A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS
Document Type
Article
Publication Date
1-1-2017
Abstract
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. Multiobjective 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.
Keywords
Automated guided vehicle, Fuzzy hybrid GA-PSO, Genetic algorithm, Multi-objective optimization, Multi-objective optimization, Scheduling
Divisions
fac_eng
Funders
University of Malaya: UMRG Top Down Programme (Grant No. RP027-14AET),Ministry of Higher Education of Malaysia: High Impact Research Grant UM.C/HIR/MOHE/ENG/35 (D000035-16001)
Publication Title
International Journal of Simulation Modelling
Volume
16
Issue
1
Publisher
DAAAM International Vienna