Computers & Operations Research最新文献

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A Q-learning-based variable greedy algorithm to minimize the sum of total earliness and tardiness in a no-wait flowshop scheduling problem 无等待流水车间调度问题中基于q学习的变量贪心算法

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

Computers & Operations Research

Pub Date : 2026-01-06 DOI: 10.1016/j.cor.2025.107382

Levi R. Abreu , Bruno A. Prata , Jose M. Framinan

This paper addresses a scheduling problem in a flowshop where the jobs cannot have idle times once they start their processing time in the first machine (i.e. no-wait constraint). The objective is to minimize both the sum of the earliness and tardiness of the jobs in order to optimize the inventory and backlog costs usually associated with the early and late completion of the jobs. As this decision problem is known to be NP-hard, previous contributions have focused on the proposal of heuristic procedures to yield good –albeit not optimal– solutions in reasonable computation times. Despite these advances, we believe that there is room for developing more efficient solution procedures, particularly by combining Machine Learning (ML) techniques with advanced local search procedures. To do so, first we model the problem using Mixed-Integer Linear Programming and Constraint Programming, so optimal solutions can be found for small-size problem instances and the quality of the approximate solutions can be better assessed. Next, we propose an innovative Q-Learning Variable Greedy (QLVG) algorithm to provide approximate solutions for medium/big instance sizes. Q-learning is an Artificial Intelligence technique that we use to dynamically obtain the best combination of parameters of a Variable Greedy Search algorithm. Our proposal is compared to the aforementioned exact methods and to six state-of-the-art procedures for the problem, as well as for closely related problems in a testbed with 800 instances. According to the statistical analysis carried out, the proposed QLVG outperformed all the implemented algorithms from the literature, achieving an exceptional performance in terms of the quality of the solutions.

本文解决了一个流程车间中的调度问题，其中作业在第一台机器上开始加工时间后不能有空闲时间（即无等待约束）。目标是最小化作业的早完成和延迟完成的总和，以便优化通常与作业的早完成和晚完成相关的库存和积压成本。由于这个决策问题被认为是np困难的，以前的贡献集中在启发式程序的建议上，以在合理的计算时间内产生良好的（尽管不是最优的）解决方案。尽管取得了这些进步，但我们认为仍有空间开发更有效的解决方案程序，特别是通过将机器学习（ML）技术与高级局部搜索程序相结合。为了做到这一点，我们首先使用混合整数线性规划和约束规划对问题进行建模，因此可以找到小尺寸问题实例的最优解，并且可以更好地评估近似解的质量。接下来，我们提出了一种创新的Q-Learning变量贪心（QLVG）算法，为中/大实例规模提供近似解。Q-learning是一种人工智能技术，我们使用它来动态地获得变量贪心搜索算法的最佳参数组合。将我们的建议与上述精确的方法以及针对该问题的六种最先进的程序进行比较，并在具有800个实例的测试平台中对密切相关的问题进行比较。根据所进行的统计分析，所提出的QLVG优于文献中所有已实现的算法，在解的质量方面取得了卓越的性能。

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Novel models and efficient heuristic for the vessel–unmanned surface vehicle routing problem 船舶-无人水面车辆路径问题的新模型和高效启发式算法

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

Computers & Operations Research

Pub Date : 2026-01-06 DOI: 10.1016/j.cor.2026.107384

Lianhua Tang , Yantong Li , Kanglin Liu , Jean-François Côté , Leandro C. Coelho

This paper addresses the vessel–unmanned surface vehicle (USV) routing problem (VURP), which jointly determines the routes of a vessel and a USV. The problem is applicable to offshore supply, search and rescue, and inspection operations. In the VURP, a vessel carries a loaded USV and the USV departs from the vessel to deliver goods to offshore platforms when in proximity. The objective is to minimize the total routing costs of both vehicles by optimizing their routes along with the USV’s departure and arrival points. We begin by exploiting the structural properties of the problem and propose two enhanced mixed-integer second-order conic programming (MISOCP) formulations, each further strengthened with valid inequalities to improve computational performance. Due to the NP-hard nature of the problem, we develop a tailored adaptive large neighborhood search (ALNS) algorithm designed to handle practical-sized instances. The proposed ALNS employs a two-phase framework to enable a multi-start mechanism: the first phase generates a diverse set of initial solutions using an effective approximation-based scheme, while the second phase iteratively improves these solutions through a problem-specific ALNS procedure. This structure balances global exploration and local intensification, enhancing both solution diversity and robustness. Extensive computational experiments demonstrate the strong performance of the improved formulations and the heuristic method. The integration of USVs significantly reduces vessel travel costs, especially when the USVs have larger capacities. Moreover, numerical results on benchmark instances show that our ALNS outperforms state-of-the-art heuristics, achieving 45 new best solutions among 72 open benchmark cases.

