Computers & Operations Research最新文献_第10页

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 : 2026-01-01 Epub 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。

{"title":"A branch-and-price algorithm for energy aware task scheduling of constellations of nanosatellites","authors":"Pedro Marcolin Antunes, Laio Oriel Seman, Eduardo Camponogara","doi":"10.1016/j.cor.2025.107259","DOIUrl":"10.1016/j.cor.2025.107259","url":null,"abstract":"<div><div>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 <em>constellation tasks</em> that can be performed by any satellite and <em>satellite-specific tasks</em> 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.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107259"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096134","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

Q-learning-based hyper-heuristic algorithm for open dimension irregular packing problems 基于q学习的开维不规则包装问题超启发式算法

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-09-10 DOI: 10.1016/j.cor.2025.107279

Yongchun Wang , Qingjin Peng , Zhen Wang , Shuiquan Huang , Zhengkai Xu , Chuanzhen Huang , Baosu Guo

Heuristic methods provide a computationally efficient framework for addressing two-dimensional irregular packing problems, particularly in resource-constrained industrial settings. As a typical combinatorial optimization problem, irregular packing exhibits exponential growth in computational complexity with increasing workpiece counts, while the solution space dynamically reconfigures due to geometric variability among workpieces. Although heuristic algorithms can generate feasible layouts within acceptable timeframes, their reliance on fixed search rule limits adaptability to diverse scenarios, necessitating more flexible approaches. In this paper, a hyper-heuristic algorithm based on Q-Learning is proposed to solve open dimension packing problems, including one-open and two-open dimension problems. Q-Learning is adopted as the high-level strategy for its ability to optimize low-level heuristic selection through reward-driven experience accumulation. The method incorporates a mixed encoding method for solution representation, four specialized low-level heuristic operators, a linear population decline mechanism, and an elite preservation strategy to balance exploration–exploitation. The Q-Learning controller dynamically selects operators by updating the Q-table based on Bellman’s equation. The proposed algorithm is compared to some advanced algorithms in general datasets. The results show that our method has better performance and applicability.

启发式方法为解决二维不规则包装问题提供了一个计算效率高的框架，特别是在资源受限的工业环境中。不规则填充是一个典型的组合优化问题，其计算复杂度随着工件数量的增加呈指数增长，且求解空间由于工件之间的几何变化而动态重构。虽然启发式算法可以在可接受的时间范围内生成可行的布局，但它们对固定搜索规则的依赖限制了对不同场景的适应性，需要更灵活的方法。本文提出了一种基于q -学习的超启发式算法来解决开放维包装问题，包括一维和二维问题。Q-Learning能够通过奖励驱动的经验积累来优化低级启发式选择，因此被用作高级策略。该方法采用混合编码方法表示解，四个专门的低级启发式算子，线性种群下降机制和精英保存策略来平衡探索-开发。Q-Learning控制器根据Bellman方程，通过更新q表来动态选择算子。在一般数据集上与一些先进的算法进行了比较。结果表明，该方法具有较好的性能和适用性。

{"title":"Q-learning-based hyper-heuristic algorithm for open dimension irregular packing problems","authors":"Yongchun Wang , Qingjin Peng , Zhen Wang , Shuiquan Huang , Zhengkai Xu , Chuanzhen Huang , Baosu Guo","doi":"10.1016/j.cor.2025.107279","DOIUrl":"10.1016/j.cor.2025.107279","url":null,"abstract":"<div><div>Heuristic methods provide a computationally efficient framework for addressing two-dimensional irregular packing problems, particularly in resource-constrained industrial settings. As a typical combinatorial optimization problem, irregular packing exhibits exponential growth in computational complexity with increasing workpiece counts, while the solution space dynamically reconfigures due to geometric variability among workpieces. Although heuristic algorithms can generate feasible layouts within acceptable timeframes, their reliance on fixed search rule limits adaptability to diverse scenarios, necessitating more flexible approaches. In this paper, a hyper-heuristic algorithm based on Q-Learning is proposed to solve open dimension packing problems, including one-open and two-open dimension problems. Q-Learning is adopted as the high-level strategy for its ability to optimize low-level heuristic selection through reward-driven experience accumulation. The method incorporates a mixed encoding method for solution representation, four specialized low-level heuristic operators, a linear population decline mechanism, and an elite preservation strategy to balance exploration–exploitation. The Q-Learning controller dynamically selects operators by updating the Q-table based on Bellman’s equation. The proposed algorithm is compared to some advanced algorithms in general datasets. The results show that our method has better performance and applicability.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107279"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096233","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

