A FUZZY HYBRID GA-PSO ALGORITHM FOR MULTI-OBJECTIVE AGV SCHEDULING IN FMS

作者：Mousavi M; Yap H J; Musa S N; Dawal S Z M

来源：International Journal of Simulation Modelling, 2017, 16(1): 58-71.

DOI：10.2507/IJSIMM16(1)5.368

• Automated Guided Vehicle
• Scheduling
• Multi-Objective Optimization
• Genetic Algorithm
• Particle Swarm Optimization
• Fuzzy Hybrid GA-PSO

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.

• 出版日期2017-3