Computers & Operations Research最新文献_第2页

A matheuristic for solving the single row facility layout problem 求解单行设施布局问题的数学方法

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

Computers & Operations Research

Pub Date : 2026-01-21 DOI: 10.1016/j.cor.2026.107397

Thomas Pammer , Markus Sinnl

The single row facility layout problem (SRFLP) is a well-studied NP-hard combinatorial optimization problem with applications in manufacturing and logistics systems. In the SRFLP, a set of facilities with lengths is given, as well as weights between each pair of facilities. The facilities must be arranged on a line, such that the sum of the weighted center-to-center distances is minimized. In this work, we introduce a novel matheuristic approach that integrates exact optimization into a metaheuristic framework based on simulated annealing to effectively solve large-scale SRFLP instances. Specifically, we propose the window approach matheuristic, which allows to solve subsegments of the layout to optimality using mixed-integer programming while preserving the ordering of facilities outside the window. To the best of our knowledge, this constitutes the first matheuristic approach specifically designed for the SRFLP. We evaluate the performance of our method on the widely-used benchmark instance sets from literature. The computational results demonstrate that our matheuristic improves the best-known solution values for 13 of 70 instances, and matches the best-known solution values for the remaining 57 instances, outperforming current state-of-the-art metaheuristics.

单排设施布局问题（SRFLP）是一个在制造和物流系统中应用广泛的NP-hard组合优化问题。在SRFLP中，给出了一组具有长度的设施，以及每对设施之间的权值。这些设施必须布置在一条线上，使中心到中心的加权距离之和最小。在这项工作中，我们引入了一种新的数学方法，该方法将精确优化集成到基于模拟退火的元启发式框架中，以有效地解决大规模SRFLP实例。具体来说，我们提出了窗口数学方法，它允许使用混合整数规划解决布局的子段的最优性，同时保留窗口外设施的顺序。据我们所知，这是第一个专门为SRFLP设计的数学方法。我们在文献中广泛使用的基准实例集上评估了我们的方法的性能。计算结果表明，我们的数学方法提高了70个实例中13个最知名的解决方案值，并与其余57个实例的最知名解决方案值相匹配，优于当前最先进的元启发式方法。

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引用次数: 0

An exact solver for submodular knapsack problems 子模背包问题的精确求解器

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

Computers & Operations Research

Pub Date : 2026-01-20 DOI: 10.1016/j.cor.2026.107407

Sabine Münch, Stephen Raach

We study the problem of maximizing a monotone increasing submodular function over a set of weighted elements subject to a knapsack constraint. Although this problem is NP-hard, some applications require exact solutions, as approximate solutions are often insufficient in practice. To address this need, we propose an exact branch-and-bound algorithm tailored for the submodular knapsack problem and introduce several acceleration techniques to enhance its efficiency. We evaluate these techniques on artificial instances of three benchmark problems as well as on instances derived from real-world data. We compare the proposed solver with two solvers by Sakaue and Ishihata (2018) as well as with a branch-and-cut algorithm implemented using Gurobi that solves a binary linear reformulation of the submodular knapsack problem, demonstrating that our methods are highly successful.

研究了在背包约束下的一组加权元素上的单调递增子模函数的最大化问题。虽然这个问题是np困难的，但一些应用需要精确的解，因为近似解在实践中往往是不够的。为了满足这一需求，我们提出了一种针对子模块背包问题的精确分支定界算法，并引入了几种加速技术来提高其效率。我们在三个基准问题的人工实例以及来自真实世界数据的实例上评估了这些技术。我们将提出的求解器与Sakaue和Ishihata（2018）的两个求解器以及使用Gurobi实现的分支切断算法（branch-and-cut algorithm）进行了比较，该算法解决了子模块背包问题的二元线性重构，表明我们的方法非常成功。

引用次数: 0

A survey of workforce scheduling and routing problems 劳动力调度和路由问题的调查

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

Computers & Operations Research

Pub Date : 2026-01-20 DOI: 10.1016/j.cor.2025.107344

Nicolás Cabrera, Jean-François Cordeau, Jorge E. Mendoza

The workforce scheduling and routing problem (WSRP) involves assigning geographically dispersed tasks to workers and planning their routes to complete these tasks efficiently. This problem arises in numerous real-world scenarios, including technicians conducting preventive maintenance at customer sites, nurses providing home care, and security guards patrolling multiple locations. To address these challenges, researchers have incorporated a wide range of constraints, such as time windows, skill compatibility, and team composition. In this survey, we systematically structure and analyze the WSRP literature, identifying its core characteristics and uncovering key research gaps. Our findings highlight critical areas for future investigation, including the integration of multi-modal routes and precedence constraints. Additionally, we emphasize practical features that should guide the development of new solution methods for this family of problems, ensuring their applicability to real-world workforce management challenges.

