Computers & Operations Research最新文献_第2页

Multi-commodity vehicle routing problem with pickup and delivery for electric micromobility devices rebalancing and battery swapping 电动微移动设备再平衡和电池交换的多商品车辆路线问题

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

Computers & Operations Research

Pub Date : 2025-12-02 DOI: 10.1016/j.cor.2025.107348

Esra Koca, Arghavan Sharafi

This study addresses a variant of the multi-commodity vehicle routing problem that arises in electric micromobility systems, where a fleet of capacitated vehicles is tasked with simultaneously performing device rebalancing and battery swapping operations. The objective is to satisfy station-level demands while minimizing total travel cost. We refer to this problem as the Electric Micromobility Device Rebalancing and Battery Swapping Problem (EMDRBS), which extends the classical pickup and delivery vehicle routing model by incorporating multi-commodity flows and battery replacement constraints. To solve EMDRBS, we propose four mixed-integer linear programming (MIP) formulations and evaluate their computational performance on a set of realistic benchmark instances derived from public bike-sharing data. While two formulations exhibit strong performance on medium- and large-scale instances, solving the largest cases to optimality remains computationally challenging. To address it, we develop a fix-and-optimize (FixOpt) matheuristic that dynamically adjusts its destruction strategies based on past performance and repairs partial solutions through restricted MIP reoptimization. Computational results show that among the four MIP formulations, F3 and F4 provide the best trade-off between strength and scalability, while the proposed FixOpt matheuristic delivers comparable solution quality with substantially shorter runtimes on large instances. Moreover, coordinated planning of rebalancing and battery swapping reduces total travel distance by up to 50% — particularly under high demand and limited vehicle capacity — without increasing depot resource requirements, demonstrating the practical value of integrated optimization.

本研究解决了电动微移动系统中出现的多商品车辆路径问题的一个变体，其中一组有能力的车辆的任务是同时执行设备再平衡和电池交换操作。目标是在满足车站需求的同时最小化总旅行成本。我们将此问题称为电动微移动设备再平衡和电池更换问题（EMDRBS），该问题通过纳入多商品流和电池更换约束，扩展了经典的皮卡和送货车辆路线模型。为了解决EMDRBS问题，我们提出了四种混合整数线性规划（MIP）公式，并在一组来自公共自行车共享数据的现实基准实例上评估了它们的计算性能。虽然两种公式在中型和大型实例上表现出强大的性能，但解决最大情况下的最优性仍然是计算上的挑战。为了解决这个问题，我们开发了一种修复和优化（FixOpt）数学方法，该方法可以根据过去的性能动态调整其破坏策略，并通过受限的MIP再优化修复部分解决方案。计算结果表明，在四种MIP配方中，F3和F4提供了强度和可扩展性之间的最佳权衡，而提出的FixOpt数学方法在大型实例上以更短的运行时间提供了相当的解决方案质量。此外，再平衡和电池交换的协调规划在不增加仓库资源需求的情况下，将总行程缩短了50%，特别是在高需求和有限的车辆容量下，这表明了集成优化的实用价值。

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引用次数: 0

An integrated approach for hybrid flowshop scheduling with setup times and limited buffers in printed circuit board manufacturing system 印刷电路板制造系统中具有设置时间和有限缓冲的混合流水车间调度集成方法

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

Computers & Operations Research

Pub Date : 2025-12-02 DOI: 10.1016/j.cor.2025.107338

Huali Fan

Printed Circuit Board (PCB) manufacturing is an important sector in electronics industry. To improve its production efficiency, it is necessary to optimize the PCB manufacturing processes. In this paper, the PCB manufacturing processes are viewed as a hybrid flowshop scheduling problem with additional characteristics including sequence-dependent setup times and limited intermediate buffers. The mixed integer linear programming models of the problem are presented and they are used as an exact method to solve the problem optimally. As this problem involves multiple types of decisions, including batching, machine assignment and job sequencing, to be made, the decomposition approach which decomposes the problem into subproblems and considers them sequentially is usually used as a heuristic method to solve the problem of larger size. Since the multiple types of decisions for the problem are interdependent and mutually influential, this paper presents an integrated approach to consider these decisions simultaneously. The integrated approach is based on the Iterated Local Search (ILS) algorithm, and two local search procedures working on the job sequencing dimension and two perturbation procedures working on the machine assignment dimension are presented. The performance of the ILS-based integrated approach is evaluated by comparing it against the decomposition approach under a variety of problem instances, and the results show that the integration approach is more efficient than the decomposition approach. Besides, the performance of the ILS-based integrated approach is compared against integrated approaches based on other heuristic algorithms, and the results demonstrate that the ILS-based integrated approach outperforms the others. In addition, the sensitivity analysis of the problem parameters on the performance of the ILS-based integrated approach indicates that the integrated approach is robust under different levels of setup times and buffer capacity.

