作业车间调度
计算机科学
数学优化
流水车间调度
高效能源利用
能源消耗
调度(生产过程)
强化学习
算法
能量最小化
整数规划
启发式
分布式计算
工程类
人工智能
数学
地铁列车时刻表
操作系统
电气工程
计算化学
化学
作者
Fuqing Zhao,Tao Jiang,Ling Wang
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:19 (7): 8427-8440
被引量:45
标识
DOI:10.1109/tii.2022.3218645
摘要
Green manufacturing has attracted increasing attention under the background of carbon peaking and carbon neutrality. Distributed production has widely existed in various manufacturing industries with the development of globalization. This article investigates an energy-efficient distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (DNWFSP-SDST) to minimization of makespan and total energy consumption (TEC). A mixed-integer linear programming model of energy-efficient DNWFSP-SDST is constructed and a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) is proposed to address energy-efficient DNWFSP-SDST in this article. In CMAQ, a heuristic named RNRa is proposed to generate initial solutions. A bipopulation cooperative framework based on double Q-learning is designed to further optimize the solutions. According to the properties of energy-efficient DNWFSP-SDST, an energy-saving strategy based on knowledge is proposed to improve makespan and TEC. The results of experiments show that the performance of CMAQ is superior to certain state-of-the-art comparison algorithms in solving energy-efficient DNWFSP-SDST.
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