劳动力调度和路由问题（WSRP）涉及到将地理上分散的任务分配给工人并规划他们的路线以有效地完成这些任务。这个问题出现在许多实际场景中，包括在客户站点进行预防性维护的技术人员、提供家庭护理的护士以及在多个地点巡逻的保安。为了应对这些挑战，研究人员纳入了广泛的约束条件，如时间窗口、技能兼容性和团队组成。在本次调查中，我们对WSRP文献进行了系统的结构和分析，确定了其核心特征，并揭示了关键的研究空白。我们的研究结果强调了未来研究的关键领域，包括多模式路线的整合和优先约束。此外，我们强调了应该指导针对这一系列问题的新解决方案方法开发的实用特性，确保它们适用于现实世界的劳动力管理挑战。

引用次数: 0

New cutting planes for open-pit mine scheduling with multi-period block extraction 露天矿多周期分段开采调度的新型切割平面

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

Computers & Operations Research

Pub Date : 2026-01-20 DOI: 10.1016/j.cor.2026.107408

Venkat Akhil Ankem , Guy Desaulniers , Michel Gamache , Vincent Raymond

The open-pit mine production scheduling problem (OPMPSP) is a fundamental planning problem in mining engineering. Given a discretized ore body representation known as a block model, the OPMPSP consists in computing the schedule of block excavation (extraction time, quantity, and processing decisions) over a planning horizon while adhering to operational constraints and maximizing the net present value of the profit. This problem is typically modeled using a large-sized mixed-integer linear programming (MILP) formulation. In this paper, we focus on an OPMPSP involving blocks of varying sizes that can be extracted over multiple periods. For this problem, we propose a new MILP formulation that includes two types of scheduling variables, one indicating the starting period of the extraction of a block and the other its ending period. Considering these two variable types allows to introduce new versions of known cutting planes as well as new cut families that are defined for blocks requiring multiple periods to be extracted. All cuts are generated a priori and added to the MILP formulation which is then solved by a commercial MILP solver. Through extensive computational experiments on three real-life OPMPSP instances, we demonstrate that the proposed cuts significantly reduce computational times (by up to 70.3%), making a valuable contribution to large-scale mine planning optimization. This methodology is also integrated into a rolling-horizon heuristic, where the cutting planes can be updated at each iteration.

露天矿生产调度问题（OPMPSP）是采矿工程中的一个基本规划问题。给定一个被称为区块模型的离散矿体表示，OPMPSP包括在计划范围内计算区块挖掘时间表（开采时间、数量和处理决策），同时遵守操作约束并最大化净现值利润。该问题通常使用大型混合整数线性规划（MILP）公式进行建模。在本文中，我们关注的是一个OPMPSP，它涉及可以在多个时间段提取的不同大小的块。针对这一问题，我们提出了一种新的MILP公式，该公式包含两种调度变量，一种表示区块提取的开始周期，另一种表示区块提取的结束周期。考虑到这两个变量类型，可以引入已知切割平面的新版本，以及为需要提取多个周期的块定义的新切割族。所有切割都是先验生成的，并添加到MILP公式中，然后由商业MILP求解器求解。通过对三个现实生活中的OPMPSP实例进行广泛的计算实验，我们证明了所提出的削减方法显着减少了计算时间（高达70.3%），为大规模矿山规划优化做出了宝贵的贡献。该方法还集成到滚动地平线启发式中，其中切割平面可以在每次迭代中更新。

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引用次数: 0

Clusterwise linear regression using a probabilistic branch and bound algorithm under Gaussianity 基于高斯性的概率分支定界算法的聚类线性回归

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

Computers & Operations Research

Pub Date : 2026-01-19 DOI: 10.1016/j.cor.2025.107375

A. Fois , L. Insolia , L. Consolini , F. Laurini , M. Locatelli , M. Riani

Clusterwise Linear Regression (CLR) combines classical linear regression with cluster analysis to model heterogeneous data. It overcomes the limitations of a single global model by simultaneously partitioning the data points into distinct clusters and fitting each cluster separately. However, since the underlying point-to-cluster assignments are unknown, the estimation process typically leads to a computationally challenging combinatorial problem. In this work, we introduce a new reformulation of the CLR problem under Gaussian assumptions, and propose a probabilistic branch-and-bound algorithm called pclustreg. This algorithm gives, with high probability, solutions that are at least as good as the (unknown) ground truth in terms of log-likelihood, bridging the gap between existing likelihood-based heuristic and global methods. Moreover, by limiting the number of expanded nodes, it can also be used as an effective heuristic algorithm. We highlight the performance of pclustreg on both synthetic and real-world datasets, comparing it against the state-of-the-art likelihood-based heuristic method, and show that it achieves comparable or better results both in terms of solution accuracy and computing times.

