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

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.