A study of a matrix manufacturing system scheduling method considering equipment degradation

Fengque Pei; Hongwei Xiang; Cunbo Zhuang; Jianhua Liu; Jiaxuan Zhang; Chunguang Yang; Huihui Hao

doi:10.1016/j.eswa.2025.129363

A study of a matrix manufacturing system scheduling method considering equipment degradation

Fengque Pei
, Hongwei Xiang
, Cunbo Zhuang^*
, Jianhua Liu
, Jiaxuan Zhang
, Chunguang Yang
, Huihui Hao

^*此作品的通讯作者

机械与车辆学院

Hohai University Changzhou
Beijing Institute of Technology
Inner Mongolia First Machinery Group Co. Ltd.

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

Equipment reconfigurability and equipment degradation endow scheduling in Matrix Manufacturing System (MMS) with enhanced physical significance and more complex scheduling constraints. Equipment reconfigurability introduces setup time as the third temporal parameter in addition to traditional machining duration and logistics time, while equipment degradation-induced dynamic variability and real-time characteristics of real processing time supersede theoretical processing durations. This study innovatively addresses equipment degradation in MMS by proposing a Scheduling Problem in MMS with Real Processing Time (SPMMS-RPT). First, a mathematical model for SPMMS-RPT is constructed, comprehensively considering the impacts of equipment degradation and predictive maintenance (PM) on temporal parameters. Subsequently, a Double Deep Q Network (DDQN)-based solving algorithm designed, featuring a degradation-aware state space, a discrete scheduling rule-based action space, and a reward function incorporating virtual feedback. Experimental validation demonstrates the effectiveness of the proposed model and algorithm. Results indicate that the DDQN-based SPMMS-RPT solution achieves superior makespan and shorter computation time compared to conventional methods.

源语言	英语
文章编号	129363
期刊	Expert Systems with Applications
卷	297
DOI	https://doi.org/10.1016/j.eswa.2025.129363
出版状态	已出版 - 1 2月 2026

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title = "A study of a matrix manufacturing system scheduling method considering equipment degradation",

abstract = "Equipment reconfigurability and equipment degradation endow scheduling in Matrix Manufacturing System (MMS) with enhanced physical significance and more complex scheduling constraints. Equipment reconfigurability introduces setup time as the third temporal parameter in addition to traditional machining duration and logistics time, while equipment degradation-induced dynamic variability and real-time characteristics of real processing time supersede theoretical processing durations. This study innovatively addresses equipment degradation in MMS by proposing a Scheduling Problem in MMS with Real Processing Time (SPMMS-RPT). First, a mathematical model for SPMMS-RPT is constructed, comprehensively considering the impacts of equipment degradation and predictive maintenance (PM) on temporal parameters. Subsequently, a Double Deep Q Network (DDQN)-based solving algorithm designed, featuring a degradation-aware state space, a discrete scheduling rule-based action space, and a reward function incorporating virtual feedback. Experimental validation demonstrates the effectiveness of the proposed model and algorithm. Results indicate that the DDQN-based SPMMS-RPT solution achieves superior makespan and shorter computation time compared to conventional methods.",

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author = "Fengque Pei and Hongwei Xiang and Cunbo Zhuang and Jianhua Liu and Jiaxuan Zhang and Chunguang Yang and Huihui Hao",

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T1 - A study of a matrix manufacturing system scheduling method considering equipment degradation

AU - Pei, Fengque

AU - Xiang, Hongwei

AU - Zhuang, Cunbo

AU - Liu, Jianhua

AU - Zhang, Jiaxuan

AU - Yang, Chunguang

AU - Hao, Huihui

PY - 2026/2/1

Y1 - 2026/2/1

N2 - Equipment reconfigurability and equipment degradation endow scheduling in Matrix Manufacturing System (MMS) with enhanced physical significance and more complex scheduling constraints. Equipment reconfigurability introduces setup time as the third temporal parameter in addition to traditional machining duration and logistics time, while equipment degradation-induced dynamic variability and real-time characteristics of real processing time supersede theoretical processing durations. This study innovatively addresses equipment degradation in MMS by proposing a Scheduling Problem in MMS with Real Processing Time (SPMMS-RPT). First, a mathematical model for SPMMS-RPT is constructed, comprehensively considering the impacts of equipment degradation and predictive maintenance (PM) on temporal parameters. Subsequently, a Double Deep Q Network (DDQN)-based solving algorithm designed, featuring a degradation-aware state space, a discrete scheduling rule-based action space, and a reward function incorporating virtual feedback. Experimental validation demonstrates the effectiveness of the proposed model and algorithm. Results indicate that the DDQN-based SPMMS-RPT solution achieves superior makespan and shorter computation time compared to conventional methods.

AB - Equipment reconfigurability and equipment degradation endow scheduling in Matrix Manufacturing System (MMS) with enhanced physical significance and more complex scheduling constraints. Equipment reconfigurability introduces setup time as the third temporal parameter in addition to traditional machining duration and logistics time, while equipment degradation-induced dynamic variability and real-time characteristics of real processing time supersede theoretical processing durations. This study innovatively addresses equipment degradation in MMS by proposing a Scheduling Problem in MMS with Real Processing Time (SPMMS-RPT). First, a mathematical model for SPMMS-RPT is constructed, comprehensively considering the impacts of equipment degradation and predictive maintenance (PM) on temporal parameters. Subsequently, a Double Deep Q Network (DDQN)-based solving algorithm designed, featuring a degradation-aware state space, a discrete scheduling rule-based action space, and a reward function incorporating virtual feedback. Experimental validation demonstrates the effectiveness of the proposed model and algorithm. Results indicate that the DDQN-based SPMMS-RPT solution achieves superior makespan and shorter computation time compared to conventional methods.

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KW - Predictive Maintenance

KW - Real Processing Time

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JO - Expert Systems with Applications

JF - Expert Systems with Applications

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A study of a matrix manufacturing system scheduling method considering equipment degradation

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