Optimizing flexible job shop scheduling with automated guided vehicles using a multi-strategy-driven genetic algorithm

The flexible job scheduling problem with automated guided vehicles (FJSP-AGVs) is a simplified model of some real manufacturing industries. It contains three strongly coupled subproblems: operation sequences assignment, machine selection, and automatic guided vehicle selection, leading to a huge solution space. Its several unresolved challenges, i.e., problem model and algorithmic designing, persist. Therefore, we first adopt the sequence-based modeling method to establish a mixed-integer linear programming model with makespan, and its correctness is verified by using the Gurobi solver. Subsequently, a multi-strategy-driven genetic algorithm (Mult_stra_GA) is proposed based on the implicit features of FJSP-AGVs. In Mult_stra_GA, for the operation sequence (OS) and the machine assignment (MS) subproblems, we design three targeted strategies, i.e., two layer-based encoding and decoding strategy, a multiple heuristics-based initialization strategy, double crossover, and dual mutation operators. Meanwhile, the problem-specific diversity checking and restart strategies are introduced to avoid Mult_stra_GA falling into local optima. Finally, we conduct experiments on four well-known benchmarks. Through the statistical analysis, the outcomes demonstrate that the Mult_stra_GA algorithm exhibits efficacy when contrasted with other advanced algorithms.