Transportation Research Part E-Logistics and Transportation Review最新文献_第5页

AI-driven Just-In-Time coordination between port and ships: Advancing maritime decarbonization 人工智能驱动的港口和船舶之间的及时协调：推进海上脱碳

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-19 DOI: 10.1016/j.tre.2025.104569

Son Nguyen , Xiuju Fu , Liangbin Zhao , Haiyan Xu , Xiaocai Zhang , Ning Li , Wei Zhang , Xiao Feng Yin , Ogawa Daichi , Jimmy Koh , Qin Zheng

The hurry-up-and-wait behaviors and the bunching of arriving ships at ports are global maritime logistics challenges, causing backlogs and delays, high traffic density, and unnecessary fuel consumption and emissions. Just-in-time arrival (JITA) of vessels utilizes potential waiting time for slow-steaming and alleviates pressure on port resources. However, practical frameworks and solutions supporting JITA are still scarce due to the involvement of different stakeholders and the complexity of operational orchestration. This study proposes a comprehensive JITA framework to enable intelligent port-ship coordination that leverages maritime big data and AI-powered analytics to address dynamic operating conditions (e.g., ship speed optimization, metocean conditions, and port resource availability). Integrating Collaborative Decision-Making and System of Systems based on Supply Chain Collaboration, the combinative framework efficiently enables JITA for early speed optimization, reliability of effectiveness, quantifiable benefits, and management and minimalism of data requirements. Utilizing the framework, AI for Just-In-Time (AI4JIT) was developed as a JITA decision support tool to provide descriptive, predictive, and prescriptive analytics for port-ship coordination. Adapting to one of the biggest ports in Asia, the integrated system achieved 6.45 %–7.39 % fuel and emission savings in 2.94 % port calls based on the test using historical data. Apart from presenting a well-structured framework to JITA system design and implementation, this study demonstrates the capability of AI-augmented collaborative optimization in maritime logistics and offers quantifiable benefits for decision-making. This research also paves the way for broader adoption of JITA and AI in the industry, with identified knowledge gaps in data, risk, and quality governance.

“急着等待”的行为和港口的船舶聚集是全球海运物流面临的挑战，造成积压和延误，交通密度高，以及不必要的燃料消耗和排放。船舶准时到达（JITA）利用了潜在的慢速航行等待时间，减轻了港口资源的压力。然而，由于涉及不同涉众和操作编排的复杂性，支持JITA的实用框架和解决方案仍然很少。本研究提出了一个全面的JITA框架，以实现智能港船协调，利用海事大数据和人工智能分析来解决动态操作条件（例如，船速优化、海洋条件和港口资源可用性）。结合基于供应链协作的协同决策和系统的系统，该组合框架有效地使JITA能够实现早期速度优化、有效性可靠性、可量化效益以及数据需求的管理和最小化。利用该框架，AI for just - time （AI4JIT）被开发为JITA决策支持工具，为港船协调提供描述性、预测性和规范性分析。根据使用历史数据的测试，该综合系统在2.94%的港口停靠中实现了6.45% - 7.39%的燃料和排放节约。除了为JITA系统设计和实施提供结构良好的框架外，本研究还展示了人工智能增强海上物流协同优化的能力，并为决策提供了可量化的效益。这项研究还为JITA和人工智能在行业中的广泛应用铺平了道路，并确定了数据、风险和质量治理方面的知识差距。

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引用次数: 0

Order allocation and vehicle routing with collaborative pickup and delivery by crowdsourced and contracted couriers in a two-echelon urban logistics system 两级城市物流系统中的订单分配和车辆路线，由众包和合同快递员协同取货和交付

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-19 DOI: 10.1016/j.tre.2025.104598

