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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Reinforcement learning based co-evolutionary algorithm for solving flexible job shop energy efficient scheduling problem

Zhang Guohui1
Li Zhixiao1
Zhang Liping2
Zhang Wenqiang3
Yu Nana1
1. School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
2. Key Laboratory of Metallurgical Equipment & Control Technology, Wuhan University of Science & Technology, Wuhan 430081, China
3. School of Information Science & Engineering, Henan University of Technology, Zhengzhou 450001, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0479

Abstract

For the flexible job shop energy efficient scheduling problem (EEFJSP) , we construct a FJSP model with the optimization objectives of minimizing the maximum completion time and minimizing the total energy consumption. First, we propose an adaptive algorithm based on reinforcement learning co-evolutionary algorithm(QNSGA-II) to characterize the problem model; Second, we introduce the concepts of state space and action space and design a reward-punishment function based on the overall average fitness and population diversity to ensure the effectiveness of the algorithm in the iterative process; In order to improve the ability of the global search and local search, we propose an improved tabu search algorithm to update the population after crossover and mutation. In order to improve the ability of global search and local search, an improved taboo search algorithm is proposed to update the population after crossover and mutation. Finally, we analyze the effectiveness of the improved tabu search strategy and the Q-learning parameter adaptation strategy to verify the algorithm's effectiveness and superiority; and we compare the proposed QNSGA-II with other multi-objective optimization algorithms to verify the superiority of the algorithms in solving the EEFJSP.

Foundation Support

国家自然科学基金面上项目(52475524)、河南省重点研发专项(231111221200)、河南省科技攻关项目(242102221024)、教育部人文社会科学规划基金项目(23YJAZH193)、郑州航空工业管理学院研究生教育创新计划基金项目(2024CX16)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0479
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-12] Accepted Paper

Cite This Article

张国辉, 李志霄, 张利平, 等. 基于强化学习协同进化算法求解柔性作业车间节能调度问题 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0479. (Zhang Guohui, Li Zhixiao, Zhang Liping, et al. Reinforcement learning based co-evolutionary algorithm for solving flexible job shop energy efficient scheduling problem [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0479. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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