Computers & Operations Research最新文献_第10页

A benders-branch-and-cut methodology for global cargo vessel traffic prediction given declining arctic sea ice and changing risks 在北极海冰减少和风险变化的情况下，全球货船交通预测的折枝折枝方法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-02 DOI: 10.1016/j.cor.2025.107265

Wenjie Li, Elise Miller-Hooks

Global warming has led to declining sea-ice in the Arctic Ocean, making it easier for ice-class vessels to navigate Arctic waters for greater portions of the year. As sailing conditions in these waters improve over coming decades, these passageways are expected to open for larger portions of the year and to become increasingly viable options for unsupported transit and even open-water vessels. This paper proposes a Benders-branch-and-cut methodology for estimating changes in global maritime cargo flow patterns under future climate scenarios with declining Arctic sea ice. The model accounts for changing incident risk along Arctic passageways and corresponding ice-class vessel and icebreaker escort requirements, lower speeds, increased insurance premiums, higher accident probabilities, and constraints on path-based maximum risk exposure. The resulting mixed-integer program involves path-based, continuous decision variables. The solution technique is applied on a model of the global maritime container network including 80 ports, 76 routes, 426 links and 4,303 legs associated with the world’s largest carrier alliance. Embedded acceleration techniques and a label-correcting algorithm that employs specialized fathoming rules for a non-additive, constrained path subproblem enable solution at this global scale. The outcome is an estimate of seasonal future global maritime trade flows along key global routes and through ports predicted under six climate-related scenarios. Results illustrate that the developed model can provide support to companies, nations and regions as they prepare for a changing global landscape and climate.

全球变暖导致北冰洋的海冰减少，这使得冰级船只在一年中的大部分时间里更容易在北极水域航行。随着未来几十年这些水域的航行条件的改善，这些通道预计将在一年中的大部分时间开放，并成为无支撑运输甚至开放水域船只的越来越可行的选择。本文提出了一种Benders-branch-and-cut方法，用于估计在北极海冰减少的未来气候情景下全球海上货物流动模式的变化。该模型考虑了北极航道上不断变化的事故风险，以及相应的冰级船舶和破冰船护航要求、更低的航速、更高的保险费、更高的事故概率，以及基于路径的最大风险暴露约束。由此产生的混合整数程序涉及基于路径的连续决策变量。该解决方案技术应用于全球海运集装箱网络模型，该网络包括与世界上最大的承运人联盟相关的80个港口、76条航线、426个链接和4303个分支。嵌入式加速技术和标签校正算法，采用非加性约束路径子问题的专门深测规则，实现了这种全球范围的解决方案。结果是对六种气候相关情景下预测的全球主要航线和港口的未来季节性全球海上贸易流量的估计。结果表明，所开发的模型可以为公司、国家和地区为不断变化的全球景观和气候做好准备提供支持。

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引用次数: 0

A data-driven optimization approach for the integrated train scheduling and maintenance planning in high-speed railways 高速铁路列车调度与维修综合规划的数据驱动优化方法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-02 DOI: 10.1016/j.cor.2025.107261

Hangyu Ji , Chuntian Zhang , Jiateng Yin , Lixing Yang

In railway systems, preventive maintenance plans are essential for ensuring the safety of train operations. However, these tasks are often subject to various disturbances (e.g., bad weather), leading to unpredictable deviations between planned and actual maintenance durations, which can further disrupt train schedules. Unlike most studies that assume constant maintenance durations, this paper introduces a data-driven, two-stage distributionally robust optimization (DRO) model for jointly optimizing train scheduling and maintenance planning. In the first stage, we determine the initial train schedule and maintenance plan. In the second stage, we allow for slight adjustments to train departure and arrival times at each station to accommodate disturbances affecting maintenance tasks. Our objective is to minimize both the expected travel time of trains and the deviation from the planned schedule under worst-case scenarios for maintenance disturbances. To capture the uncertainty of maintenance disturbances, we construct an ambiguity set using historical data and the Wasserstein metric. We show that the proposed two-stage DRO model, formulated over the Wasserstein ambiguity set, can be reformulated into an efficiently solvable equivalent form. Finally, we apply our model to a real-world case study of the Beijing–Guangzhou high-speed railway and compare it with traditional stochastic programming methods, including sample average approximation and robust optimization. The results highlight the efficiency of our approach and provide valuable insights for railway management.

