Genetic Programming with Cluster Selection for Dynamic Flexible Job Shop Scheduling

Abstract

Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem, that aims to optimise machine resources for producing jobs to meet some goals. There are two important kinds of decisions that the scheduling process needs to make under dynamic environments, i.e., the routing decision for machine assignment and the sequencing decision for operation ordering. Genetic programming hyper-heuristic has been successfully applied for solving the dynamic flexible job shop scheduling problem with the advantage of automat-ically evolving good scheduling heuristics. Parent selection is an important process for genetic programming, intending to select good individuals as parents to generate offspring for the next generation. Traditional genetic programming methods select parents for crossover based on only fitness (e.g., tournament selection). In this paper, a new parent selection (i.e., cluster selection) method is proposed to select parents not only with good fitness but also with different behaviours. The proposed cluster selection is combined with genetic programming hyper-heuristic to study whether considering different behaviours in parent selection will improve the effectiveness of the evolved scheduling heuristics. The experimental results show that increasing the number of unique behaviours in the population cannot help evolve effective scheduling heuristics. Further analysis shows that considering behaviour to select parents does increase the number of unique behaviours in the population. However, it gives individuals with poor fitness more probability to be selected to generate offspring. This might be the reason why the proposed method cannot outperform the baseline method.