Dongze Li, Wenbo Sun, Fangni Zhang

This study investigates the order allocation and vehicle routing problems for an integrated logistics system that employs both crowdsourced and contracted couriers. Leveraging uncontracted travelers from the crowd, referred to as crowdsourced couriers, crowdshipping can provide flexible and cost-effective delivery service. However, this also introduces unique challenges, stemming from the limited delivery ranges of crowdsourced couriers constrained by their personal travel purposes, and reliability concerns due to the absence of formal labor contracts. Moreover, fulfilling both intracity and intercity pickup and delivery tasks mandates the incorporation of consolidation and transshipment strategies. To address these complexities, the paper presents a pickup and delivery problem with crowdshipping, transshipment, and consolidation (PDPCTC) model that jointly coordinates crowdsourced and contracted couriers in a two-echelon urban logistics system to fulfill demand across intracity and intercity. The study formulates the PDPCTC as a Mixed Integer Linear Programming (MILP) model to optimize order allocation and routing with the objective of minimizing the total delivery cost across the network. For scalability of our model in real-world scenarios, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. Numerical results across various simulated and real-world scenarios demonstrate that integrating crowdshipping into pickup and delivery services can significantly reduce operational costs, yielding considerable economic benefits. Sensitivity analysis further suggests that logistics costs can be reduced by attracting more potential crowdsourced couriers, lowering their compensation rate, and increasing the number of service points within a certain limit.

本文研究了一个采用众包快递员和合同快递员的综合物流系统的订单分配和车辆路线问题。利用人群中未签约的旅行者，即众包快递员，众包快递可以提供灵活且具有成本效益的配送服务。然而，这也带来了独特的挑战，来自于受个人旅行目的限制的众包快递员的配送范围有限，以及由于缺乏正式的劳动合同而引起的可靠性问题。此外，完成城市内和城市间的取货和交付任务需要合并和转运策略。为了解决这些复杂问题，本文提出了一个采用众包、转运和整合（PDPCTC）模型的取货和配送问题，该模型在一个两级城市物流系统中共同协调众包和合同快递员，以满足跨城市和城际的需求。该研究将PDPCTC构建为一个混合整数线性规划（MILP）模型，以最小化整个网络的总交付成本为目标，优化订单分配和路由。为了提高模型在实际场景中的可扩展性，我们开发了一种自适应大邻域搜索（ALNS）算法。各种模拟和现实场景的数值结果表明，将众筹整合到取货和送货服务中可以显著降低运营成本，产生可观的经济效益。敏感度分析进一步表明，在一定范围内，可以通过吸引更多潜在的众包快递员，降低他们的补偿率，增加服务点数量来降低物流成本。

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引用次数: 0

Dynamic speed trajectory generation for virtual coupling train operations with multi-state transitions in rail transit system 轨道交通系统多状态转换虚拟耦合列车运行的动态速度轨迹生成

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-19 DOI: 10.1016/j.tre.2025.104568

Haili Chen , Shukai Li , Xi Wang , Hongwei Wang , Yiwen Zhang

The development of train control technology has facilitated the investigation into more flexible organization modes in railway industry. This paper studies the speed trajectory generation problem with the coupling and decoupling process in rail transit systems under the model predictive control framework. Considering the constraints of train dynamic operations, boundary conditions, and train safety distance, a nonlinear optimization model with disjunctive constraints is formulated to capture the complex logic relationship existing in the process containing multi-state transitions. To obtain an effective speed trajectory in real-time, a logic-based algorithm is designed, providing an efficient way to deal with the model with disjunctive constraints. An outer approximation cut is introduced to the mixed-integer linear programming master problem, which is solved alternatively with the nonlinear programming sub-problem. Numerical simulations are conducted to validate the effectiveness of the proposed model and method. Computational results demonstrate that the proposed speed trajectory generation method effectively yields smooth speed trajectories, leading to a better coupling and decoupling performance.

列车控制技术的发展为探索更灵活的铁路行业组织模式提供了便利。本文研究了模型预测控制框架下轨道交通系统耦合解耦过程中的速度轨迹生成问题。考虑列车动态运行约束、边界条件约束和列车安全距离约束，建立了带析取约束的非线性优化模型，以捕捉包含多状态转换过程中存在的复杂逻辑关系。为了实时获得有效的速度轨迹，设计了一种基于逻辑的算法，为具有析取约束的模型提供了一种有效的处理方法。将外逼近割引入混合整数线性规划主问题，并与非线性规划子问题交替求解。通过数值仿真验证了所提模型和方法的有效性。计算结果表明，所提出的速度轨迹生成方法能有效地生成平滑的速度轨迹，具有较好的耦合解耦性能。

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引用次数: 0

Regional variations in green transformation of European ports: A robust quantile stochastic frontier analysis 欧洲港口绿色转型的区域差异：稳健分位数随机前沿分析