在铁路系统中，预防性维修计划对于确保列车运行安全至关重要。然而，这些任务经常受到各种干扰（例如，恶劣的天气），导致计划和实际维护持续时间之间的不可预测的偏差，这可能进一步扰乱列车时刻表。与大多数假设维修时间不变的研究不同，本文引入了一种数据驱动的两阶段分布鲁棒优化（DRO）模型，用于列车调度和维修计划的联合优化。在第一阶段，我们确定初始列车时刻表和维护计划。在第二阶段，我们允许对每个车站的列车出发和到达时间进行轻微调整，以适应影响维修工作的干扰。我们的目标是在维护干扰的最坏情况下最小化列车的预期运行时间和与计划时间表的偏差。为了捕获维护干扰的不确定性，我们使用历史数据和Wasserstein度量构造了一个模糊集。我们证明了在Wasserstein模糊集上提出的两阶段DRO模型可以重新表述为有效可解的等效形式。最后，以京广高速铁路为例，将该模型与传统的随机规划方法进行了比较，包括样本平均逼近和鲁棒优化。结果突出了我们方法的效率，并为铁路管理提供了宝贵的见解。

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引用次数: 0

A branch-and-price algorithm for energy aware task scheduling of constellations of nanosatellites 纳米卫星星座能量感知任务调度的分支价格算法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-02 DOI: 10.1016/j.cor.2025.107259

Pedro Marcolin Antunes, Laio Oriel Seman, Eduardo Camponogara

This paper presents a branch-and-price algorithm for solving the Optimal Network Task Scheduling (ONTS) problem in satellite constellations. The algorithm efficiently manages both constellation tasks that can be performed by any satellite and satellite-specific tasks that must be executed by designated satellites, while considering critical energy constraints. We formulate the problem as a Mixed-Integer Linear Programming (MILP) model and develop a Dantzig–Wolfe decomposition that handles battery management constraints for the satellites at the master level, while addressing constellation-wide coordination requirements in the subproblems. A novel dynamic programming algorithm is proposed to solve the pricing subproblem for constellation tasks, augmented with dual stabilization techniques to improve convergence. Comprehensive computational experiments on realistic instances derived from nanosatellite operations demonstrate the effectiveness of the algorithm. Results show that our structured formulation significantly outperforms a naive approach, particularly for large instances, while effectively balancing workload distribution and energy management across the constellation. This work provides a practical framework for optimizing task scheduling in modern satellite constellations, with direct applications in Earth observation, telecommunications, and scientific missions.

提出了一种求解卫星星座最优网络任务调度（ONTS）问题的分支价格算法。该算法在考虑关键能量约束的情况下，有效地管理可由任意卫星执行的星座任务和必须由指定卫星执行的特定卫星任务。我们将该问题表述为混合整数线性规划（MILP）模型，并开发了dantzigg - wolfe分解，该分解在主级处理卫星的电池管理约束，同时在子问题中解决星座范围内的协调要求。针对星座任务的定价子问题，提出了一种新的动态规划算法，并结合双稳定技术提高了算法的收敛性。基于纳米卫星运行实例的综合计算实验证明了该算法的有效性。结果表明，我们的结构化公式明显优于一种朴素的方法，特别是对于大型实例，同时有效地平衡了整个星座的工作负载分配和能量管理。这项工作为优化现代卫星星座的任务调度提供了一个实用框架，可直接应用于地球观测、电信和科学任务。

