Journal of Manufacturing Systems最新文献

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IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-01-01

引用次数: 0

A hybrid multiobjective particle swarm optimization with Q-learning-driven local search for distributed heterogeneous hybrid flow-shop scheduling problem with worker–machine–environment collaboration 基于q -学习驱动局部搜索的混合多目标粒子群算法求解工人-机器-环境协同的分布式异构混合流水车间调度问题

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-12-31 DOI: 10.1016/j.jmsy.2025.12.024

Wenqiang Zhang , Sen Yang , Mingzhe Li , Yashuang Mu , Guohui Zhang , Mitsuo Gen , Peng Li

The distributed heterogeneous hybrid flow-shop scheduling problem (DHHFSP) involves geographically dispersed factories, heterogeneous machines, and skilled workers, leading to complex multiobjective scheduling challenges. Existing studies usually ignore the critical role of workers in distributed manufacturing, and conventional multiobjective optimization algorithms struggle to balance convergence and solution diversity. To address these gaps, this paper develops a worker–machine–environment collaborative model for DHHFSP that simultaneously minimizes makespan, total energy consumption, and total worker cost, and proposes a hybrid multiobjective particle swarm optimization algorithm with Q-learning-driven local search (HMOPSO-QLS). The proposed approach features a hybrid framework that combines multidirectional particle swarm global search with reinforcement learning-based local search, a Pareto front oriented four directional swarm decomposition and update mechanism where three boundary exploration sub-swarms and one central exploration sub-swarm cooperatively guide particle evolution, and a two level local search scheme that integrates domain knowledge based inter-factory load balancing with Q-learning based adaptive intra-factory variable neighborhood search. Comprehensive experiments demonstrate that HMOPSO-QLS significantly outperforms classical multiobjective optimization algorithms in solving DHHFSP in terms of convergence and solution diversity. In distributed human–machine collaborative manufacturing, the proposed framework and algorithm support more effective configuration of factories, machines, and workers, providing robust schedules that are directly applicable to practical production decision-making and cross-factory coordination.

分布式异构混合流水车间调度问题（DHHFSP）涉及地理上分散的工厂、异构机器和熟练工人，导致复杂的多目标调度挑战。现有的研究往往忽略了工人在分布式制造中的关键作用，传统的多目标优化算法难以平衡收敛性和解的多样性。为了解决这些问题，本文开发了DHHFSP的工人-机器-环境协同模型，同时最小化完工时间、总能耗和总工人成本，并提出了一种基于q -学习驱动的局部搜索（hmpso - qls）的混合多目标粒子群优化算法。该方法采用多向粒子群全局搜索与基于强化学习的局部搜索相结合的混合框架，采用三个边界探索子群与一个中心探索子群协同引导粒子进化的Pareto前沿四向群体分解与更新机制；结合基于领域知识的工厂间负载均衡和基于q学习的自适应工厂内变量邻域搜索的两级局部搜索方案。综合实验表明，hmpso - qls在求解DHHFSP的收敛性和解的多样性方面明显优于经典多目标优化算法。在分布式人机协同制造中，提出的框架和算法支持更有效的工厂、机器和工人配置，提供直接适用于实际生产决策和跨工厂协调的鲁棒调度。

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A knowledge-enhanced discrete artificial bee colony algorithm for flexible job shop scheduling problem with transport robots 运输机器人柔性作业车间调度问题的知识增强离散人工蜂群算法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-12-30 DOI: 10.1016/j.jmsy.2025.12.023