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-17 DOI: 10.1016/j.tre.2025.104602

Xiangyang Tao, Jiafu Li, Jingke Hong

Facing rising sustainability pressures, green transformation is crucial for European ports to enhance operational capacity and meet environmental targets. This study develops a Green Transformation Index (GTI) to assess the performance of 70 European ports (2006–2018) in balancing operational growth with pollution reduction. We apply a robust composite assessment method combining directional distance functions and quantile stochastic frontier analysis. Analysis reveals three key trends. Firstly, the pace of green transformation closely aligns with environmental regulations; stricter sulfur limits implemented in 2010 and 2015 significantly boosted GTI scores. Conversely, the 2007–2009 financial crisis, causing reduced cargo volumes, temporarily slowed environmental improvement, highlighting economic instability as a constraint. Secondly, significant variation exists in ports’ GTI outcomes. Certain larger ports achieved sustained improvements by proactively investing in efficient infrastructure and pollution control. Others expanded operations more rapidly than adopting green technologies, resulting in lower GTI scores. Similar national-level variations reflect differing policy priorities and technical capabilities. Thirdly, early adoption of pollution control technology proved crucial. Ports installing equipment before new regulations took effect saw swifter gains in environmental performance, while slower adopters maintained lower levels for longer periods. Methodologically, the robustness of the new approach was verified through comparisons with conventional benchmark methods. These findings collectively suggest that effective port green transformation requires tailored governance: ports with lower GTI performance need prioritized support for technology adoption, while top performers require deepened operational coordination to sustain gains.

面对日益增长的可持续性压力，绿色转型对欧洲港口提高运营能力和实现环境目标至关重要。本研究开发了绿色转型指数（GTI），以评估70个欧洲港口（2006-2018年）在平衡运营增长和减少污染方面的表现。我们采用了一种结合方向距离函数和分位数随机前沿分析的鲁棒复合评价方法。分析揭示了三个关键趋势。首先，绿色转型的步伐与环境法规密切相关；2010年和2015年实施的更严格的硫限制显著提高了GTI分数。相反，2007-2009年的金融危机导致货运量减少，暂时减缓了环境改善，突显出经济不稳定是一个制约因素。其次，港口的GTI结果存在显著差异。某些大型港口通过积极投资于高效的基础设施和污染控制，实现了持续改善。其他企业扩张业务的速度快于采用绿色技术，导致GTI得分较低。类似的国家层面差异反映了不同的政策重点和技术能力。第三，及早采用污染控制技术至关重要。在新法规生效之前安装设备的港口在环境绩效方面的收益更快，而采用较慢的港口在较长时间内保持较低的水平。在方法上，通过与传统基准方法的比较，验证了新方法的鲁棒性。这些研究结果共同表明，有效的港口绿色转型需要量身定制的治理：GTI绩效较低的港口需要优先支持技术采用，而绩效最高的港口需要深化运营协调以保持收益。

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引用次数: 0

Bayesian optimization for traffic sensor deployment: Integrating network flow estimation into location planning 交通传感器部署的贝叶斯优化：将网络流量估计集成到位置规划中

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-17 DOI: 10.1016/j.tre.2025.104593

Jiping Xing , Zhou Jia , Tong Wu , Tianli Tang , Zhiyuan Liu

Accurate network flow estimation (NFE) is essential for effective traffic management, with its performance highly reliant on optimal sensor placement. Traditional two-stage methodologies, which first optimize sensor locations using surrogate observability metrics and then train an estimation model, often lead to suboptimal performance. This study proposes a unified bi-level black-box optimization framework that jointly optimizes sensor deployment at the upper level and NFE model training at the lower level. The framework explicitly accounts for the zero-shot nature of NFE tasks by excluding unobservable link data from training and is compatible with various NFE models, with a transfer learning-based estimator used as a representative example. The resulting black-box integer optimization problem is solved efficiently using an Embedding Bayesian Optimization algorithm, further enhanced by a hybrid random dictionary generation method that improves the numerical stability of Gaussian process fitting. Experiments on the Nanjing real-world case and Eastern Massachusetts networks demonstrate substantial improvements in estimation performance compared to the traditional two-stage method, highlighting the framework’s scalability and transferability for optimizing sensor placement in intelligent transportation systems.