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引用次数: 0

An efficient resource utilization and scheduling strategy for in-service aircraft maintenance and operations 在役飞机维修和运行的有效资源利用和调度策略

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-02 DOI: 10.1016/j.cor.2025.107262

Sandeep Singh Chauhan , Likhith Maadhav , Abhijit Dake , Gauthier Brillaud

Optimal scheduling of maintenance activities requires the solution of combinatorial optimization problems that need to be efficiently modeled and solved with optimization techniques. Maintenance scheduling and operations-associated problems in the aviation industry can efficiently enhance competitiveness. In the maintenance and scheduling problem, aircrafts need to undergo tasks for both line (A check) and base (C check) maintenance at various hangers at MRO (Maintenance, Repair and Operations) based on resource availability (both human and material). The determination of the optimal maintenance plan, in terms of allocating the resources to the aircraft, and resource movement from one aircraft to another based on availability and licensed skills in the presence of multiple tasks and capacity constraints so as to obtain maximum utilization of resources at maintenance site and minimize the turnaround time is a complex combinatorial optimization problem. To the best of our knowledge, this work is the first CP (Constraint Programming) based mathematical solution that jointly integrates zone, task precedence, technician-pool sharing, and multi-shift continuity for large-scale aircraft maintenance scheduling. In this article, we proposed an efficient optimization strategy that overcomes many of the drawbacks of the formulation/strategies available in literature and helps in determining efficient execution of maintenance work packages. The proposed strategy is generic, encompassing multi-aircraft, multi-skill and multi-shift scheduling capabilities and is validated on two real scenario business case studies, one each for line maintenance (A check) tasks and base maintenance (C check) tasks, as well as six large-scale synthetic scenarios with up to 20,000 tasks, demonstrating feasibility and scalable performance. The proposed strategy is demonstrated on MRO scheduling and it shows an improvement of up to 30.68% in the turn-around time by incorporating the proposed optimization strategy.

维修活动的最优调度需要解决组合优化问题，这些问题需要有效地建模并使用优化技术进行求解。航空工业的维修计划和运营相关问题可以有效地提高竞争力。在维修和调度问题中，飞机需要在MRO（维护、修理和操作）的各个机库中完成基于资源可用性（包括人力和材料）的线（A检查）和基地（C检查）维修任务。在多任务和能力约束的情况下，根据可用性和许可技能，确定最优维修计划，将资源分配给飞机，并将资源从一架飞机转移到另一架飞机，以最大限度地利用维修现场的资源，并使周转时间最短，是一个复杂的组合优化问题。据我们所知，这项工作是第一个基于CP（约束规划）的数学解决方案，它将区域、任务优先级、技术人员池共享和多班次连续性联合集成在大型飞机维修调度中。在本文中，我们提出了一种有效的优化策略，该策略克服了文献中可用的表述/策略的许多缺点，并有助于确定维护工作包的有效执行。所提出的策略是通用的，包括多飞机、多技能和多班次调度能力，并在两个真实场景业务案例研究中进行了验证，其中一个用于线路维护（A检查）任务和基地维护（C检查）任务，以及六个具有多达20,000个任务的大规模综合场景，展示了可行性和可扩展性能。在MRO调度中进行了验证，结果表明，采用该优化策略后，MRO的周转时间提高了30.68%。

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引用次数: 0

ROBIST: Robust optimization by iterative scenario sampling and statistical testing ROBIST：通过迭代场景采样和统计测试进行稳健优化

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-02 DOI: 10.1016/j.cor.2025.107260

Justin Starreveld , Guanyu Jin , Dick den Hertog , Roger J.A. Laeven

In this paper, we propose ROBIST, a simple, yet effective, data-driven algorithm for optimization under parametric uncertainty. The algorithm first generates solutions in an iterative manner by sampling and optimizing over a relatively small set of scenarios. Then, using statistical testing, the robustness of the solutions is evaluated, which can be done with a much larger set of scenarios. ROBIST offers a number of practical advantages over existing methods as it is: (i) easy to implement, (ii) able to deal with a wide range of problems and (iii) capable of providing sharp probability guarantees that are easily computable and independent of the dimensions of the problem. Numerical experiments demonstrate the effectiveness of ROBIST in comparison to alternative methods.