印刷电路板（PCB）制造是电子工业的一个重要部门。为了提高其生产效率，有必要对PCB制造工艺进行优化。在本文中，PCB制造过程被视为具有附加特征的混合流水车间调度问题，包括顺序相关的设置时间和有限的中间缓冲区。提出了该问题的混合整数线性规划模型，并将其作为最优求解该问题的精确方法。由于该问题涉及多个类型的决策，包括批处理、机器分配和作业排序等，因此将问题分解为子问题并依次考虑的分解方法通常被用作求解较大规模问题的启发式方法。由于问题的多种类型的决策是相互依存和相互影响的，本文提出了一种综合的方法来同时考虑这些决策。该方法基于迭代局部搜索（ILS）算法，给出了工作排序维的两个局部搜索过程和工作分配维的两个扰动过程。在多种问题实例下，通过与分解方法的比较，对基于il的集成方法的性能进行了评价，结果表明，集成方法比分解方法更有效。此外，将基于il的集成方法与基于其他启发式算法的集成方法进行了性能比较，结果表明基于il的集成方法优于其他启发式算法。此外，对问题参数对集成方法性能的敏感性分析表明，在不同的设置时间和缓冲容量水平下，集成方法具有鲁棒性。

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引用次数: 0

Two-agent single batch machine scheduling with a fixed capacity distribution 具有固定容量分配的双代理单批机器调度

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

Computers & Operations Research

Pub Date : 2025-11-30 DOI: 10.1016/j.cor.2025.107346

Bayi Cheng, Junwei Gao, Lingjun Wang, Mi Zhou

We study a joint scheduling problem where a single batch-processing machine is used to process jobs from two competing agents. Once the jobs are completed, they are delivered by a single vehicle to the agents with distinct delivery times. We propose a polynomial-time approximation algorithm Coordinate Scheduling Algorithm,

C S A

, to optimize the joint problem. Particularly, an optimal distribution scheme is proposed for two agents problem. Three models are considered, where production constraints are different. First, for the model where jobs have identical sizes, we prove that our algorithm is optimal when the production scheme is given. Second, for the model where jobs have identical processing times, we propose a modified algorithm

C S A_{p}

, which achieves a service span asymptotically at most 1.55 times the optimal. Third, for the model where jobs have arbitrary processing times and sizes, the algorithm can produce a schedule and delivery plan with a service span asymptotically at most twice the optimal. Finally, we evaluate the performance of the proposed algorithm with a set of computational experiments and the results show the effectiveness of our algorithm.

我们研究了一个联合调度问题，其中单个批处理机器用于处理来自两个竞争代理的作业。一旦作业完成，它们将由一辆车以不同的交付时间交付给代理。我们提出了一种多项式时间逼近算法——坐标调度算法（Coordinate Scheduling algorithm， CSA）来优化联合问题。特别地，针对双智能体问题，提出了一种最优分配方案。考虑三种模型，其中生产约束是不同的。首先，对于作业大小相同的模型，我们证明了当生产方案给定时，算法是最优的。其次，对于作业具有相同处理时间的模型，我们提出了一种改进的CSAp算法，该算法使服务跨度渐近地最多达到最优的1.55倍。第三，对于作业具有任意处理时间和大小的模型，该算法可以生成服务跨度渐近不超过最优的两倍的调度和交付计划。最后，通过一组计算实验对算法的性能进行了评价，结果表明了算法的有效性。

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引用次数: 0

Slack-aware scheduling of AGVs in just-in-time matrix manufacturing systems via adaptive large neighborhood search 基于自适应大邻域搜索的准时制矩阵制造系统agv松弛感知调度