本文研究了船舶-无人水面车辆（USV）的路由问题（VURP），该问题是船舶和无人水面车辆共同确定路径的问题。该问题适用于海上供应、搜救和检查作业。在VURP中，一艘船携带装载的USV， USV离开船，在附近向海上平台运送货物。目标是通过优化USV的出发和到达点的路线，使两辆车的总路线成本最小化。我们首先利用问题的结构性质，提出了两个增强的混合整数二阶二次规划（MISOCP）公式，每个公式都进一步加强了有效不等式以提高计算性能。由于问题的np困难性质，我们开发了一种定制的自适应大邻域搜索（ALNS）算法，旨在处理实际大小的实例。提出的ALNS采用两阶段框架来实现多启动机制：第一阶段使用有效的基于近似的方案生成多样化的初始解决方案，而第二阶段通过特定问题的ALNS过程迭代改进这些解决方案。这种结构平衡了全局探索和局部强化，增强了解决方案的多样性和稳健性。大量的计算实验证明了改进公式和启发式方法的良好性能。usv的集成大大降低了船舶旅行成本，特别是当usv具有更大的容量时。此外，在基准实例上的数值结果表明，我们的ALNS优于最先进的启发式算法，在72个开放基准案例中获得了45个新的最佳解决方案。

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The Robust Multicrew Scheduling and Routing Problem in road restoration 道路恢复中的鲁棒多机组调度与路由问题

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

Computers & Operations Research

Pub Date : 2026-01-03 DOI: 10.1016/j.cor.2025.107379

Alfredo Moreno , Douglas Alem , Michel Gendreau , Pedro Munari

Restoring transportation networks is crucial in the aftermath of disasters due to severe disruptions that hinder emergency response efforts, including delays in relief distribution, search-and-rescue operations, and medical assistance. This logistical activity becomes even more challenging with uncertain repair times, as repair durations can significantly deviate from expected conditions. This uncertainty makes it difficult to create accurate repair schedules and to allocate resources efficiently for restoration. The Robust Multicrew Scheduling and Routing Problem (RMCSRP) addresses this challenge by optimizing the scheduling, routing, and coordination of multiple heterogeneous repair crews to robustly restore network accessibility, thus accommodating the uncertainty of repair times. We introduce a latency objective function that considers not only the time required to restore accessibility but also the travel time along relief paths. Given the computational complexity of the RMCSRP, we have developed a logic-based Benders decomposition to solve the problem within reasonable computational times. Our approach is validated through computational experiments on real-world disaster scenarios, demonstrating its effectiveness in restoring normalcy and improving accessibility for disaster response operations. Particularly, our results show that the Benders-based approach achieves optimal and near-optimal solutions in 47.81% and 100% of the tested instances, respectively, compared to 37.47% and 72.24% with the MIP model. The robust model also prevents total latency violations in 100% of the scenarios generated via Monte Carlo simulation, while maintaining the price of robustness below 13%. Lastly, we demonstrate that increasing the number of crews improves solution robustness, with latency violations decreasing by nearly 30% when using three crews instead of one.

灾后恢复交通网络至关重要，因为严重的中断阻碍了应急响应工作，包括救济品分发、搜索和救援行动以及医疗援助的延误。由于维修时间不确定，维修持续时间可能会大大偏离预期条件，因此这种后勤活动变得更加具有挑战性。这种不确定性使得制定准确的维修计划和有效地分配修复资源变得困难。鲁棒多机组调度和路由问题（RMCSRP）通过优化调度、路由和多个异构维修机组的协调来解决这一挑战，以鲁棒地恢复网络可达性，从而适应维修时间的不确定性。我们引入了一个延迟目标函数，它不仅考虑了恢复可达性所需的时间，而且考虑了沿救济路径的旅行时间。考虑到RMCSRP的计算复杂性，我们开发了一种基于逻辑的Benders分解，以在合理的计算时间内解决问题。我们的方法通过真实灾难场景的计算实验进行了验证，证明了其在恢复正常状态和提高灾难响应操作的可及性方面的有效性。特别是，我们的结果表明，基于benders的方法分别在47.81%和100%的测试实例中获得最优和接近最优解，而MIP模型分别为37.47%和72.24%。鲁棒性模型还可以在通过蒙特卡罗模拟生成的100%场景中防止总延迟违规，同时将鲁棒性的代价保持在13%以下。最后，我们证明增加工作人员的数量可以提高解决方案的鲁棒性，当使用三个工作人员而不是一个工作人员时，延迟违规减少了近30%。

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