Improved Q-learning Algorithm to Solve the Permutation Flow Shop Scheduling Problem

Authors

  • Zifeng Xuan
  • Yunfei Liu
  • Xinxin Peng

DOI:

https://doi.org/10.62051/ijmee.v2n3.07

Keywords:

Q-learning Algorithm, Boltzmann Action Exploration Strategy, Permutation Flow Shop

Abstract

A modified Q-learning algorithm is proposed for the permutation flow shop scheduling problem. This algorithm initializes the environment with the job sequence and considers each processable job as an executable action. A reward function is defined as the reciprocal of the completion time. Moreover, the completion time is calculated using the principle of diagonalization of a two-dimensional matrix, significantly enhancing computational efficiency. The Boltzmann action exploration strategy is designed, where the probability of selecting an action decreases as the temperature coefficient T decreases, and the probability of randomly selecting an action decreases, favoring the selection of actions corresponding to larger Q values. Finally, the performance of the proposed algorithm is validated using instances of permutation flow shop scheduling problems of different scales. By comparing the results with standard instances and other algorithms, the accuracy of the algorithm is demonstrated.

References

WANG L, PAN Z and Wang J. " A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling," in Complex System Modeling and Simulation, vol. 1, no. 4, pp. 257-270, December 2021.

Li XinYu, Huang JiangPin, Li JiangHang. Research and Development Trend Analysis of Dynamic Scheduling in Intelligent Workshops[J]. Science in China: Technical Sciences, 2023,53(07):1016-1030.

Lu Y, Jiang T. Bi-population based discrete bat algorithm for the low-carbon job shop scheduling problem [J]. IEEE Access, 2019, 7: 14513-14522.

Dai M, Zhang Z, Giret A, et al. An enhanced estimation of distribution algorithm for energy-efficient job-shop scheduling problems with transportation constraints[J]. Sustainability, 2019, 11(11): 3085.

Qin Xuan, Fang ZiHan, Zhang ZhaoXin. Solving Permutation Flow Shop Scheduling Problem using Hybrid Symbiotic Organisms Search Algorithm [J]. Journal of Zhejiang University (Engineering Science Edition),2020,54(04):712-721.

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Published

21-05-2024

Issue

Section

Articles

How to Cite

Xuan, Z., Liu, Y., & Peng, X. (2024). Improved Q-learning Algorithm to Solve the Permutation Flow Shop Scheduling Problem. International Journal of Mechanical and Electrical Engineering, 2(3), 63-68. https://doi.org/10.62051/ijmee.v2n3.07

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