聚类线性回归（CLR）将经典线性回归与聚类分析相结合，对异构数据进行建模。它通过同时将数据点划分为不同的聚类并单独拟合每个聚类来克服单个全局模型的局限性。然而，由于潜在的点到簇分配是未知的，估计过程通常会导致计算上具有挑战性的组合问题。在本文中，我们引入了高斯假设下CLR问题的一种新的重新表述，并提出了一种称为pclustreg的概率分支定界算法。该算法以高概率给出至少与对数似然（未知）基础真值一样好的解决方案，弥合了现有基于似然的启发式方法和全局方法之间的差距。此外，通过限制扩展节点的数量，它还可以作为一种有效的启发式算法。我们强调了pclustreg在合成数据集和真实数据集上的性能，将其与最先进的基于似然的启发式方法进行了比较，并表明它在解决方案精度和计算时间方面都取得了相当或更好的结果。

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引用次数: 0

An adaptive large neighborhood decomposition search-based approach for the location-routing problem with pickup facilities and heterogeneous demands 基于自适应大邻域分解搜索的取货设施和异构需求定位路由问题

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

Computers & Operations Research

Pub Date : 2026-01-19 DOI: 10.1016/j.cor.2026.107398

Zhaofang Mao , Yida Xu , Enyuan Fu

In last-mile delivery, the flexibility and heterogeneity of customer demands have driven package delivery companies to implement more adaptive strategies, such as utilizing pickup points and lockers. However, selecting optimal locations for these pickup points or lockers can be challenging due to various factors. To address these challenges, we propose the location-routing problem with pickup facilities and heterogeneous demands (LRP-PFHD). To solve this problem, we formulate a mixed integer linear programming (MILP) model to minimize the total cost. We adapt the adaptive large neighborhood decomposition search (ALNDS) algorithm by incorporating initial solution generation strategies to improve solution quality and efficiency. Furthermore, we conduct a comprehensive computational study to verify the effectiveness and efficiency of our proposed method. The results show that this distribution mode could give a total cost-saving of about 9.97%–42.40% compared to the conventional CVRP mode. Finally, we carry out a case study in Vienna, Graz, and Linz and conduct a sensitivity analysis to provide managerial insights.

在最后一英里的递送中，客户需求的灵活性和异质性促使包裹递送公司实施更具适应性的策略，例如利用取件点和储物柜。然而，由于各种因素，为这些取货点或储物柜选择最佳位置可能具有挑战性。为了解决这些挑战，我们提出了具有拾取设施和异构需求的位置路由问题（LRP-PFHD）。为了解决这个问题，我们建立了一个混合整数线性规划（MILP）模型，以最小化总成本。通过引入初始解生成策略，对自适应大邻域分解搜索（ALNDS）算法进行改进，以提高解的质量和效率。此外，我们进行了全面的计算研究，以验证我们提出的方法的有效性和效率。结果表明，与传统CVRP模式相比，该分配模式总成本节约约9.97% ~ 42.40%。最后，我们在维也纳、格拉茨和林茨进行了案例研究，并进行了敏感性分析，以提供管理见解。

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引用次数: 0

Optimization of fleet search on network of regions 区域网络上的车队搜索优化

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

Computers & Operations Research

Pub Date : 2026-01-15 DOI: 10.1016/j.cor.2026.107394

Ertan Yakıcı , Levent Eriskin , Mumtaz Karatas , Orhan Karasakal

Unmanned Aerial Vehicles (UAVs) are widely used in modern military missions, primarily for surveillance, reconnaissance, search and detection, and air-to-ground strikes. The widespread use of UAVs in recent conflicts, such as the Russia–Ukraine war, once again highlighted their growing strategic importance. The complexity of military missions carried out by UAVs, coupled with the need for autonomous and coordinated fleet operations, requires analytical approaches to optimize deployment planning and improve operational efficiency. In this study, we address a UAV deployment planning problem for search and detection missions, in which a homogeneous fleet of UAVs is tasked with searching for hostile assets across a network of disjoint regions. Each region is characterized by an a priori probability of target presence, a search difficulty factor which affects the probability of detection, and known inter-region distances.