An adaptive and efficient path planning algorithm for UAV navigation in complex environments 复杂环境下无人机导航的自适应高效路径规划算法

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.cor.2025.107296

Yonggang Wang , Guoliang Wang

Efficient and reliable path planning is critical for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, including urban obstacle fields and sensor-restricted airspaces. This paper proposes the rapidly converging and greedy-optimized RRT* (RGO-RRT*) algorithm, which integrates four adaptive modules to enhance planning performance. These include: (1) region-based probabilistic sampling that prioritizes high-potential regions to reduce redundant exploration and accelerate convergence; (2) adaptive step-size adjustment based on obstacle density for fine maneuverability in cluttered areas and rapid expansion in open spaces; (3) dynamic goal biasing that gradually increases goal attraction to balance exploration and convergence; and (4) an improved artificial potential field with an adaptive repulsion model to mitigate local minima and ensure smoother trajectories. Additionally, three auxiliary strategies — bidirectional tree expansion, greedy optimization, and feasibility constraints-are employed to further refine path quality and search efficiency. Extensive simulations are conducted in four structurally diverse environments to evaluate performance under various levels of obstacle density and geometric complexity. Results show that RGO-RRT* consistently outperforms five benchmark algorithms (RRT*, Bi-RRT*, APF-RRT*, Bi-APF-RRT*, and Improved Bi-APF-RRT*), achieving up to 83.1% fewer iterations, 11.7% shorter path lengths, and 87.9% reduction in planning time. These findings demonstrate the method’s robustness, efficiency, and applicability to UAV navigation in cluttered scenarios.

高效、可靠的路径规划对于自主无人机（UAV）在复杂环境下的导航至关重要，包括城市障碍物场和传感器受限空域。本文提出了快速收敛贪婪优化RRT* (RGO-RRT*)算法，该算法集成了四个自适应模块，提高了规划性能。这包括：(1)基于区域的概率抽样，优先考虑高潜力区域，以减少冗余勘探并加速收敛；(2)基于障碍物密度的自适应步长调整，在杂乱区域具有良好的机动能力，在开放空间具有快速扩张能力；(3)动态目标偏置，逐渐增加目标吸引力，平衡探索与收敛；(4)基于自适应斥力模型的改进人工势场，以减小局部极小值，保证轨迹平滑。此外，还采用了双向树展开、贪婪优化和可行性约束三种辅助策略来进一步优化路径质量和搜索效率。在四种不同结构的环境中进行了大量的模拟，以评估不同水平障碍物密度和几何复杂性下的性能。结果表明，RGO-RRT*持续优于5种基准算法（RRT*、Bi-RRT*、APF-RRT*、Bi-APF-RRT*和Improved Bi-APF-RRT*），迭代次数减少83.1%，路径长度缩短11.7%，规划时间减少87.9%。这些结果证明了该方法的鲁棒性、有效性和对无人机在混乱场景下导航的适用性。

{"title":"An adaptive and efficient path planning algorithm for UAV navigation in complex environments","authors":"Yonggang Wang , Guoliang Wang","doi":"10.1016/j.cor.2025.107296","DOIUrl":"10.1016/j.cor.2025.107296","url":null,"abstract":"<div><div>Efficient and reliable path planning is critical for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, including urban obstacle fields and sensor-restricted airspaces. This paper proposes the rapidly converging and greedy-optimized RRT* (RGO-RRT*) algorithm, which integrates four adaptive modules to enhance planning performance. These include: (1) region-based probabilistic sampling that prioritizes high-potential regions to reduce redundant exploration and accelerate convergence; (2) adaptive step-size adjustment based on obstacle density for fine maneuverability in cluttered areas and rapid expansion in open spaces; (3) dynamic goal biasing that gradually increases goal attraction to balance exploration and convergence; and (4) an improved artificial potential field with an adaptive repulsion model to mitigate local minima and ensure smoother trajectories. Additionally, three auxiliary strategies — bidirectional tree expansion, greedy optimization, and feasibility constraints-are employed to further refine path quality and search efficiency. Extensive simulations are conducted in four structurally diverse environments to evaluate performance under various levels of obstacle density and geometric complexity. Results show that RGO-RRT* consistently outperforms five benchmark algorithms (RRT*, Bi-RRT*, APF-RRT*, Bi-APF-RRT*, and Improved Bi-APF-RRT*), achieving up to 83.1% fewer iterations, 11.7% shorter path lengths, and 87.9% reduction in planning time. These findings demonstrate the method’s robustness, efficiency, and applicability to UAV navigation in cluttered scenarios.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107296"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262362","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

