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

Mousavi, M. and Yap, Hwa Jen and Musa, S.N. and Md Dawal, Siti Zawiah (2017) A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS. International Journal of Simulation Modelling, 16 (1). pp. 58-71. ISSN 1726-4529

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Official URL: http://dx.doi.org/10.2507/IJSIMM16(1)5.368

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.

Item Type: Article
Uncontrolled Keywords: Automated guided vehicle; Fuzzy hybrid GA-PSO; Genetic algorithm; Multi-objective optimization; Multi-objective optimization; Scheduling
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Aug 2017 01:57
Last Modified: 27 Feb 2019 01:25
URI: http://eprints.um.edu.my/id/eprint/17661

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