列聚类算法识别每个六边形分区内网约车需求的时空格局。此外，通过对各六边形分区未来短期出行需求的准确预测，确定中心热点的方向。最后，提出了一种将六边形划分为有限固定数量的空间多智能体的空间均场深度Q网络（SMFDQN）强化学习方法来优化闲置车辆转移效率。STFAR改进了SMFDQN方法，将上述时空特征整合到状态空间和动作空间设计中，有效改善了整个区域的供需平衡。基于滴滴出行在成都某时间段的订单数据进行的实验表明，与现有算法相比，STFAR算法使累计订单收入提高3.64%，使需求完成率提高4.03%，使闲置车辆调度率提高2.98%。

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引用次数: 0

The flood fighting problem: A basic model and construction heuristics 防洪问题：一个基本模型及施工启发式

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2026-01-02 DOI: 10.1016/j.tre.2025.104636

Karolin Eisele, Alf Kimms

Natural disasters such as floods occur more and more frequently due to climate change and claim many victims. If protective measures such as floodplains and dams are not sufficient or are damaged, emergency services must be deployed. In order to be able to deploy them as effectively as possible, we present a model for emergency services planning in the event of flooding. The mathematical model is based on the idea that the area of interest is subdivided into cells and snapshots of the situation are considered at discrete time periods. This way, we can model the spread of water over time taking the specific profile of the terrain into account. Also, the locations and the movement of the emergency teams can be described with user–specified granularity. Since solving such models optimally is out of the scope of today’s computational capabilities, we discuss several variants of so–called construction heuristics. Such methods run fast and produce results that help to assess a flood situation and about what can be achieved over time by fighting the floods. Such insights may not only help after the occurrence of an event, but also in advance in order to be prepared better. In a computational study the performance of heuristics based in simple priority rules is studied.

由于气候变化，洪水等自然灾害越来越频繁地发生，并造成许多受害者。如果洪泛区和水坝等保护措施不够或遭到破坏，就必须部署紧急服务。为了能够尽可能有效地部署它们，我们提出了一个在发生洪水时进行应急服务规划的模型。数学模型是基于这样的思想，即感兴趣的区域被细分为单元，并且在离散的时间段考虑情况的快照。这样，我们就可以在考虑到地形的特定剖面的情况下，对水随时间的扩散进行建模。此外，可以用用户指定的粒度描述应急小组的位置和移动情况。由于以最佳方式求解此类模型超出了当今计算能力的范围，因此我们讨论了所谓的构造启发式的几种变体。这种方法运行迅速，产生的结果有助于评估洪水情况，以及随着时间的推移，通过抗洪可以取得什么成果。这样的洞见不仅可以在事件发生后有所帮助，还可以提前做好准备。在计算研究中，研究了基于简单优先规则的启发式算法的性能。

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引用次数: 0

Robust design and pricing of electric vehicle battery reuse network by tailored branch-and-cut algorithm 基于分支切断算法的电动汽车电池再利用网络稳健设计与定价

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-31 DOI: 10.1016/j.tre.2025.104643

Qi Wang , Yankui Liu , Guoqing Zhang

The rapid proliferation of electric vehicles (EVs) has led to a significant increase in the quantity of used electric vehicle batteries (EVBs). This necessitates the design of a waste reverse supply chain to reuse and recycle EVBs and protect the environment. This paper examines an integrated reuse network design and pricing problem for EVBs, which involves two stakeholders: an echelon utilization enterprise (leader) and a recycling company (follower). Two stakeholders interact through a hierarchical decision-making process under the uncertainty of return quantity. To tackle this problem, we present two bilevel globalized distributionally robust (GDR) design and pricing models. The leader optimizes the locations of collection and echelon utilization centers, the transportation of used EVBs, and pricing strategies to maximize profit. The follower determines the quantity of used EVBs to purchase for dismantling and recycling in order to maximize profit. We derive computationally tractable reformulations of GDR expectation and chance constraints using Lagrangian duality and conjugate function. To efficiently solve the resulting joint chance-constrained model, we propose a tailored branch-and-cut (B&C) algorithm incorporating a strengthened formulation. A real-world case study is conducted to validate the superiority of the proposed methods. Results demonstrate that the globalized distributionally robust optimization models exhibit greater robustness than stochastic optimization models. The computational performance of the tailored B&C algorithm incorporating a strengthened formulation is assessed compared to the standard solver. We also analyze the impact of globalized sensitivity parameter, Wasserstein radius, norm choice, and tolerance level on profitability and provide decision-makers with insights for choosing parameters.

