A shuffled frog-leaping algorithm with Q-learning for unrelated parallel machine scheduling with additional resource and learning effect

Article type: Research Article

Authors: Yi, Tian | Li, Mingbo^{; *} | Lei, Deming

Affiliations: School of Automation, Wuhan University of Technology, Wuhan, China

Correspondence: [*] Corresponding author. Mingbo Li, School of Automation, Wuhan University of Technology, Wuhan 430070, China. E-mail: feng10111217@whut.edu.cn.

Abstract: Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP.

Keywords: parallel machine scheduling, additional resource, learning effect, shuffled frog-leaping algorithm, reinforcement learning

DOI: 10.3233/JIFS-213473

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5357-5375, 2023