Hybrid metaheuristics for integrated dispatching and conflict-free vehicle routing of automated guided vehicles in flexible manufacturing systems

Automated guided vehicles (AGVs) are pervasively used for transportation in flexible manufacturing systems (FMSs), in which production scheduling, vehicle dispatching, and conflict-free vehicle routing (CVR) are three pivot sub-problems. Even though these sub-problems are interrelated, they are often solved sequentially due to their complexity. By combining the particle swarm optimization (PSO), genetic algorithm (GA), and A* algorithm, this paper proposed a metaheuristic (HPSO-GA) that can solve these three sub-problems simultaneously. A hybridization method, specially designed for FJSPCVR, can let PSO compensate for GA’s weakness and vice versa by using their complementary strengths. A modified A* algorithm based on time windows is embedded in the HPSO-GA to find the shortest route on the time domain and resolve conflicts along the path. The proposed HPSO-GA is implemented on a new benchmark with a more complex roadmap and larger manufacturing job scale compared to a former benchmark used in flexible job shop problems. Simulation results indicate that HPSO-GA is applicable for solving integrated scheduling problems in FMSs from an engineering perspective.