Surrogate-assisted genetic programming for dynamic flexible job shop scheduling

Abstract

Genetic programming (GP) has been widely used for automatically evolving priority rules for solving job shop scheduling problems. However, one of the main drawbacks of GP is the intensive computation time. This paper aims at investigating appropriate surrogates for GP to reduce its computation time without sacrificing its performance in solving dynamic flexible job shop scheduling (DFJSS) problems. Firstly, adaptive surrogate strategy with dynamic fidelities of simulation models are proposed. Secondly, we come up with generation-range-based surrogate strategy in which homogeneous (heterogeneous) surrogates are used in same (different) ranges of generations. The results show that these two surrogate strategies with GP are efficient. The computation time are reduced by 22.9% to 27.2% and 32.6% to 36.0%, respectively. The test performance shows that the proposed approaches can obtain rules with at least the similar quality to the rules obtained by the GP approach without using surrogates. Moreover, GP with adaptive surrogates achieves significantly better performance in one out of six scenarios. This paper confirms the potential of using surrogates to solve DFJSS problems.