Solving the strip packing problem with a decomposition framework and a generic solver: Implementation, tuning, and reinforcement-learning-based hybridization 用分解框架和通用求解器解决条形包装问题：实现、调整和基于强化学习的杂交

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-09-27 DOI: 10.1016/j.cor.2025.107276

Fatih Burak Akçay, Maxence Delorme

In the strip packing problem, the objective is to pack a set of two-dimensional items into a strip of fixed width such that the total height of the packing is minimized. The current state-of-the-art exact approach for the problem uses a decomposition framework in which the main problem (MP) fixes the item abscissas and the strip height, whereas the subproblem (SP) determines whether a set of item ordinates resulting in a feasible packing exists. Even though this decomposition framework has already been used several times in the literature, implementation details were often obfuscated, limiting the outreach of the approach. We address this issue by thoroughly describing and testing various builds for this framework, investigating important features such as the way to forbid an infeasible solution in the MP (e.g., by rejecting them or through a no-good cut) and the techniques used to solve the MP and the SP. One of our findings is that a minor implementation tweak such as changing the random seed between two MP iterations can bring the same level of improvement as a more involved feature such as strengthening the no-good cuts. From our extensive experiments, we identify two versions of the framework that produce complementary results: one where the main problem is solved with integer linear programming and the other where it is solved with constraint programming. We then train a reinforcement learning agent to find the best hybridization of these two algorithms and show that the resulting approach obtains state-of-the-art results on benchmark instances.

在条形包装问题中，目标是将一组二维物品包装成固定宽度的条形，使包装的总高度最小。目前最先进的精确方法是使用一个分解框架，其中主问题（MP）确定项目的横坐标和条形高度，而子问题（SP）确定是否存在一组导致可行包装的项目坐标。尽管这个分解框架已经在文献中被使用了几次，但是实现细节经常被混淆，限制了方法的扩展。为了解决这个问题，我们彻底地描述和测试了这个框架的各种构建，研究了一些重要的特性，比如如何在MP中禁止一个不可行的解决方案(例如：我们的发现之一是，一个小的执行调整（如改变两次MP迭代之间的随机种子）可以带来与更复杂的功能（如加强无益处切割）相同水平的改进。从我们广泛的实验中，我们确定了产生互补结果的框架的两个版本：其中一个主要问题是用整数线性规划解决的，另一个是用约束规划解决的。然后，我们训练一个强化学习代理来找到这两种算法的最佳杂交，并表明所得到的方法在基准实例上获得了最先进的结果。

{"title":"Solving the strip packing problem with a decomposition framework and a generic solver: Implementation, tuning, and reinforcement-learning-based hybridization","authors":"Fatih Burak Akçay, Maxence Delorme","doi":"10.1016/j.cor.2025.107276","DOIUrl":"10.1016/j.cor.2025.107276","url":null,"abstract":"<div><div>In the strip packing problem, the objective is to pack a set of two-dimensional items into a strip of fixed width such that the total height of the packing is minimized. The current state-of-the-art exact approach for the problem uses a decomposition framework in which the main problem (MP) fixes the item abscissas and the strip height, whereas the subproblem (SP) determines whether a set of item ordinates resulting in a feasible packing exists. Even though this decomposition framework has already been used several times in the literature, implementation details were often obfuscated, limiting the outreach of the approach. We address this issue by thoroughly describing and testing various builds for this framework, investigating important features such as the way to forbid an infeasible solution in the MP (e.g., by rejecting them or through a no-good cut) and the techniques used to solve the MP and the SP. One of our findings is that a minor implementation tweak such as changing the random seed between two MP iterations can bring the same level of improvement as a more involved feature such as strengthening the no-good cuts. From our extensive experiments, we identify two versions of the framework that produce complementary results: one where the main problem is solved with integer linear programming and the other where it is solved with constraint programming. We then train a reinforcement learning agent to find the best hybridization of these two algorithms and show that the resulting approach obtains state-of-the-art results on benchmark instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107276"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262361","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

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 : 2026-01-01 Epub 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。

{"title":"A benders-branch-and-cut methodology for global cargo vessel traffic prediction given declining arctic sea ice and changing risks","authors":"Wenjie Li, Elise Miller-Hooks","doi":"10.1016/j.cor.2025.107265","DOIUrl":"10.1016/j.cor.2025.107265","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107265"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020001","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