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

Computers & Operations Research

Pub Date : 2025-11-28 DOI: 10.1016/j.cor.2025.107340

Boyu Li, Weimin Wu, Shuo Wang, Dacheng Li

The growing demand for intelligent logistics accelerates the adoption of automated guided vehicles (AGVs), making the AGV scheduling problem (AGVSP) a central research focus. The matrix manufacturing system (MMS), utilizing AGVs for Just-In-Time (JIT) pickup and delivery, attracts considerable attention as a promising manufacturing paradigm. However, existing studies on AGVSP in MMS primarily focus on task assignment, while neglecting the optimization of execution schedules from a temporal perspective, which is crucial in JIT scenarios. To bridge this research gap, this paper investigates a slack-aware AGVSP in MMS, integrating both task assignment and temporal optimization. A slack-aware execution strategy (SES) is proposed, incorporating forward time slack and backward time tightening to regulate slack time for task execution. To address task assignment, an adaptive large neighborhood search (ALNS) algorithm is developed, incorporating problem-specific initialization, neighborhood operators, and an instance-adaptive acceptance criterion. Experimental results demonstrate that SES significantly reduces earliness penalty, yielding near-optimal schedules while balancing computational complexity and optimization performance. Further comparisons reveal that ALNS outperforms three state-of-the-art metaheuristics and the Gurobi solver. A sensitivity analysis further provides managerial insights into optimizing AGV and workstation parameters.

智能物流需求的增长加速了自动导引车（AGV）的采用，使AGV调度问题（AGVSP）成为研究的焦点。矩阵制造系统（MMS）利用agv进行准时制（JIT）取货和交付，作为一种有前途的制造模式引起了相当大的关注。然而，现有的关于MMS中AGVSP的研究主要集中在任务分配上，而忽略了从时间角度优化执行计划，这在JIT场景中至关重要。为了弥补这一研究空白，本文研究了一种集成任务分配和时间优化的MMS松弛感知AGVSP。提出了一种松弛感知执行策略（SES），将前向时间松弛和后向时间收紧相结合，调节任务执行的松弛时间。为了解决任务分配问题，开发了一种自适应大邻域搜索（ALNS）算法，该算法结合了特定问题的初始化、邻域算子和实例自适应接受准则。实验结果表明，SES显著减少了早期惩罚，在平衡计算复杂性和优化性能的同时产生了接近最优的调度。进一步的比较表明，ALNS优于三种最先进的元启发式和Gurobi求解器。灵敏度分析进一步为优化AGV和工作站参数提供管理见解。

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引用次数: 0

Two-echelon optimization framework for semi-autonomous truck platooning in container drayage 集装箱拖运半自主卡车队列的两梯队优化框架

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

Computers & Operations Research

Pub Date : 2025-11-28 DOI: 10.1016/j.cor.2025.107343

Zhaojie Xue , Wenxiang Peng , Jialu Zhang , Rui Chen

Local container drayage is a critical component of port-hinterland logistics, involving short-distance transportation between container terminals, inland depots, and end customers. This paper investigates the integration of semi-autonomous truck platooning into a two-echelon drayage system and proposes a novel optimization framework that captures the operational characteristics of coordinated vehicle movements in the trunk leg. A mixed-integer programming model is developed to jointly optimize vehicle routing, platoon formation, and cargo assignment, with the objective of minimizing total transportation costs. Given the computational complexity, a tailored tabu search algorithm is designed to efficiently generate high-quality solutions for large-scale instances. Extensive computational experiments on both synthetic and real-world-inspired datasets demonstrate the effectiveness of the proposed approach in terms of solution quality and computational time. The results highlight the potential of semi-autonomous platooning to reduce operational costs, enhance system efficiency, and improve resource utilization, particularly in urban logistics settings. These findings offer valuable insights for public sector decision-making and infrastructure planning in both developed and emerging economies.