随着电动汽车的快速发展，废旧电动汽车电池的数量也在不断增加。这就需要设计一个废物逆向供应链，以重新利用和回收evb并保护环境。本文研究了一个综合再利用网络设计与定价问题，该问题涉及两个利益相关者：梯队利用企业（领导者）和回收企业（追随者）。在回报数量不确定的情况下，两个利益相关者通过层次决策过程相互作用。为了解决这个问题，我们提出了两个双层全球化分布式鲁棒性（GDR）设计和定价模型。领导者优化收集和梯队利用中心的位置，二手evb的运输和定价策略，以实现利润最大化。追随者决定购买报废evb的数量，用于拆解和回收，以实现利润最大化。我们利用拉格朗日对偶性和共轭函数导出了GDR期望和机会约束的计算上易于处理的重新表述。为了有效地解决由此产生的联合机会约束模型，我们提出了一种包含强化公式的定制分支和切割（B&；C）算法。通过实际案例研究，验证了所提方法的优越性。结果表明，全球化分布鲁棒优化模型比随机优化模型具有更强的鲁棒性。与标准求解器相比，结合强化公式的定制B&；C算法的计算性能进行了评估。我们还分析了全球化敏感性参数、Wasserstein半径、规范选择和容忍度对盈利能力的影响，为决策者选择参数提供了见解。

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引用次数: 0

Train timetable optimization for urban railway systems under the virtual formation mode combined with the rolling stock utilization strategy 虚拟编队模式下结合车辆利用策略的城市轨道系统列车时刻表优化

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-30 DOI: 10.1016/j.tre.2025.104641

Nan Zheng, Shukai Li, Yin Yuan, Dongfan Xie

The distribution of passenger demands on certain urban railway lines exhibits obvious spatiotemporal imbalances, posing challenges for the traditional fixed formation mode. This paper presents the optimization of the virtual formation train timetable and rolling stock utilization strategy, which aims to maximize the quantity of connections and minimize the number of detained passengers. A mixed-integer nonlinear programming model (MINLP) is formulated to characterize this problem, in which the coupling/decoupling operations between different types of rolling stock are considered. By applying linearization techniques, the aforementioned MINLP model can be transformed into a mixed-integer linear programming (MILP) model. To effectively address the model, a two-stage (TS) optimization approach is designed to decompose the original problem into two sequential steps for the solution. In the first stage, a reduced-scale optimization problem is solved, focusing solely on a subset of services; then, the partial binary variables obtained from the first stage are incorporated into the original problem for further resolution in the second stage. Furthermore, we design an accelerated technique of bound contraction based on logical inference to enhance the solving efficiency of the second stage. Five sets of numerical experiments based on the Beijing metro Yizhuang line are conducted to verify the effectiveness and practicability of the model and algorithm. The experimental results illustrate that the virtual formation mode can effectively address the spatiotemporal imbalances of passenger demands on the line. The proposed TS approach is also proven to exhibit greater efficiency than traditional heuristic algorithms, such as genetic algorithm (GA), for large-scale problems.

城市轨道交通客运需求分布呈现明显的时空不平衡，对传统的固定队形模式提出了挑战。本文提出了以最大连接量和最小滞留旅客为目标的虚拟组队列车时刻表优化和车辆利用策略。考虑不同类型车辆之间的耦合/解耦操作，建立了混合整数非线性规划模型（MINLP）。通过应用线性化技术，可以将上述MINLP模型转化为混合整数线性规划（MILP）模型。为了有效地解决该模型，设计了一种两阶段优化方法，将原始问题分解为两个连续的步骤来求解。在第一阶段，解决一个缩小规模的优化问题，只关注服务的子集；然后，将第一阶段得到的部分二元变量纳入原问题，以便在第二阶段进一步求解。此外，我们设计了一种基于逻辑推理的界缩加速技术，以提高第二阶段的求解效率。以北京地铁亦庄线为例，进行了5组数值实验，验证了该模型和算法的有效性和实用性。实验结果表明，虚拟排队模式能有效解决线路上乘客需求的时空不平衡问题。对于大规模问题，所提出的TS方法也被证明比传统的启发式算法（如遗传算法（GA））表现出更高的效率。

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引用次数: 0

Integrated routing of drones and public transportation vehicles for simultaneous parcel pickup and delivery 无人机和公共交通工具的综合路线，同时收取和递送包裹

