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

• Main Authors: Li, Jiangpeng, Guo, Yao, Yao, Jiarong, Hu, Zhijian, Su, Rong, Han, Boon Siew, Wong, Hong Yee Alvin
• Format: Conference Proceeding
• Language: English
• Subjects: A algorithm; Benchmark testing; Conflict-free vehicle routing; Dispatching; Flexible manufacturing system; Genetic algorithm; Job shop scheduling; Metaheuristics; Particle swarm optimization; Processor scheduling; Remotely guided vehicles; Vehicle routing
• ISSN: 2326-8239
• DOI: 10.1109/CIS-RAM55796.2023.10370013

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.