准确的网络流量估计（NFE）对于有效的交通管理至关重要，其性能高度依赖于最优的传感器位置。传统的两阶段方法首先使用替代可观测性度量优化传感器位置，然后训练估计模型，这通常会导致性能不理想。本研究提出了一个统一的双层黑盒优化框架，上层共同优化传感器部署，下层共同优化NFE模型训练。该框架通过从训练中排除不可观察的链接数据，明确地说明了NFE任务的零射击性质，并且与各种NFE模型兼容，并使用基于迁移学习的估计器作为代表性示例。采用嵌入贝叶斯优化算法有效地解决了黑盒整数优化问题，并通过混合随机字典生成方法进一步增强了黑盒整数优化问题，提高了高斯过程拟合的数值稳定性。南京实际案例和马萨诸塞州东部网络的实验表明，与传统的两阶段方法相比，估计性能有了实质性的改进，突出了该框架在优化智能交通系统中传感器放置方面的可扩展性和可移植性。

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引用次数: 0

Integrated optimization of train timetabling, stop planning, rolling stock maintenance and assignment with flexible train composition and short-turning strategies 列车调度、停站计划、车辆维护和分配的综合优化，灵活的列车组成和短转弯策略

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-16 DOI: 10.1016/j.tre.2025.104566

Tao Li , Dewei Li , Haotian Ji , Jialun Ma

Efficient utilization of rolling stock stands as an important goal for railway enterprises. Achieving it requires assigning appropriate rolling stock to train paths that cover a given set of routes in the operational plan, while accounting for practical constraints such as maintenance schedules and depot capacity. Although numerous studies have focused on minimizing operating costs by reducing the number of rolling stocks to develop operational plan, they often ignore practical requirements or neglect rolling stock assignment, rendering plans infeasible in practice. To tackle this problem, this paper develops an integrated approach for train timetabling, stop planning, rolling stock maintenance and assignment, while considering circulation rules and operational constraints. The model incorporates constraints such as time- and mileage-based maintenance rules, rolling stock assignments, the initial number of available rolling stocks, the number of stored rolling stocks in the depot. A rolling stock selection variable is introduced to represent the state of available rolling stock and the train formation, and a nonlinear mixed-integer programming model is developed to minimize train operating costs and the passenger travel costs. To solve large-scale real-world problem, the adaptive large neighborhood search (ALNS) algorithm is employed. The effectiveness of the formulated method is verified through numerical experiments and a real-world case study. Results demonstrate that the proposed approach not only guarantees operational feasibility but also reduces the required rolling stock by 5.4–8.6 % and operational costs by approximately 4.9–8.4 % compared to sequential optimization strategies, without significantly compromising passenger service quality. Furthermore, the method offers valuable guidance for decision-makers with diverse preferences.

铁路车辆的高效利用是铁路企业追求的重要目标。要实现这一目标，需要在运营计划中为覆盖一组给定路线的火车路线分配适当的机车车辆，同时考虑到维护时间表和车辆段容量等实际限制。虽然已有大量研究侧重于通过减少车辆数量来制定运营计划，以降低运营成本，但往往忽视实际需求或忽略车辆分配，使计划在实践中不可行。为了解决这一问题，本文提出了一种综合考虑流通规律和运行约束的列车调度、停车规划、机车车辆维修和分配的方法。该模型结合了诸如基于时间和里程的维护规则、机车车辆分配、可用机车车辆的初始数量、车辆段存储的机车车辆数量等约束。引入车辆选择变量来表示可用车辆状态和列车编队状态，建立了以列车运行成本和旅客出行成本最小为目标的非线性混合整数规划模型。为了解决大规模的现实问题，采用了自适应大邻域搜索（ALNS）算法。通过数值实验和实际算例验证了该方法的有效性。结果表明，与顺序优化策略相比，该方法不仅保证了运营的可行性，而且在不显著影响客运服务质量的情况下，将所需的车辆减少了5.4 - 8.6%，运营成本减少了约4.9 - 8.4%。此外，该方法为具有不同偏好的决策者提供了有价值的指导。