在本文中，我们提出了ROBIST，一个简单而有效的数据驱动算法，用于参数不确定性下的优化。该算法首先通过对相对较小的场景集进行采样和优化，以迭代的方式生成解决方案。然后，使用统计测试来评估解决方案的稳健性，这可以用更大的场景集来完成。与现有方法相比，ROBIST提供了许多实际优势，因为它：(i)易于实现，（ii）能够处理广泛的问题，（iii）能够提供易于计算且独立于问题维度的明确概率保证。数值实验证明了ROBIST算法的有效性。

引用次数: 0

A deep reinforcement learning approach for dynamic job-shop scheduling problem considering time variable and new job arrivals 考虑时间变量和新作业到达的车间动态调度问题的深度强化学习方法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-01 DOI: 10.1016/j.cor.2025.107263

Haoyang Yu , Wenbin Gu , Na Tang , Zhenyang Guo

In recent years, the complexity of the production process due to increased demand for customization has greatly increased the difficulty of dynamic job-shop scheduling problem (DJSP). This paper proposes a deep reinforcement learning (DRL) approach to tackle the DJSP based on proximal policy optimization (PPO) algorithm. A novel state representation method that expresses state features as multi-channel images is proposed to simplify the state characterization process. Various heuristic-based priority dispatching rules (PDRs)are used to construct action space. By converting scheduling instances into images and leveraging the spatial pyramid pooling fast (SPPF) module for feature extraction, this model can handle scheduling instances of varying scales and map size-independent processing information matrix to fixed action space. Additionally, a dense reward based on a predefined scheduling region is developed to offer detailed guidance to the agent, enabling more precise and comprehensive policy assessment. Static tests are conducted on well-known benchmarks, and the experimental results indicate that our scheduling model surpasses the performance of the three latest DRL approaches on average. Compared with PDR methods, dynamic experiments demonstrate that the proposed DRL model excels in adaptability and robustness when new tasks arrive and the processing time fluctuates with uncertainty.

近年来，由于定制需求的增加，生产过程的复杂性大大增加了动态作业车间调度问题（DJSP）的难度。本文提出了一种基于近端策略优化（PPO）算法的深度强化学习（DRL）方法来解决DJSP问题。为了简化状态表征过程，提出了一种将状态特征表示为多通道图像的状态表征方法。各种基于启发式的优先级调度规则（pdr）被用来构建动作空间。该模型通过将调度实例转换为图像，利用空间金字塔池快速（SPPF）模块进行特征提取，可以处理不同尺度的调度实例，并将大小无关的处理信息矩阵映射到固定的动作空间。此外，开发了基于预定义调度区域的密集奖励，为智能体提供详细的指导，使策略评估更加精确和全面。在知名的基准测试上进行了静态测试，实验结果表明，我们的调度模型的性能平均优于三种最新的DRL方法。动态实验表明，与PDR方法相比，本文提出的DRL模型在新任务到来和处理时间随不确定性波动时具有较好的适应性和鲁棒性。

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引用次数: 0

A hybrid memetic metaheuristic for medical staff assignment in major public health emergencies 重大突发公共卫生事件中医务人员分配的混合模因元启发式

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-08-30 DOI: 10.1016/j.cor.2025.107256

Yang Wang , He Zheng , Zequn Wei , Christophe Wilbaut , Saïd Hanafi

During major public health emergencies, effective assignment of medical staff is crucial for saving lives and controlling the spread of epidemics. This work focuses on the assignment of doctors and nurses to hospitals to form treatment groups that carry out patient treatment tasks. We consider the practical constraints of skill types of medical staff and the severity of patients’ conditions and propose a mixed integer programming model with the objective of maximizing demand satisfaction and personnel skill matching. To solve this problem, we introduce a hybrid memetic search algorithm that combines a specialized crossover operator for generating promising offspring solutions and a variable neighborhood search procedure to improve their quality. Computational results demonstrate that our algorithm outperforms the general mixed integer programming solver GUROBI. The key components of the proposed algorithm are experimentally analyzed and managerial insights are derived.