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 : 2026-01-01 Epub 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。

{"title":"A data-driven optimization approach for the integrated train scheduling and maintenance planning in high-speed railways","authors":"Hangyu Ji , Chuntian Zhang , Jiateng Yin , Lixing Yang","doi":"10.1016/j.cor.2025.107261","DOIUrl":"10.1016/j.cor.2025.107261","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107261"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046601","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

Modeling and algorithm for job shop scheduling with batch operations in semiconductor fabs 半导体晶圆厂批量作业车间调度建模与算法

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-09-24 DOI: 10.1016/j.cor.2025.107287

Wen Ma , Gedong Jiang , Nuogang Sun , Chaoqing Min , Xuesong Mei

Semiconductor manufacturing presents a highly complex Job Shop Scheduling Problem (JSP) due to the diversity and large number of processing machines, as well as the intricate manufacturing processes including batch and non-batch operations. Existing studies often either overlook batching problems or address them in oversimplified ways, failing to provide effective solutions for large-scale scheduling challenges with batch operations. For this problem, a model for the JSP involving both batching and non-batching processes in semiconductor fabs is first developed. Then, the First Come First Served (FCFS) approach, as an effective rule-based method, is employed to generate high-quality initial solutions. A tailored Constrained Genetic Algorithm (CGA) by embedding constraints to the stages of genetic algorithms is proposed to further optimize the solution. The CGA incorporates batch grouping, constrained encoding, constrained crossover and constrained mutation to effectively handle the sequential constraints of batch and non-batch processes, ensuring the generation of valid solutions. The CGA is validated using the SMT2020 and SMAT2022 datasets across various scales and scenarios. Experimental results demonstrate that the CGA outperforms FCFS, backward simulation and reinforcement learning. These results highlight the CGA’s effectiveness and robustness in solving complex scheduling problems in semiconductor manufacturing.

半导体制造业由于加工机器的多样性和数量多，以及包括批量和非批量操作在内的复杂制造过程，提出了一个高度复杂的作业车间调度问题（JSP）。现有的研究往往忽视了批处理问题，或者以过于简化的方式处理批处理问题，无法为大规模的批处理调度挑战提供有效的解决方案。针对这一问题，首先建立了涉及半导体晶圆厂中批处理和非批处理的JSP模型。然后，采用先到先得（FCFS）方法作为一种有效的基于规则的方法，生成高质量的初始解。提出了一种定制约束遗传算法（CGA），将约束嵌入到遗传算法的各个阶段，进一步优化求解。该算法结合批量分组、约束编码、约束交叉和约束突变等方法，有效地处理了批量和非批量过程的序列约束，保证了有效解的生成。CGA使用SMT2020和SMAT2022数据集跨各种尺度和场景进行验证。实验结果表明，CGA优于FCFS、后向仿真和强化学习。这些结果突出了CGA在解决半导体制造中复杂调度问题方面的有效性和鲁棒性。

{"title":"Modeling and algorithm for job shop scheduling with batch operations in semiconductor fabs","authors":"Wen Ma , Gedong Jiang , Nuogang Sun , Chaoqing Min , Xuesong Mei","doi":"10.1016/j.cor.2025.107287","DOIUrl":"10.1016/j.cor.2025.107287","url":null,"abstract":"<div><div>Semiconductor manufacturing presents a highly complex Job Shop Scheduling Problem (JSP) due to the diversity and large number of processing machines, as well as the intricate manufacturing processes including batch and non-batch operations. Existing studies often either overlook batching problems or address them in oversimplified ways, failing to provide effective solutions for large-scale scheduling challenges with batch operations. For this problem, a model for the JSP involving both batching and non-batching processes in semiconductor fabs is first developed. Then, the First Come First Served (FCFS) approach, as an effective rule-based method, is employed to generate high-quality initial solutions. A tailored Constrained Genetic Algorithm (CGA) by embedding constraints to the stages of genetic algorithms is proposed to further optimize the solution. The CGA incorporates batch grouping, constrained encoding, constrained crossover and constrained mutation to effectively handle the sequential constraints of batch and non-batch processes, ensuring the generation of valid solutions. The CGA is validated using the SMT2020 and SMAT2022 datasets across various scales and scenarios. Experimental results demonstrate that the CGA outperforms FCFS, backward simulation and reinforcement learning. These results highlight the CGA’s effectiveness and robustness in solving complex scheduling problems in semiconductor manufacturing.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107287"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217130","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

Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements 合作自主农机具船队公平高效的多智能体路径选择

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-08-26 DOI: 10.1016/j.cor.2025.107252

Aitor López-Sánchez , Marin Lujak , Frédéric Semet , Holger Billhardt

The growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computing time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair.