For this purpose, we first develop a mixed-integer nonlinear programming formulation which determines the base locations of UAVs, allocates the limited search time across regions, and sequences the visits to maximize the total time-weighted detection probability mass to achieve the highest probability as much and as early as possible during the operation. Next, we apply a tangent line approximation technique to reformulate the model as a mixed-integer linear programming problem, which we solve using commercial off-the-shelf solvers. We then propose a hybrid heuristic approach based on the ant colony optimization method to generate high-quality solutions. Our computational experiments reveal that the proposed heuristic significantly reduces solution time while maintaining superior performance compared to the linear approximation model.

无人驾驶飞行器（uav）广泛用于现代军事任务，主要用于监视、侦察、搜索和探测以及空对地打击。无人机在最近的冲突中广泛使用，例如俄罗斯-乌克兰战争，再次突出了它们日益增长的战略重要性。无人机执行军事任务的复杂性，加上对自主和协调舰队作战的需求，需要分析方法来优化部署计划和提高作战效率。在本研究中，我们解决了搜索和探测任务的无人机部署规划问题，其中一个均匀的无人机舰队的任务是在不相交的区域网络中搜索敌对资产。每个区域的特征包括目标存在的先验概率、影响检测概率的搜索难度因子和已知的区域间距离。为此，我们首先开发了一种混合整数非线性规划公式，该公式确定无人机的基地位置，分配有限的区域搜索时间，并对访问进行排序，以最大化总时间加权检测概率质量，以便在操作过程中尽可能多、尽早地获得最高概率。接下来，我们应用切线近似技术将模型重新表述为混合整数线性规划问题，我们使用商用现成的求解器来解决这个问题。然后，我们提出了一种基于蚁群优化方法的混合启发式方法来生成高质量的解。我们的计算实验表明，与线性近似模型相比，所提出的启发式算法显着减少了求解时间，同时保持了优越的性能。

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引用次数: 0

Integrated routing optimization of pilotage and tugging services 综合航线优化引航和拖船服务

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

Computers & Operations Research

Pub Date : 2026-01-15 DOI: 10.1016/j.cor.2026.107395

Xin Wang , Yijing Liang , Haobin Li , Ek Peng Chew , Kok Choon Tan

Seaports are important connections between inland and maritime transportation. During the vessels’ entering/leaving ports, the pilotage service is necessary to mitigate risk, especially for large vessels and congested ports. In the pilotage process, pilots are transported by pilot boats to board the vessels and provide guidance until the vessels’ arriving/leaving the berths, and tugboats are in charge of providing horsepower for vessels to move safely near the port. In this paper, a joint optimization problem considering the pilotage and tugging services is studied. Realistic constraints, including the multi-waypoints of tugboats, required service time windows of vessels, different types of tugboats and pilots. A mixed integer programming model is introduced, and small-size instances are solved by CPLEX solver. To solve large-scale instances, an adaptive large neighborhood search algorithm with linear programming models (ALNS-LP) together with a tailored feasibility check procedure and cost evaluation process is proposed. Extensive computational experiments are conducted to verify efficiency and effectiveness of the algorithm and obtain some managerial insights for port operators.

海港是连接内陆和海上运输的重要枢纽。在船舶进出港口的过程中，引航服务是降低风险的必要条件，特别是对于大型船舶和拥挤的港口。在引航过程中，引航员由引航艇运送到船舶上并提供指导，直到船舶到达/离开泊位，拖船负责为船舶安全靠近港口提供马力。本文研究了一个考虑引航和拖航服务的联合优化问题。现实的制约因素包括拖船的多航路点、船舶所需的服务时间窗口、不同类型的拖船和引航员。引入了一种混合整数规划模型，并采用CPLEX求解器对小实例进行求解。为了解决大规模实例问题，提出了一种线性规划模型自适应大邻域搜索算法（ALNS-LP），并结合了定制化的可行性检验程序和成本评估过程。通过大量的计算实验验证了算法的效率和有效性，并为港口运营商提供了一些管理见解。

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引用次数: 0

Frequent pattern mining driven evolutionary search for cross-dock door assignment 频繁模式挖掘驱动的交叉码头门分配进化搜索

