A Reinforcement Learning and The Northern Goshawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Authors

  • Changshun Shao College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China | Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China
  • Zhenglin Yu College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China | Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China
  • Han Hou Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China
  • Hongchang Ding College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China | Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China
  • Guohua Cao College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China | Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China
  • Bin Zhou College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China | Chongqing Research Institute, Changchun University of Science and Technology, Chongqing, China

DOI:

https://doi.org/10.23055/ijietap.2025.32.1.10505

Abstract

This paper introduces northern goshawk optimization, a novel global search strategy for the flexible job shop scheduling problem. It uses two-stage encoding and random-key-based encoding to transform individual position vectors into flexible job shop scheduling problem solutions. To improve local search, reinforcement learning is integrated, converting the flexible job shop scheduling problem into a Markov decision process with 10 states and 6 rules. A reward function based on optimal completion time guides the search. The proposed hybrid northern goshawk optimization-Q-learning-state-action-reward-state-action framework combines global and local search strengths. Experiments on standard datasets show the algorithm's superior performance, validating its effectiveness and practicality in solving the flexible job shop scheduling problem and real-world production scheduling problems.

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Published

2025-01-31

How to Cite

Shao, C., Yu, Z., Hou, H., Ding, H., Cao, G., & Zhou, B. (2025). A Reinforcement Learning and The Northern Goshawk Optimization Algorithm for Flexible Job Shop Scheduling Problem. International Journal of Industrial Engineering: Theory, Applications and Practice, 32(1). https://doi.org/10.23055/ijietap.2025.32.1.10505

Issue

Vol. 32 No. 1 (2025)

Section

Production Planning and Control

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