Youjie Yao , Kai Ye , Chunjiang Zhang , Xinyu Li , Liang Gao

The flexible job shop scheduling problem with transport robots (FJSPTR) has garnered significant attention due to the widespread integration of transport robots in manufacturing systems. Owing to its dual-resource scheduling characteristics, metaheuristic algorithms have become the dominant approach for solving the FJSPTR, as they can effectively handle its inherent complexity. However, existing algorithms often lack domain-specific knowledge, leading to suboptimal solution quality and limited robustness. To address these challenges, this study proposes a knowledge-enhanced discrete artificial bee colony (KEDABC) algorithm aimed at minimizing makespan. First, three tailored neighborhood structures are designed, and two feasibility theorems are introduced to ensure the validity of the generated neighborhood solutions. Second, these neighborhood structures are integrated with tabu search to form a knowledge-driven search approach, enabling a more comprehensive and detailed search of the neighborhoods. Third, to integrate domain knowledge into the discrete artificial bee colony algorithm, a novel task-based solution representation is proposed, which provides a complete mapping from the encoding to the solution space. In addition, a machine assignment-based local search strategy is developed to further improve algorithm performance. Finally, extensive experiments are conducted to benchmark the proposed algorithm against state-of-the-art methods using two benchmark datasets. The comparison results demonstrate that the KEDABC algorithm consistently achieves optimal or near-optimal solutions, outperforming other methods in both solution quality and computational efficiency.

由于运输机器人在制造系统中的广泛集成，运输机器人柔性作业车间调度问题（FJSPTR）受到了广泛关注。由于FJSPTR的双资源调度特性，元启发式算法能够有效地处理其固有的复杂性，已成为解决该问题的主要方法。然而，现有的算法往往缺乏特定领域的知识，导致求解质量不佳，鲁棒性有限。为了解决这些挑战，本研究提出了一种知识增强离散人工蜂群（KEDABC）算法，旨在最小化最大完工时间。首先，设计了三种定制的邻域结构，并引入了两个可行性定理来保证生成的邻域解的有效性。其次，将这些邻域结构与禁忌搜索相结合，形成知识驱动搜索方法，对邻域进行更全面、更细致的搜索。第三，为了将领域知识整合到离散人工蜂群算法中，提出了一种新的基于任务的解表示，提供了从编码到解空间的完整映射。此外，为了进一步提高算法性能，提出了一种基于机器分配的局部搜索策略。最后，使用两个基准数据集对所提出的算法进行了广泛的实验，以比较最先进的方法。对比结果表明，KEDABC算法始终能得到最优或近最优解，在解质量和计算效率方面都优于其他方法。

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引用次数: 0

Small-sample machining quality prediction via a fuzzy broad learning system enhanced by prior knowledge 基于先验知识增强模糊广义学习系统的小样本加工质量预测

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-12-29 DOI: 10.1016/j.jmsy.2025.12.021

Zewen Hu , Yu Shen , Shuyue Zhang , Hongcai Chen , Kanjian Zhang , Haikun Wei

Surface roughness is a critical indicator of machined workpiece quality, and accurately modeling its relationship with process parameters is essential for process optimization and intelligent decision-making. Fuzzy broad learning system (FBLS) has demonstrated considerable advantages in nonlinear predictive modeling; however, its performance under small-sample conditions may be limited due to an incomplete rule base and the lack of explicit physical mechanisms. To address this challenge, this article proposes a knowledge-enhanced fuzzy broad learning system (KEFBLS) that integrates dual sources of prior knowledge — expert-knowledge-guided fuzzy partition and physics-based fuzzy rule consequents — to improve predictive accuracy and generalization ability. The effectiveness of KEFBLS is validated on both real-world robotic grinding experiments and a publicly available machining dataset, achieving average prediction errors of only 10.3% and 4.7%, respectively, representing over 20% accuracy improvement over the FBLS baseline. These results highlight the significance of combining domain-specific prior knowledge with data-driven learning, enabling robust performance under limited-data conditions. Overall, KEFBLS provides a unified knowledge- and data-driven framework for surface roughness prediction, with potential applicability to other manufacturing processes where labeled data are scarce.

