Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

Mousavi, M. and Yap, Hwa Jen and Musa, S.N. and Tahriri, F. and Md Dawal, Siti Zawiah (2017) Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLoS ONE, 12 (3). e0169817. ISSN 1932-6203, DOI

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Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Item Type: Article
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)
Uncontrolled Keywords: Algorithms; Commerce; Computer Simulation; Models, Theoretical
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 04 Sep 2018 05:05
Last Modified: 27 Feb 2019 01:24

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