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

Computers & Operations Research

Pub Date : 2026-01-14 DOI: 10.1016/j.cor.2026.107393

Yongliang Lu , Jin-Kao Hao , Qinghua Wu , Mingjie Li

Capacitated cross-dock door assignment and uncapacitated cross-dock door assignment are two critical and challenging problems in supply chain management. This paper presents the first frequent pattern mining driven evolutionary algorithm to effectively solve these problems. The proposed approach incorporates a specialized data mining technique designed to extract significant frequent patterns from a collection of high-quality solutions, thereby guiding the search process. It also incorporates an efficient two-phase local optimization method that intensively inspects a given region to identify high-quality solutions, along with a quality-and-distance updating rule to manage the population of solutions. We evaluate the effectiveness of the proposed algorithm on popular benchmark instances of both problems. In particular, we report 26 improved best results (new upper bounds) out of 99 benchmark instances for the capacitated case and 25 improved best results out of 40 benchmark instances for the uncapacitated case. In addition, we show the importance of the two main search components, i.e., frequent pattern mining and local optimization. This research highlights the benefits of a collaboration between optimization algorithms and data mining methods. The code for our proposed algorithm will be made publicly available.

有能力的跨码头门分配和无能力的跨码头门分配是供应链管理中的两个关键和具有挑战性的问题。本文首次提出了频繁模式挖掘驱动的进化算法，有效地解决了这些问题。所提出的方法结合了专门的数据挖掘技术，旨在从高质量解决方案的集合中提取重要的频繁模式，从而指导搜索过程。它还结合了一种高效的两阶段局部优化方法，该方法集中检查给定区域以识别高质量的解决方案，以及质量和距离更新规则来管理解决方案的数量。我们在这两个问题的常用基准实例上评估了所提出算法的有效性。特别是，我们报告了在99个有能力情况的基准实例中有26个改进的最佳结果（新的上限），在40个无能力情况的基准实例中有25个改进的最佳结果。此外，我们还展示了两个主要搜索组件的重要性，即频繁模式挖掘和局部优化。这项研究强调了优化算法和数据挖掘方法之间协作的好处。我们提出的算法的代码将会公开。

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引用次数: 0

Humanitarian relief logistics network design considering facility location, inventory pre-positioning and evacuation planning: A two-stage distributionally robust optimization approach 考虑设施选址、库存预定位和疏散规划的人道主义救援物流网络设计：一种两阶段分布鲁棒优化方法

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

Computers & Operations Research

Pub Date : 2026-01-14 DOI: 10.1016/j.cor.2026.107390

Tao Zhang , Shuaian Wang , Xu Xin

The high uncertainty in the occurrence, space, and scale of natural disasters presents significant challenges to reliable humanitarian relief logistics network (HRLN) design. After a disaster occurs, relief supplies and evacuees are usually transported simultaneously through the HRLN, which occupies limited logistics infrastructure (i.e., roads). This phenomenon drives the integration of three crucial decisions in the design of HRLNs: the emergency facility locations, the pre-positioning of the relief inventory, and the human evacuation planning. This composite problem is formulated as a two-stage distributionally robust optimization model, with the two stages corresponding to pre-disaster and post-disaster decision-making. To capture the characteristics of the distribution functions of the number of evacuees and the road capacity, we design an ambiguity set using historical data and the type-1 Wasserstein metric. We show that there is an equivalent reformulation of the abovementioned model that can be solved by decomposition algorithms. Two versions of the decomposition algorithm, i.e., single-cut and multi-cut versions, are developed based on the generic Benders-decomposition technique. A case study is conducted on the Yushu earthquake in China and several managerial implications are proposed.

自然灾害发生、空间和规模的高度不确定性给可靠的人道主义救援物流网络（HRLN）设计提出了重大挑战。灾难发生后，救济物资和撤离人员通常同时通过HRLN运输，而HRLN占用有限的物流基础设施（即道路）。这一现象推动了HRLNs设计中三个关键决策的整合：应急设施位置、救援库存的预定位和人员疏散计划。将该复合问题表述为一个两阶段分布鲁棒优化模型，分别对应灾前和灾后决策。为了捕捉疏散人数和道路容量分布函数的特征，我们使用历史数据和type-1 Wasserstein度量设计了一个模糊集。我们证明了上述模型有一个等价的重新表述，可以用分解算法求解。在通用benders分解技术的基础上，提出了单切口和多切口两种版本的分解算法。本文以中国玉树地震为例进行了研究，并提出了几点管理启示。

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引用次数: 0