Distributed flexible flow-shop scheduling problem with time-of-use electricity tariffs constraint and its solving algorithm

被引：0

作者：

Xu, Tianpeng ^{[1
]}

Zhao, Fuqing ^{[1
]}

Zhang, Jianlin ^{[1
]}

Wang, Weiyuan ^{[1
]}

Du, Songlin ^{[2
]}

机构：

[1] College of Computer and Communication, Lanzhou University of Technology, Lanzhou

[2] School of Mechatronic Engineering and Automation, Shanghai University, Shanghai

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2025年 / 31卷 / 04期

基金：

中国国家自然科学基金;

关键词：

distributed flexible flow-shop scheduling; learning mechanism; monarch butterfly optimization algorithm; multi-objective optimization; time-of-use electricity tariffs;

D O I：

10.13196/j.cims.2023.0026

中图分类号：

学科分类号：

摘要：

Energy costs and production efficiency are key factors in smart manufacturing. In order to reduce electricity costs while improving production efficiency, hy taking the Distributed Flexible Flow-shop Scheduling Problem (DFFSP) as the objective, the characteristics of DFFSP were analyzed. Considering the constraint of the Time-of-Use(TOU)electricity tariffs, an integer programming model of DFFSP-TOU was formulated with the objectives of minimizing makespan and total energy consumption. A Multi-objective Learning Monarch Butterfly Optimization algorithm (MOLMBO) based on self-learning mechanism was proposed according to the characteristics of DFFSP-TOU, in which the migration operator and adjusting operator were generated by the information of historical optimal solutions to enhance self-learning and the adaptive ability. Furthermore, the variable neighborhood search strategy was used to improve the performance of local search and enhance the diversity of population. In addition, the right-shift operation was applied to transfer the production from the peak times to reduce the energy consumption. The performance of the proposed algorithm was verified on benchmark problems, the experimental results showed that the MOLMBO was an effective method in addressing DFFSP. © 2025 CIMS. All rights reserved.

引用

页码：1299 / 1313

页数：14

共 32 条

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