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引用次数: 0

Digital twin-enabled cooperative autonomous truck platooning framework for modeling, cost estimation, and decision-making in transportation systems 用于运输系统建模、成本估算和决策的数字孪生协作式自动卡车队列框架

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-16 DOI: 10.1016/j.tre.2025.104596

Xinyue Guo , Yi Li , Songyi Cui , Ray Y. Zhong

Cooperative Autonomous Trucks (CATs) play a critical role in enabling the intelligence and automation of logistics. However, the involvement of multiple carriers, diverse autonomous trucks, and complex transportation environments presents substantial challenges to the large-scale deployment. This study proposes a Digital Twin (DT)-enabled framework for CAT to address these challenges, including transport task synchronization, platoon formation, and revenue estimation. The framework is structured with three interrelated components: (i) a Multiobjective Mixed Integer Programming (MOMIP) model for CATs platooning to optimize task allocation and decision-making, (ii) the DT-enabled kinematic and dynamic models for real-time synchronization and energy estimation, and (iii) real-time control and communication topologies to bridge optimization and practical implementation. A Resource Directive Decomposition (RDD) algorithm is introduced to efficiently solve the MOMIP model by decomposing the problem and applying disjunctive relaxation. Numerical validations show that the RDD algorithm achieves significantly faster convergence than the ϵ-constraint method when MOMIP has more than two objectives. CATs platoon formations achieve energy savings of approximately 10-14 % compared with historical human-driven trucks, and the proposed adaptive control with DT synchronization effectively ensures safety during emergency braking scenarios. An empirical validation using data from a real-world cross-border construction logistics project demonstrates cost savings and enhanced carrier compatibility.

协作式自动驾驶卡车（CATs）在实现物流的智能化和自动化方面发挥着关键作用。然而，多家运输公司、各种自动驾驶卡车和复杂的运输环境的参与，给大规模部署带来了巨大挑战。本研究提出了一个支持数字孪生（DT）的CAT框架，以应对这些挑战，包括运输任务同步、排编队和收入估计。该框架由三个相互关联的组件构成：(i)用于CATs队列优化任务分配和决策的多目标混合整数规划（MOMIP）模型，（ii）用于实时同步和能量估计的dt支持的运动学和动态模型，以及（iii）用于桥梁优化和实际实施的实时控制和通信拓扑结构。引入资源指令分解（RDD）算法，通过对问题进行分解并应用析取松弛来有效求解MOMIP模型。数值验证表明，当MOMIP有两个以上目标时，RDD算法的收敛速度明显快于ϵ-constraint方法。与历史上的人类驾驶卡车相比，CATs车队编队节能约10- 14%，并且所提出的具有DT同步的自适应控制有效地确保了紧急制动场景下的安全。使用现实世界跨境建筑物流项目的数据进行实证验证，证明节省了成本并增强了运营商兼容性。

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引用次数: 0

A three-stage stochastic optimization approach for robust train timetabling and rolling stock planning with virtual (de)coupling 具有虚拟（去）耦合的鲁棒列车调度和机车车辆规划的三阶段随机优化方法

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-15 DOI: 10.1016/j.tre.2025.104590

Nsabimana Buhigiro , Liujiang Kang , Qingying Lai , Huijun Sun , Qianwen Xu , Bardia Mashhoodi

The emergence of suburban clusters has significantly reshaped urban mobility patterns, intensifying the reliance on central business districts for work and commerce. This dependency has led to an asymmetric and time-dependent demand for passengers, characterized by peak-hour congestion in one direction and under-utilization in the opposite direction. Existing studies often address passenger-dependent and time-dependent uncertainties in isolation, neglecting their combined impact on transit operations. To bridge this gap, this study proposes a novel three-stage stochastic optimization model that integrates robust train timetabling and rolling stock planning under virtual (de)coupling, explicitly considering both time-dependent and asymmetric passenger-dependent uncertainties. Passenger-dependent uncertainty is modeled through stochastic variations in arrival rates, while time-dependent uncertainty captures operational delays in running and dwell times. The model reformulation into a tractable mixed-integer program leverages the superposition principle and the mean conditional-value-at-risk criterion. This approach simultaneously optimizes nominal and robust scheduling aspects, including passenger loading, train timetables, rolling stock assignments, and virtual (de)coupling decisions. The primary objective is to develop an integrated robust train timetable and rolling stock plan that minimizes schedule deviations, unserved passengers, and operational costs. To enhance computational efficiency, a branch-and-price algorithm based on Dantzig-Wolfe decomposition is introduced, decomposing the problem into a master problem for rolling stock planning and sub-problems for robust train timetable and passenger loading process. The proposed methodology is validated through extensive computational experiments, including small- and medium-scale examples, as well as a real-world case study of the Beijing Batong metro line using historical data. The results demonstrate the effectiveness of the approach in generating robust train schedules that mitigate operational disruptions while optimizing rolling stock utilization.