本地集装箱运输是港口腹地物流的重要组成部分，涉及集装箱码头、内陆仓库和最终客户之间的短途运输。本文研究了半自主卡车队列与两梯队运输系统的集成，并提出了一种新的优化框架，该框架捕捉了车辆在主干腿部协调运动的操作特征。以总运输成本最小为目标，建立了混合整数规划模型，对车辆路线、排队形和货物分配进行联合优化。考虑到禁忌搜索算法的计算复杂度，设计了一种定制的禁忌搜索算法，以有效地生成大规模实例的高质量解。在合成和现实世界启发的数据集上进行了大量的计算实验，证明了所提出的方法在解决质量和计算时间方面的有效性。研究结果强调了半自动车队在降低运营成本、提高系统效率和提高资源利用率方面的潜力，特别是在城市物流环境中。这些发现为发达国家和新兴经济体的公共部门决策和基础设施规划提供了宝贵的见解。

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引用次数: 0

Generic machine-learning-augmented beam search for resource-constrained shortest path reformulations of combinatorial optimization problems 资源约束下组合优化问题最短路径重构的通用机器学习增强束搜索

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

Computers & Operations Research

Pub Date : 2025-11-27 DOI: 10.1016/j.cor.2025.107339

Fulin Yan , François Clautiaux , Aurélien Froger , Boris Albar

In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search, where an ML model serves as one of the scoring functions to select candidate paths for expansion, complementing lower and upper bound estimates. Our method offers several key advantages. First, the path features used by our model are generic and only derived from the reformulation, without relying on any additional problem-specific information beyond the graph structure and resource constraints. Second, we manually designed and aggregated features to obtain vectors of fixed length, enabling us to train the model on small to medium-sized instances and apply it to much larger instances. We evaluate our algorithm on two benchmark problems: the Single Machine Total Weighted Tardiness Problem and the Temporal Knapsack Problem, each under two settings: with and without access to optimal Lagrangian multipliers. Our numerical experiments show that integration of ML into an anytime beam search enhances its solution quality in most cases, with at most minor performance degradation in the other cases. They suggest that one should systematically incorporate ML into the approach.

在这项工作中，我们提出了一个通用的启发式的资源约束最短路径问题，从硬组合优化问题的动态规划的重新表述。该方法是一种机器学习（ML）增强光束搜索，其中ML模型作为评分函数之一，用于选择候选路径进行扩展，补充下限和上限估计。我们的方法有几个关键的优点。首先，我们的模型使用的路径特征是通用的，并且仅从重新表述中派生出来，不依赖于图结构和资源约束之外的任何其他特定于问题的信息。其次，我们手动设计和聚合特征以获得固定长度的向量，使我们能够在中小型实例上训练模型，并将其应用于更大的实例。我们在两个基准问题上评估了我们的算法：单机总加权延迟问题和时间背包问题，每个问题都有两种设置：有和没有最优拉格朗日乘子。我们的数值实验表明，在大多数情况下，将机器学习集成到任意波束搜索中可以提高其解的质量，而在其他情况下，性能下降很小。他们建议应该系统地将ML纳入该方法。

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引用次数: 0

An exact decomposition-based approach to the conflict-free-transportation-constrained flexible job-shop scheduling problem 基于精确分解的无冲突运输约束柔性作业车间调度方法

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

Computers & Operations Research

Pub Date : 2025-11-26 DOI: 10.1016/j.cor.2025.107342

Roel W.M. van Os, Twan Basten

Automated material handling and transportation of semi-finished products has great potential to enhance the efficiency of Flexible Manufacturing Systems (FMSs). The resulting optimization problems for finding the shortest-makespan manufacturing schedules are complex and often hard to solve. Automated vehicles used for transportation must avoid colliding with one another while completing all required transports in the shortest possible time. The Conflict-Free-Transportation-constrained Flexible Job-Shop Scheduling Problem (CFTFJSSP) is the combination of flexible job-shop scheduling with conflict-free vehicle routing. To solve the CFTFJSSP, we propose an exact Logic-Based Benders Decomposition (LBBD) using both Constraint Programming (CP) and Integer Linear Programming (ILP) techniques. This LBBD-based approach is proven to outperform existing approaches to solving the CFTFJSSP in terms of solution quality on benchmark instances currently available in the literature. For most benchmark instances, our LBBD-based approach finds optimal makespan values. Because of time boxing, only in a few cases, the optimality of the found solution cannot be guaranteed. The solutions found by our LBBD-based approach show a makespan improvement of at least 10% for about half of the benchmarks instances, up to a 35% improvement in the best case, when compared to the heuristic solution approaches from the literature.