越来越多地使用带有可拆卸工具的自动拖拉机车队，给农业带来了复杂的物流挑战。当前的系统通常依赖于简单的启发式，避免实现交换，限制了效率。一个核心挑战是动态协调车辆路线和实现交换，以实现高效、低干预的任务执行。由于成本高，这类车队主要由大型企业或合作社拥有，公平的任务分配和利润分享至关重要。为了兼顾协调与公平，本文引入了农用机具车辆路径问题（AFVRPI）。我们提出了一个分布式模型，该模型来源于本文中提出的集中式公式。该模型嵌入到分布式多代理系统架构（DIMASA）中，其中自动驾驶车辆代理管理路线并在有限的燃料自主权下实现使用，而执行代理确保兼容性和足够的容量以满足任务需求。我们的解决方案采用系统平均主义的社会福利优化，迭代最大化最贫困车辆的利润，平衡公平性和系统效率。为了增强可伸缩性，我们在分布式模型中使用列生成，实现了与集中式模型相当的解决方案质量，同时显著减少了计算时间。在新的基准实例上的仿真结果表明，我们的分布式多智能体AFVRPI方法具有可扩展性、有效性和公平性。

{"title":"Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements","authors":"Aitor López-Sánchez , Marin Lujak , Frédéric Semet , Holger Billhardt","doi":"10.1016/j.cor.2025.107252","DOIUrl":"10.1016/j.cor.2025.107252","url":null,"abstract":"<div><div>The growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computing time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107252"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925994","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

Great Deluge-based metaheuristic incorporating integer nonlinear programming for modeling and solving dynamic capability-based machine layout problem 基于大洪水的整合整数非线性规划的元启发式建模与求解基于动态能力的机器布局问题

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-10-10 DOI: 10.1016/j.cor.2025.107302

Adil Baykasoğlu , Kemal Subulan , Alper Hamzadayı

This paper introduces a novel Dynamic Capability-Based Machine Layout (DCB-ML) problem by integrating the Quadratic Assignment Problem (QAP) formulation with a Dynamic Capability-Based Part Flow Assignment (DCB-PFA) problem. This integration enables the simultaneous consideration of machines’ processing capabilities, routing flexibility, dynamic flow assignment, and machine capacity utilization. First, a new Integer Nonlinear Programming (INLP) model is developed. The dynamic part flows are determined via the DCB-PFA sub-problem, while machine–location assignments are obtained by solving QAP. To address the complex nature of this problem, a hybrid solution approach is proposed that combines a Great Deluge Algorithm (GDA) with a Mixed-Integer Linear Programming (MILP) model, complemented by local search procedures. Since the problem has a decomposable structure, the proposed approach allows each sub-problem to be addressed independently, while the overall solution quality is jointly evaluated. Decomposition reduces the size of the resulting MILP model, as several binary variables and assignment constraints are eliminated. The proposed hybrid approach is also compared with the INLP and its linearized equivalent on several test problems. For large-scale problems with medium to high capability overlaps, nonlinear and MIP solvers fail to obtain feasible solutions, whereas the proposed approach can efficiently generate high-quality solutions within reasonable times. Moreover, when the effects of different machine-capability overlaps are investigated, it is observed that the solution of the problem will be more complex in the case of higher machine-capability overlaps. However, considering machine capabilities improves overall layout scores and eliminates the necessity of frequent reconfigurations, which is costly and time-consuming.