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-30 DOI: 10.1016/j.tre.2025.104594

Wei Xu , Zhixiao Wang , Zhenjie Zheng , Zhengli Wang , Hai Yang

The integration of drones with trucks or public transportation (PT) vehicles has become an increasingly popular strategy to extend the operational range of drone-based deliveries. Compared to truck-drone systems, PT-drone integration leverages existing public vehicles (e.g., buses) without the need for additional ground fleets, thereby reducing operational costs and environmental impact. However, existing studies on PT-drone integration have primarily focused on one-way parcel delivery tasks, whereas the simultaneous pickup and delivery (SPD) service remains underexplored. In this study, we develop a mixed integer linear programming (MILP) model that enables the effective synchronization of drone-based SPD service with fixed PT timetables and routes. Specifically, we first construct a time-expanded graph that encodes the spatial distribution of PT stations and the temporal scheduling of their associated trips across different lines. To capture the operational dynamics of drone-based SPD, we then formulate energy consumption as a function of flight time and payload, both of which evolve with routing decisions. Finally, the MILP model is solved to minimize both service time and system cost while ensuring compliance with operational constraints. We derive a set of valid inequalities to tighten the MILP formulation and enhance its overall computational efficiency. For large-scale instances, we also design a tailored Adaptive Large Neighborhood Search (ALNS) algorithm with problem-specific operators. Numerical experiments using real-world data from Nanjing, China, demonstrate the effectiveness of our proposed model in realizing the long-range SPD. The valid inequalities reduce the MILP solver time by 69.15 %, and the ALNS algorithm produces near-optimal solutions within reasonable time.

无人机与卡车或公共交通（PT）车辆的整合已经成为一种日益流行的策略，以扩大无人机交付的操作范围。与卡车无人机系统相比，pt -无人机集成利用现有的公共车辆（如公共汽车），而不需要额外的地面车队，从而降低了运营成本和对环境的影响。然而，现有的pt -无人机集成研究主要集中在单向包裹递送任务上，而同时取件和投递（SPD）服务仍未得到充分探索。在本研究中，我们开发了一个混合整数线性规划（MILP）模型，使基于无人机的SPD服务与固定的PT时间表和路线有效同步。具体而言，我们首先构建了一个时间扩展图，该图编码了PT站的空间分布及其在不同线路上相关行程的时间调度。为了捕捉基于无人机的SPD的操作动态，我们将能耗作为飞行时间和有效载荷的函数，这两者都随着路线决策而变化。最后，对MILP模型进行了求解，以最小化服务时间和系统成本，同时确保符合操作约束。我们推导了一组有效的不等式来收紧MILP公式并提高其整体计算效率。对于大规模实例，我们还设计了一个具有问题特定算子的定制自适应大邻域搜索（ALNS）算法。利用南京的实际数据进行的数值实验证明了该模型在实现远程SPD方面的有效性。有效不等式使MILP求解时间缩短了69.15%，ALNS算法在合理的时间内产生了近似最优解。

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引用次数: 0

Optimal dedicated lane management for mixed traffic with connected and autonomous vehicles accounting for heterogeneous headways and speeds 考虑不同车头和速度的网联车辆和自动驾驶车辆混合交通的最优专用车道管理

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-30 DOI: 10.1016/j.tre.2025.104588

Jeongin Yun , Seungmin Oh , Jinwoo Lee

Connected and autonomous vehicle (CAV) platooning, where a group of CAVs travel closely together at higher speeds, has the potential to improve both traffic capacity and free-flow speed of mixed traffic on roads. In this paper, we present a dedicated lane management framework based on an analytical understanding of mixed traffic involving CAVs and human-driven vehicles (HDVs), taking into account diverse headways, free-flow speeds, and CAV penetration rates. This framework is a bi-criteria optimization that maximizes both traffic capacity and free-flow time-mean speed of a multi-lane section, where each lane can be a non-dedicated lane, a CAV-dedicated lane, or an HDV-dedicated lane. In the capacity-maximizing case, through using both types of dedicated lanes, our approach can consistently maximize capacity across various environmental settings, such as lane numbers, CAV rates, and car-following aggressiveness. The optimal dedicated lane management scheme is summarized as follows: implement HDV-dedicated lane(s) when the total CAV ratio is low, and introduce CAV-dedicated lane(s) otherwise. The scheme aims to consolidate CAVs as much as possible to maximize the number of platooning events. In the capacity-and-speed-maximizing case, CAV-dedicated lane(s) are introduced at lower CAV penetration rates compared to the capacity-maximizing case, with greater emphasis on speed, resulting in more complete separation between CAVs and HDVs. In the bi-criteria optimization, a Pareto solution set is found, illustrating the tradeoff between two objectives, which allows transportation planners flexibility in selecting lane management strategies in accordance with operational priorities. Finally, we validate the proposed framework through agent-based simulations in VISSIM, demonstrating its effectiveness.

