A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

• Published in: International journal of simulation modelling Vol. 16; no. 1; pp. 58 - 71
• Main Authors: Mousavi, M., Yap, H. J., Musa, S. N.
• Format: Journal Article
• Language: English
• Published: 01.03.2017
• Subjects: Algorithms; Automated guided vehicles; Computer simulation; Fuzzy; Genetic algorithms; Mathematical models; Robots; Task scheduling
• ISSN: 1726-4529; 1996-8566; 1726-4529
• DOI: 10.2507/IJSIMM16(1)5.368

An automated guided vehicle (AGV) is a mobile robot with remarkable industrial applicability for transporting materials within a manufacturing facility or a warehouse. AGV scheduling refers to the process of allocating AGVs to tasks, taking into account the cost and time of operations. Multi-objective scheduling is adopted in this study to acquire a more complex and combinatorial model in contrast with single objective practices. The model objectives are the makespan and number of AGVs minimization while considering the AGVs battery charge. A fuzzy hybrid GA-PSO (genetic algorithm - particle swarm optimization) algorithm was developed to optimize the model. Results have been compared with GA, PSO, and hybrid GA-PSO algorithms to explore the applicability of the algorithm developed. Model's feasibility and the algorithms' performance were investigated through a numerical example before and after the optimization. The model evaluation and validation was conducted through simulation via Flexsim software. The fuzzy hybrid GA-PSO surpassed the other methods, although obtaining less mean computational time was the only significant improvement over hybrid GA-PSO. 35 refs.