将二次分配问题（QAP）与基于动态能力的零件流分配问题（DCB-PFA）相结合，提出了一种新的基于动态能力的机床布局问题（DCB-ML）。这种集成可以同时考虑机器的处理能力、路由灵活性、动态流分配和机器容量利用率。首先，提出了一种新的整数非线性规划模型。通过DCB-PFA子问题确定动态零件流，通过求解QAP获得机器位置分配。为了解决该问题的复杂性，提出了一种混合解决方法，该方法将大洪水算法（GDA）与混合整数线性规划（MILP）模型相结合，并辅以局部搜索过程。由于问题具有可分解的结构，因此所提出的方法允许独立处理每个子问题，同时联合评估整体解决方案的质量。分解减少了结果MILP模型的大小，因为消除了几个二进制变量和分配约束。在若干测试问题上，将所提出的混合方法与INLP及其线性化等效方法进行了比较。对于具有中高能力重叠的大规模问题，非线性和MIP求解方法无法获得可行解，而该方法可以在合理的时间内有效地生成高质量的解。此外，当研究不同机器-能力重叠的影响时，发现在机器-能力重叠较高的情况下，问题的求解将更加复杂。然而，考虑机器的功能可以提高总体布局得分，并消除频繁重新配置的必要性，这是昂贵和耗时的。

{"title":"Great Deluge-based metaheuristic incorporating integer nonlinear programming for modeling and solving dynamic capability-based machine layout problem","authors":"Adil Baykasoğlu , Kemal Subulan , Alper Hamzadayı","doi":"10.1016/j.cor.2025.107302","DOIUrl":"10.1016/j.cor.2025.107302","url":null,"abstract":"<div><div>This paper introduces a novel Dynamic Capability-Based Machine Layout (DCB-ML) problem by integrating the Quadratic Assignment Problem (QAP) formulation with a Dynamic Capability-Based Part Flow Assignment (DCB-PFA) problem. This integration enables the simultaneous consideration of machines’ processing capabilities, routing flexibility, dynamic flow assignment, and machine capacity utilization. First, a new Integer Nonlinear Programming (INLP) model is developed. The dynamic part flows are determined via the DCB-PFA sub-problem, while machine–location assignments are obtained by solving QAP. To address the complex nature of this problem, a hybrid solution approach is proposed that combines a Great Deluge Algorithm (GDA) with a Mixed-Integer Linear Programming (MILP) model, complemented by local search procedures. Since the problem has a decomposable structure, the proposed approach allows each sub-problem to be addressed independently, while the overall solution quality is jointly evaluated. Decomposition reduces the size of the resulting MILP model, as several binary variables and assignment constraints are eliminated. The proposed hybrid approach is also compared with the INLP and its linearized equivalent on several test problems. For large-scale problems with medium to high capability overlaps, nonlinear and MIP solvers fail to obtain feasible solutions, whereas the proposed approach can efficiently generate high-quality solutions within reasonable times. Moreover, when the effects of different machine-capability overlaps are investigated, it is observed that the solution of the problem will be more complex in the case of higher machine-capability overlaps. However, considering machine capabilities improves overall layout scores and eliminates the necessity of frequent reconfigurations, which is costly and time-consuming.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107302"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324593","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

Learning-guided iterated local search for the minmax multiple traveling salesman problem 最小最大多旅行商问题的学习引导迭代局部搜索

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

Computers & Operations Research

Pub Date : 2026-01-01 Epub Date: 2025-09-07 DOI: 10.1016/j.cor.2025.107255

Pengfei He , Jin-Kao Hao , Jinhui Xia

The minmax multiple traveling salesman problem involves minimizing the costs of a longest tour among a set of tours. The problem is of great practical interest because it can be used to formulate several real-life applications. To solve this computationally challenging problem, we propose a learning-driven iterated local search approach that combines an effective local search procedure to find high-quality local optimal solutions and a multi-armed bandit algorithm to select removal and insertion operators to escape local optimal traps. Extensive experiments on 77 commonly used benchmark instances show that the algorithm achieves excellent results in terms of solution quality and running time. In particular, it achieves 32 new best results (improved upper bounds) and matches the best-known results for 35 other instances. Additional experiments shed light on the understanding of the algorithm’s constituent elements. Multi-armed bandit selection can be used advantageously in other multi-operator local search algorithms.

最小最大多重旅行商问题涉及在一组旅行中最小化最长旅行的成本。这个问题具有很大的实际意义，因为它可以用来制定几个实际应用。为了解决这一具有计算挑战性的问题，我们提出了一种学习驱动的迭代局部搜索方法，该方法结合了有效的局部搜索过程来寻找高质量的局部最优解和多臂强盗算法来选择移除和插入算子以逃避局部最优陷阱。在77个常用的基准实例上进行的大量实验表明，该算法在求解质量和运行时间方面都取得了很好的效果。特别是，它实现了32个新的最佳结果（改进的上界），并与35个其他实例的最知名结果相匹配。更多的实验揭示了对算法组成要素的理解。多臂强盗选择在其他多算子局部搜索算法中具有优势。

引用次数: 0