在重大突发公共卫生事件中，有效分配医务人员对于挽救生命和控制流行病传播至关重要。这项工作的重点是将医生和护士分配到医院，组成治疗小组，执行患者治疗任务。考虑到医务人员技能类型的实际限制和患者病情的严重程度，提出了以需求满意度和人员技能匹配最大化为目标的混合整数规划模型。为了解决这一问题，我们引入了一种混合模因搜索算法，该算法结合了一个专门的交叉算子来产生有希望的后代解和一个可变邻域搜索过程来提高后代解的质量。计算结果表明，该算法优于一般的混合整数规划求解器GUROBI。实验分析了该算法的关键组成部分，并得出了管理见解。

引用次数: 0

A selection hyper-heuristic approach to the single airport slot allocation problem 单机场机位分配问题的选择超启发式方法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-08-30 DOI: 10.1016/j.cor.2025.107223

Sha Wang , John H. Drake , David Melder , John R. Woodward , Edmund K. Burke

There is a significant imbalance between air traffic demand and airport capacity at many congested airports. As a result, airlines are required to request time slots for aircraft take-off or landing. The effective allocation of slots forms the basis of the single airport slot allocation problem. A significant amount of existing research on this problem focuses on mathematical modelling and exact solution methods for relatively small-scale problem instances. As the computational effort required for such approaches can be prohibitive as the size of a problem increases, heuristic search methods are often deployed as a more practical alternative. In this paper, we propose a new hyper-heuristic solution approach based on adaptive large neighbourhood search. The proposed approach consists of two stages, a constructive stage, which quickly generates initial feasible solutions, and an improvement stage, which attempts to improve an incumbent solution through an iterative process of destroy and repair. In the improvement stage, five destroy operators are used to modify a solution, including a novel operator which identifies a set of related slot requests to be rescheduled. The destroy operators are selected by an adaptive selection hyper-heuristic according to their past performance. The results using data from a real-world airport show that the proposed approach can provide near-optimal solutions within two hours, whereas a commercial solver can only find feasible solutions of much lower quality within the same time frame.

在许多拥挤的机场，空中交通需求和机场容量之间存在严重的不平衡。因此，航空公司需要申请飞机起飞或降落的时间段。机位的有效分配是解决单机场机位分配问题的基础。目前对该问题的大量研究主要集中在相对小规模问题实例的数学建模和精确求解方法上。由于随着问题规模的增加，此类方法所需的计算量可能会令人望而却步，因此启发式搜索方法通常被部署为更实用的替代方法。本文提出了一种基于自适应大邻域搜索的超启发式求解方法。建议的方法包括两个阶段，一个是建设性阶段，它迅速产生初步可行的解决方案；一个是改进阶段，它试图通过破坏和修复的迭代过程来改进现有的解决方案。在改进阶段，使用五个销毁算子来修改解决方案，其中包括一个新算子，该算子识别一组需要重新调度的相关插槽请求。销毁操作符采用自适应选择超启发式算法，根据销毁操作符过去的表现选择销毁操作符。使用真实机场数据的结果表明，所提出的方法可以在两小时内提供接近最优的解决方案，而商业求解器只能在相同的时间框架内找到质量低得多的可行解决方案。

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引用次数: 0

Centrality measures and opinion dynamics in two-layer networks with replica nodes 带有复制节点的双层网络中的中心性度量和意见动态