自动化物料处理和半成品运输在提高柔性制造系统（FMSs）效率方面具有很大的潜力。由此产生的寻找最短完工时间制造计划的优化问题是复杂的，往往难以解决。用于运输的自动化车辆必须避免相互碰撞，并在尽可能短的时间内完成所有所需的运输。无冲突运输约束柔性作业车间调度问题（CFTFJSSP）是柔性作业车间调度与无冲突车辆路径的结合。为了解决CFTFJSSP，我们提出了一种精确的基于逻辑的Benders分解（LBBD）方法，该方法使用约束规划（CP）和整数线性规划（ILP）技术。就目前文献中可用的基准实例的解决方案质量而言，这种基于lbbd的方法被证明优于解决CFTFJSSP的现有方法。对于大多数基准测试实例，我们基于lbbd的方法可以找到最佳的最大跨度值。由于时间装箱，只有在少数情况下，找到的解决方案的最优性不能得到保证。与文献中的启发式解决方案相比，我们基于lbbd的方法发现的解决方案在大约一半的基准测试实例中至少提高了10%，在最好的情况下提高了35%。

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引用次数: 0

A data-driven heuristic for the dynamic vehicle routing problem with multiple soft time windows 多软时间窗动态车辆路径问题的数据驱动启发式算法

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

Computers & Operations Research

Pub Date : 2025-11-24 DOI: 10.1016/j.cor.2025.107341

Slim Belhaiza , Gilbert Laporte

In the Dynamic Vehicle Routing Problem with Multiple Soft Time Windows (Dynamic VRPMSTW), customer requests arrive in real time and must be scheduled within flexible service intervals. This problem is complicated by operational constraints, such as vehicle capacities, travel durations, and heterogeneous fleets, which make it difficult for classical optimization methods to adapt quickly to changing conditions. Following recent trends in contextual optimization, we propose a Data-Driven Dynamic Heuristic that integrates Artificial Neural Networks for predicting travel times and demands into a Dynamic Hybrid Adaptive Large Neighborhood Search (DD-Dynamic HALNS). Using cluster assignment and genetic crossover operators, the method generates high-quality initial solutions and continuously re-optimizes them as new requests emerge, ensuring adaptability and service reliability. The effectiveness of the proposed method is evaluated on real-world logistics data and benchmark instances. Results from real-world delivery operations demonstrate an average distance reduction of 11.6% compared with the current solution, with further improvements up to 15.5% when a 10-minute time window flexibility is introduced. These findings highlight the practical benefits of integrating predictive analytics with heuristic optimization, leading to improved cost efficiency, reduced operational constraints, and enhanced service reliability.

在多软时间窗动态车辆路径问题（Dynamic VRPMSTW）中，客户请求是实时到达的，并且必须在灵活的服务间隔内进行调度。由于车辆容量、行驶时间和异构车队等操作约束，使得传统的优化方法难以快速适应不断变化的条件，从而使问题复杂化。根据上下文优化的最新趋势，我们提出了一种数据驱动的动态启发式算法，该算法将预测旅行时间和需求的人工神经网络集成到动态混合自适应大邻域搜索（DD-Dynamic HALNS）中。该方法利用聚类分配和遗传交叉算子，生成高质量的初始解，并随着新请求的出现不断进行再优化，保证了适应性和服务可靠性。在实际物流数据和基准实例上对该方法的有效性进行了评估。实际交付作业的结果表明，与目前的解决方案相比，平均距离减少了11.6%，当引入10分钟的时间窗口灵活性时，进一步提高了15.5%。这些发现突出了将预测分析与启发式优化相结合的实际好处，从而提高了成本效率，减少了操作限制，增强了服务可靠性。

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引用次数: 0

An optimization-based algorithm for fair and calibrated synthetic data generation 一种基于优化的公平和校准合成数据生成算法

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

Computers & Operations Research

Pub Date : 2025-11-20 DOI: 10.1016/j.cor.2025.107337

Jan Pablo Burgard , João Vitor Pamplona , Maria Eduarda Pinheiro

For agent based micro simulations, as used for example for epidemiological modeling during the COVID-19 pandemic, a realistic base population is crucial. Beyond demographic variables, health-related variables should also be included. In Germany, health-related surveys are typically small in scale, which presents several challenges when generating these variables. Specifically, strongly imbalanced classes and insufficient observations within sensitive groups necessitate the use of advanced synthetic data generation methods. To address these challenges, we present a method formulated as a mixed-integer linear optimization model designed to create health variables based on class probabilities. This model incorporates fairness by considering the class distribution across sensitive groups as constraints. Furthermore, we prove that the proposed model possesses unimodularity properties and present a preprocessing technique. This allows us to generate data for large populations, such as Germany’s population of over 80 million. Our numerical tests, using one of the largest German Health Survey (GEDA), demonstrate that our approach yields better classification results than a standard random forest when considering different ages as sensitive groups.