表面粗糙度是加工工件质量的重要指标，准确建模其与工艺参数的关系对工艺优化和智能决策至关重要。模糊广义学习系统（FBLS）在非线性预测建模方面显示出相当大的优势；然而，由于不完整的规则库和缺乏明确的物理机制，其在小样本条件下的性能可能受到限制。为了解决这一挑战，本文提出了一种知识增强模糊广义学习系统（KEFBLS），该系统集成了先验知识的双重来源-专家知识引导的模糊划分和基于物理的模糊规则结果-以提高预测精度和泛化能力。KEFBLS的有效性在实际机器人磨削实验和公开可用的加工数据集上得到了验证，平均预测误差分别仅为10.3%和4.7%，比FBLS基线精度提高了20%以上。这些结果强调了将特定领域的先验知识与数据驱动学习相结合的重要性，从而在有限数据条件下实现稳健的性能。总体而言，KEFBLS为表面粗糙度预测提供了统一的知识和数据驱动框架，具有潜在的适用性，可用于标记数据稀缺的其他制造过程。

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引用次数: 0

A spatio-temporal parallel ensemble learning approach for operation situation prediction in discrete manufacturing workshop 离散制造车间运行态势预测的时空并行集成学习方法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-12-27 DOI: 10.1016/j.jmsy.2025.12.022

Sai Geng , Yu Guo , Weiwei Qian , Weiguang Fang , Shengbo Wang , Shaohua Huang , Xiaoyu Hou

In the complex and dynamic discrete manufacturing environment, accurate prediction of the workshop operation situation (WOS) is crucial to ensure on-time delivery of orders. However, the spatio-temporal (ST) coupling characteristics of the manufacturing process, dynamic fluctuations of workshop performance, and varying contributions of samples to the prediction model make WOS prediction more challenging. To address these issues, this paper proposes a ST parallel ensemble learning approach for WOS prediction. Specifically, based on workshop production data, a temporal data model and a dynamic graph model are constructed to comprehensively characterize the ST characteristics of the production process. Subsequently, this paper proposes a ST parallel ensemble learning method, named Adaboost-GLT, which integrates three ST weak learners (GCN, LSTM, and TGCN) to effectively capture the ST characteristics. Furthermore, a dynamic optimal selection mechanism is designed to adaptively select the best-performing weak learner at each stage, enabling the prediction method to evolve synchronously with the dynamic changes of the manufacturing process. Additionally, a sample weight updating strategy that takes into account sample timeliness and prediction error is introduced to improve the rationality of Adaboost-GLT's attention allocation to samples during training. Finally, the performance of Adaboost-GLT is experimentally validated on real workshop production datasets. The experimental results show that Adaboost-GLT can fully exploit the ST characteristics, effectively cope with the dynamic fluctuations of workshop performance, and thereby achieve high-precision prediction of WOS.

在复杂动态的离散制造环境下，准确预测车间运行状况是保证订单准时交货的关键。然而，制造过程的时空耦合特征、车间绩效的动态波动以及样本对预测模型的不同贡献使得WOS预测更具挑战性。为了解决这些问题，本文提出了一种用于WOS预测的ST并行集成学习方法。具体而言，以车间生产数据为基础，构建时序数据模型和动态图形模型，全面表征生产过程的ST特征。随后，本文提出了一种ST并行集成学习方法Adaboost-GLT，该方法集成了三个ST弱学习器（GCN、LSTM和TGCN）来有效捕获ST特征。此外，设计了动态最优选择机制，在每个阶段自适应地选择表现最好的弱学习者，使预测方法能够与制造过程的动态变化同步进化。此外，引入了考虑样本时效性和预测误差的样本权值更新策略，提高了Adaboost-GLT在训练过程中对样本的关注分配的合理性。最后，在实际车间生产数据集上对Adaboost-GLT的性能进行了实验验证。实验结果表明，Adaboost-GLT能够充分利用ST特性，有效应对车间性能的动态波动，从而实现对WOS的高精度预测。

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