联网和自动驾驶汽车（CAV）队列，即一组自动驾驶汽车以更高的速度紧密地行驶，有可能提高道路上混合交通的交通容量和自由流动速度。在本文中，我们提出了一个专用车道管理框架，该框架基于对包括自动驾驶汽车和人类驾驶汽车（HDVs）的混合交通的分析理解，考虑到不同的前方、自由流速度和自动驾驶汽车普及率。该框架是一个双标准优化，最大限度地提高了多车道部分的交通容量和自由流时间平均速度，其中每个车道可以是非专用车道，cav专用车道或hdv专用车道。在容量最大化的情况下，通过使用两种类型的专用车道，我们的方法可以在各种环境设置（如车道数、CAV率和车辆跟随侵略性）中始终如一地最大化容量。最优的专用车道管理方案总结为：当总CAV比较低时，采用hdv专用车道；当总CAV比较低时，采用CAV专用车道。该方案旨在尽可能多地整合自动驾驶汽车，以最大化队列事件的数量。在容量和速度最大化的情况下，与容量最大化的情况相比，引入CAV专用车道的CAV渗透率较低，更强调速度，从而使CAV和hdv之间更加完全分离。在双标准优化中，找到了一个帕累托解集，说明了两个目标之间的权衡，使交通规划者能够根据运营优先级灵活地选择车道管理策略。最后，通过VISSIM中基于agent的仿真验证了该框架的有效性。

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引用次数: 0

Resilient supply chain network design under super-disruption considering inter-arrival time dependency: a new data-driven stochastic optimization approach 考虑到达时间依赖的超中断弹性供应链网络设计：一种新的数据驱动随机优化方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-30 DOI: 10.1016/j.tre.2025.104615

Mohammad Mahdi Vali-Siar , Hamid Tikani , Emrah Demir , Yousof Shamstabar

During large-scale disruptions, particularly super-disruptions such as global pandemics or large-scale natural disasters, supply chains are exposed to significant adverse impacts. This paper addresses the resilience in a supply chain network design problem under disruption risk by explicitly modeling the dependency between the inter-arrival times of disruptive events and severity of their consequences. A novel data-driven stochastic optimization framework is proposed to consider the ripple effects that typically propagate across supply chain networks following severe disruptions. Specifically, we have devised a hybrid methodology that integrates a clustering algorithm (unsupervised machine learning technique), a phase-type disruption model, and a two-stage stochastic model. To elaborate, a genetic-based clustering algorithm is used to identify the structure dependencies in the input data. Phase-type distributions and their associated theorems are then used to determine the probability distributions of disruptions. A novel mathematical model is developed to design the supply chain using the scenarios generated based on the obtained distributions, which is then solved using the Lagrangian decomposition combined with a new hyper-matheuristic algorithm. The computational efficiency and practical value of the proposed approach are demonstrated through a real-world case study. The findings highlight the effectiveness of developed methodology in designing a resilient supply chain, the proposed resilience strategies substantially improve the supply chain’s performance compared to a non-resilient approach.

在大规模中断期间，特别是全球流行病或大规模自然灾害等超级中断期间，供应链面临重大不利影响。本文通过明确建模破坏事件的间隔到达时间与其后果严重程度之间的依赖关系，解决了中断风险下供应链网络设计问题中的弹性问题。提出了一种新的数据驱动的随机优化框架，以考虑严重中断后通常在供应链网络中传播的连锁反应。具体而言，我们设计了一种混合方法，该方法集成了聚类算法（无监督机器学习技术），阶段型中断模型和两阶段随机模型。为此，采用基于遗传的聚类算法来识别输入数据中的结构依赖关系。然后使用相型分布及其相关定理来确定中断的概率分布。建立了一种新的数学模型，利用基于得到的分布生成的场景来设计供应链，然后使用拉格朗日分解结合新的超数学算法对其进行求解。通过实例分析，验证了该方法的计算效率和实用价值。研究结果强调了开发方法在设计弹性供应链方面的有效性，与非弹性方法相比，提出的弹性策略大大提高了供应链的绩效。

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