郊区集群的出现极大地改变了城市的流动模式，加强了对中心商务区的工作和商业依赖。这种依赖导致了乘客需求的不对称和时间依赖，其特点是一个方向的高峰时段拥堵，而相反方向的利用率不足。现有的研究往往孤立地处理依赖乘客和依赖时间的不确定性，而忽略了它们对过境业务的综合影响。为了弥补这一差距，本研究提出了一种新的三阶段随机优化模型，该模型集成了虚拟（去）耦合下的鲁棒列车调度和机车车辆规划，明确考虑了时间相关和非对称乘客相关的不确定性。乘客依赖的不确定性通过到达率的随机变化来建模，而时间依赖的不确定性捕获运行和停留时间的操作延迟。利用叠加原理和平均条件风险值准则，将模型重构为可处理的混合整数规划。该方法同时优化了名义调度和鲁棒调度方面，包括乘客装载、列车时刻表、机车车辆分配和虚拟（分离）耦合决策。主要目标是制定一个综合可靠的列车时刻表和机车车辆计划，以最大限度地减少时刻表偏差、未服务的乘客和运营成本。为了提高计算效率，引入了基于dantzigg - wolfe分解的分支价格算法，将该问题分解为车辆规划主问题和鲁棒列车时刻表子问题和旅客装车过程子问题。通过大量的计算实验，包括中小规模的例子，以及使用历史数据的北京八通地铁的现实案例研究，验证了所提出的方法。结果表明，该方法在生成稳健的列车时刻表方面是有效的，可以减轻运营中断，同时优化机车车辆利用率。

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引用次数: 0

Dynamic vehicle relocation and assignment for an intelligent vehicle platooning transit system 智能车辆队列交通系统的动态车辆迁移与分配

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-15 DOI: 10.1016/j.tre.2025.104556

Aijing Su , Bing Wu , Chi Xie

An emerging on-demand urban transit system, namely, Intelligent Vehicle Platooning Transit (IVPT), promises to provide rapid, flexible, comfortable and affordable passenger mobility services. IVPT features direct station-to-station transit services by utilizing dynamically formed platoons possibly composed of different types of vehicles running on exclusive traffic lanes and through intersections controlled by transit signal priority. This paper defines, formulates, and solves a dynamic vehicle relocation and assignment problem for the daily operations of an IVPT system. The focus is on the modeling and solution methods for the system’s real-time vehicle relocation and assignment decisions, which primarily include two parts: 1) Optimal relocation of service vehicles from low-demand stations to high-demand stations so as to mitigate the supply-demand imbalance; 2) optimal assignment of vehicles to carry matched passengers from their boarding stations to alighting stations. With its implementation in the rolling horizon framework, a mixed integer linear programming model is formulated, and both an exact and a heuristic algorithm are designed and tested. The exact method is powered by Lagrangian relaxation, embedding a dynamic programming algorithm for solving its decomposed relaxed Lagrangian subproblems and a tailored vehicle-removing-and-reloading algorithm for finding feasible solutions. The heuristic method relies on adaptive large neighborhood search, integrating several problem-specific destroy and repair subheuristics to effectively explore the solution space and enhance search diversity. A few illustrative numerical examples and a real-world case study are employed to validate the advantages of this new demand-responsive, platoon-based transit system, along with the efficacy and performance of the proposed solution methods for real-time vehicle relocation and assignment.