对于基于智能体的微模拟，例如用于COVID-19大流行期间的流行病学建模，真实的基础人口至关重要。除人口变量外，还应包括与健康有关的变量。在德国，与健康有关的调查通常规模较小，这在产生这些变量时带来了一些挑战。具体来说，在敏感群体中，严重不平衡的类别和不充分的观察需要使用先进的合成数据生成方法。为了应对这些挑战，我们提出了一种混合整数线性优化模型，旨在根据类别概率创建健康变量。该模型通过考虑跨敏感群体的类分布作为约束来结合公平性。进一步证明了该模型具有单模性，并提出了一种预处理技术。这使我们能够生成大量人口的数据，例如德国的8000多万人口。我们使用最大的德国健康调查（GEDA）之一进行的数值测试表明，在将不同年龄的人视为敏感群体时，我们的方法比标准随机森林产生更好的分类结果。

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引用次数: 0

Seru order acceptance and scheduling in the distributed production network considering preventive maintenance 考虑预防性维护的分布式生产网络中批量订单的接收与调度

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

Computers & Operations Research

Pub Date : 2025-11-19 DOI: 10.1016/j.cor.2025.107329

P. Hajipour, J. Behnamian

In today’s globalized manufacturing environment, producers encounter unprecedented challenges stemming from intense competition, volatile customer demands, and the imperative to deliver high-quality, customized products. Collaborative production within multi-factory networks has emerged as a vital strategy for optimizing workflows and increasing flexibility. Complementing this approach, seru systems—which segment production lines into smaller, autonomous units—enhance responsiveness to fluctuating demand patterns. However, coordinating and scheduling operations across such networks introduces significant complexity, posing a substantial challenge for production-optimization research. Moreover, preventive maintenance is essential for sustaining system productivity by minimizing unplanned downtime and extending equipment lifespan. This study examines scheduling and order acceptance in a distributed production network that integrates seru systems, requiring customer orders to be allocated among factories—some of which operate under seru configurations. A mixed‑integer nonlinear programming (MINLP) model is proposed and implemented in GAMS. For larger-scale instances, the study introduces two memetic algorithms, each integrating a distinct local-search strategy: one utilizing simulated annealing and the other applying hill-climbing. Comparative analysis against a genetic algorithm demonstrates the superior efficiency of both memetic approaches in solving complex instances. The findings offer significant practical implications, such as reduced operational costs, increased production flexibility, and accelerated order fulfillment. Results further demonstrate that integrating seru systems with preventive maintenance strategies in multi-factory networks enhances system stability and efficiency, supports customized manufacturing, and mitigates downtime.

在当今全球化的制造环境中，生产商面临着前所未有的挑战，这些挑战来自激烈的竞争、多变的客户需求以及提供高质量、定制化产品的必要性。多工厂网络中的协同生产已经成为优化工作流程和提高灵活性的重要策略。作为这种方法的补充，seru系统——将生产线分割成更小的、自主的单元——提高了对波动需求模式的响应能力。然而，在这样的网络中协调和调度作业带来了极大的复杂性，给生产优化研究带来了巨大的挑战。此外，预防性维护对于通过减少计划外停机时间和延长设备寿命来维持系统生产力至关重要。本研究考察了集成seru系统的分布式生产网络中的调度和订单接受情况，要求客户订单在工厂之间分配，其中一些工厂在seru配置下运行。提出了一种混合整数非线性规划（MINLP）模型并在GAMS中实现。对于更大规模的实例，该研究引入了两种模因算法，每种算法都集成了不同的局部搜索策略：一种利用模拟退火，另一种应用爬坡。与遗传算法的比较分析表明，这两种模因方法在求解复杂实例时都具有优越的效率。研究结果提供了重要的实际意义，如降低运营成本，提高生产灵活性，加快订单履行。结果进一步表明，在多工厂网络中，将seru系统与预防性维护策略集成可以提高系统的稳定性和效率，支持定制制造，并减少停机时间。

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引用次数: 0