A Reinforcement Learning Driven Cooperative Meta-Heuristic Algorithm for Energy-Efficient Distributed No-Wait Flow-Shop Scheduling With Sequence-Dependent Setup Time
Fuqing Zhao, Tao Jiang, Ling Wang
Abstract
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.