一种新兴的按需城市交通系统，即智能车辆队列交通（IVPT），有望提供快速、灵活、舒适和负担得起的乘客出行服务。IVPT的特点是直接站到站的交通服务，利用动态形成的队列，可能由不同类型的车辆组成，在专用车道上行驶，并通过由交通信号优先控制的交叉口。本文定义、制定并解决了IVPT系统日常运行中的动态车辆搬迁与分配问题。重点研究了系统实时车辆迁移与分配决策的建模与求解方法，主要包括两部分：1)服务车辆从低需求站点向高需求站点的最优迁移，以缓解供需不平衡；2)优化车辆配置，将匹配的乘客从上车站运送到下车站。在滚动地平线框架下，建立了混合整数线性规划模型，设计了精确算法和启发式算法并进行了测试。该方法以拉格朗日松弛为动力，嵌入了一个求解其分解的松弛拉格朗日子问题的动态规划算法和一个寻找可行解的定制车辆移除和重新装载算法。启发式方法依靠自适应大邻域搜索，结合多个针对特定问题的破坏和修复子启发式方法，有效地探索解空间，增强搜索多样性。通过几个数值示例和一个实际案例研究，验证了这种基于队列的需求响应型交通系统的优势，以及所提出的实时车辆迁移和分配解决方案的有效性和性能。

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引用次数: 0

Conditional responsive pricing contracts: Balancing flexibility and stability in supply chains with fluctuating input costs 条件响应式定价合同：在投入成本波动的情况下平衡供应链的灵活性和稳定性

IF 8.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-12-15 DOI: 10.1016/j.tre.2025.104592

Xiaofan Hu, Xiangrong Song, Jianbin Li

This paper explores contract design for supply chains in which upstream manufacturers are exposed to raw material price volatility. To balance pricing flexibility and stability, we propose a novel Conditional Responsive Pricing contract. Unlike conventional Weighted Pricing contracts, which set wholesale prices as a linear combination of futures and spot prices constrained to full exposure, our proposed contract introduces two key innovations. First, it allows supply chain partners to either amplify (“over-exposure”) or dampen the responsiveness of wholesale prices to spot price fluctuations. Second, it incorporates a tolerance threshold that determines when price adjustments are triggered, thereby providing selective price stability. We analyze the optimal contract design in a single-manufacturer, single-retailer supply chain. Our analysis reveals that the manufacturer benefits from dampening spot-price fluctuations when employing a medium level of market responsiveness, but becomes worse off as responsiveness increases further. Conversely, strategically amplifying wholesale price responsiveness to spot price volatility can significantly increase the retailer’s profit and overall supply chain efficiency. Regarding the tolerance threshold, we discover a volatility shield effect that demonstrates how threshold-based pricing can strategically balance the opposing profit interests of the manufacturer and retailer. Increasing the threshold benefits the retailer but harms the manufacturer at low responsiveness levels, with the effect reversing when responsiveness is high. Our comparative analysis shows that although the manufacturer’s maximum attainable profit is similar under both contracts, the Conditional Responsive Pricing contract yields higher retailer’s profit and greater overall supply chain efficiency than the Weighted Pricing contract.

本文探讨了上游制造商面临原材料价格波动的供应链合同设计问题。为了平衡定价的灵活性和稳定性，我们提出了一种新的条件响应式定价合同。传统的加权定价合约将批发价格设定为期货和现货价格的线性组合，限制在充分暴露的情况下，与此不同，我们的提议合约引入了两个关键创新。首先，它允许供应链合作伙伴放大（“过度暴露”）或抑制批发价格对现货价格波动的反应。其次，它包含一个容忍阈值，决定何时触发价格调整，从而提供选择性的价格稳定。本文分析了单制造商、单零售商供应链的最优契约设计。我们的分析表明，当采用中等水平的市场响应性时，制造商从抑制现货价格波动中受益，但随着响应性的进一步增加，情况会变得更糟。相反，战略性地放大批发价格对现货价格波动的响应可以显著提高零售商的利润和整体供应链效率。对于容忍阈值，我们发现了波动屏蔽效应，表明基于阈值的定价如何在战略上平衡制造商和零售商的对立利润利益。在低响应水平下，提高阈值对零售商有利，但对制造商不利，当响应水平高时，效果正好相反。我们的比较分析表明，尽管制造商在两种合同下的最大可获得利润相似，但条件响应定价合同比加权定价合同产生更高的零售商利润和更高的整体供应链效率。

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