Genetic Programming with Tabu List for Dynamic Flexible Job Shop Scheduling

Abstract Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem, requiring simultaneous decision-making for machine assignment and operation sequencing in dynamic environments. Genetic programming (GP), as a hyper-heuristic approach, has been extensively employed for acquiring scheduling heuristics for DFJSS. A drawback of GP for DFJSS is that GP has weak exploration ability indicated by its quick diversity loss during the evolutionary process. This paper proposes an effective GP algorithm with tabu lists to capture the information of explored areas and guide GP to explore more unexplored areas to improve GP’s exploration ability for enhancing GP’s effectiveness. First, we use phenotypic characterisation to represent the behaviour of tree-based GP individuals for DFJSS as vectors. Then, we build tabu lists that contain phenotypic characterisations of explored individuals at the current generation and across generations, respectively. Finally, newly generated offspring are compared with the individuals’ phenotypic characterisations in the built tabu lists. If an individual is unseen in the tabu lists, it will be kept to form the new population at the next generation. Otherwise, it will be discarded. We have examined the proposed GP algorithm in nine different scenarios. The findings indicate that the proposed algorithm outperforms the compared algorithms in the majority of scenarios. The proposed algorithm can maintain a diverse and well-distributed population during the evolutionary process of GP. Further analyses show that the proposed algorithm does cover a large search area to find effective scheduling heuristics by focusing on unseen individuals.