Knowledge-driven co-evolutionary algorithm for flexible job shop scheduling problem with consistent sublots

Abstract

The flexible job shop scheduling problem with consistent sublots (FJSP-CS) is a typical lot streaming scheduling problem in the discrete production mode, which can effectively improve production efficiency by splitting appropriate sublots. In this paper, a knowledge-driven co-evolutionary algorithm (KDCA) is proposed to address FJSP-CS for minimizing makespan, based on a methodology combining heuristics and metaheuristics. Considering the characteristics of FJSP-CS, KDCA is designed based on genetic algorithm (GA) and gene expression programming (GEP), where GA is adopted to optimize the consistent lot-splitting problem, and GEP is adopted to evolve composite dispatching rules for addressing the scheduling problem. In KDCA, a hybrid initialization method based on chaotic mapping is designed to ensure the quality and diversity of the initial population. Then, a probabilistic model based on three quantifiable lot-splitting knowledge is established to guide the search process of KDCA. On this basis, knowledge-driven mutation and local search operators are designed to improve the local search capability. Additionally, a knowledge-driven catastrophe operator is developed to avoid premature convergence. Finally, numerical experiments based on widely used FJSP-CS instances are conducted to verify the effectiveness of the proposed operators and the superior performance of KDCA. Experimental results demonstrate that KDCA can obtain better solutions compared to several state-of-the-art algorithms in over 70% instances.