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-08-29 DOI: 10.1016/j.cor.2025.107245

Chi Zhao , Elena Parilina

We propose two fast and accurate algorithms to approximate game-theoretic centrality measures and examine connection between centrality measures, network properties, and key performance indicators (consensus time and winning rate) of opinion dynamic processes on such networks. As an example, we consider a Zachary’s karate club as a social network and extend it by adding the second (internal) layer of communication. The internal layer represents the network where individuals can share their real opinions with the close friends. The structures of the external and internal layers may be different. The significant positive correlation between internal graph density and consensus time, and significant negative correlation between centrality of authoritative nodes and consensus time are found. The proposed algorithms are verified by a series of experiments from two aspects: the accuracy and the efficiency. The algorithms are novel and can be considered as a contribution to the network theory independently of opinion dynamics as they can be used to calculate node centrality in any weighted graph.

我们提出了两种快速准确的算法来近似博弈论中的中心性度量，并研究了中心性度量、网络属性和意见动态过程的关键绩效指标（共识时间和胜率）之间的联系。例如，我们将Zachary 's空手道俱乐部视为一个社交网络，并通过添加第二层（内部）通信层对其进行扩展。内层代表了个人可以与亲密朋友分享真实意见的网络。外层和内层的结构可能不同。发现内部图密度与共识时间呈显著正相关，权威节点中心性与共识时间呈显著负相关。通过一系列实验，从精度和效率两个方面验证了所提算法的有效性。这些算法是新颖的，可以被认为是独立于意见动态的网络理论的贡献，因为它们可以用来计算任何加权图中的节点中心性。

引用次数: 0

Optimal planning of power distribution networks with fault-tolerant configuration 带容错配置的配电网优化规划

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-08-29 DOI: 10.1016/j.cor.2025.107248

Renato Bruni , Alberto Geri , Marco Maccioni , Ludovico Nati

Power Distribution networks are essential infrastructures that should be designed by satisfying two conflicting requests: cost minimization and reliability. While traditional network planning aimed at radial configurations, which are more similar to the typical working configuration of a network but are not fault-tolerant, modern techniques seek for meshed configurations, since these architectures are more fault-tolerant. Due to the complexity of the problem and the large size of nowadays instances, most of the techniques used for planning are based on heuristic approaches. Thus, they are usually unable to guarantee optimality and not even able to provide an assessment of the distance from the optimal solution. In this work, we address the challenge of planning a fault tolerant network through an exact approach, by introducing innovative Mixed-Integer Linear Programming models designed for the planning of meshed distribution networks with loop-feeder or open-loop topology. Differently from other techniques, our approach simplifies the formulation by avoiding the need for fault scenarios, significantly reducing the computational burden of the optimization problem. The outcomes of our approach are the generation of optimal meshed network, which effectively balance cost and reliability of the electric distribution system. Comprehensive studies on realistic test instances show the advantages of the proposed formulations.

配电网是重要的基础设施，其设计应满足两个相互冲突的要求：成本最小化和可靠性。传统的网络规划以径向配置为目标，径向配置更类似于网络的典型工作配置，但不具有容错性，而现代技术寻求网状配置，因为这些架构更具容错性。由于问题的复杂性和当今实例的庞大规模，大多数用于规划的技术都是基于启发式方法。因此，它们通常不能保证最优性，甚至不能提供到最优解的距离评估。在这项工作中，我们通过引入创新的混合整数线性规划模型，设计用于规划具有环路馈线或开环拓扑的网状配电网络，通过精确的方法解决了规划容错网络的挑战。与其他技术不同的是，我们的方法通过避免对故障场景的需要来简化公式，大大减少了优化问题的计算负担。该方法的结果是生成最优的电网，有效地平衡了配电系统的成本和可靠性。对实际试验实例的综合研究表明了所提公式的优越性。

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引用次数: 0