alexa Integration of Computer Simulation, Design of Experiments and Particle Swarm Optimization to Optimize the Production Line Efficiency
ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
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Integration of Computer Simulation, Design of Experiments and Particle Swarm Optimization to Optimize the Production Line Efficiency

Morteza Khalaji Assadi1*, Seyed Mojib Zahraee1 and Jalal Taghdisi2

1Mechanical Engineering Department, Universiti Teknologi Petronas (UTP), Bandar Seri Iskandar, Malaysia

2Department of Management, Islamic Azad University, Mashhad, Razavi Khorasan, Iran>

*Corresponding Author:
Morteza Khalaji Assadi
Mechanical Engineering Department
Universiti Teknologi PETRONAS (UTP)
Bandar Seri Iskandar, Malaysia
Tel: 605 368 7202
E-mail: [email protected]

Received April 25, 2016; Accepted April 27, 2016; Published April 30, 2016

Citation: Assadi MK, Zahraee SM, Taghdisi J (2016) Integration of Computer Simulation, Design of Experiments and Particle Swarm Optimization to Optimize the Production Line Efficiency. Int J Swarm Intel Evol Comput 5: 136. doi: 10.4172/2090-4908.1000136

Copyright: © 2016 Assadi MK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



The goal of this paper is to optimize the productivity of manufacturing system by integrating computer simulation, design of experiments (DOE) and particle swarm optimization (PSO) algorithm. Optimizing productivity of colour factory was considered as the case of this study. To evaluate and estimate the effect of main factors, 2n factorial design with higher and upper levels and centre points was considered. After obtaining the significant factors, the local optimum setting of the significant factors was determined using the steepest ascent method and response surface methodology (RSM) approach. Finally, the global optimum productivity was achieved by computer programing of PSO method. Base on the final result, maximum productivity occurs in the point of 87.23 that is relevant to number of labour (B) = 26 and failure time of lifter (C) = 78.04 min. In addition, other two factors A (Service rate of Delpak mixer) and D (Number of permil) should be located at low level to obtain maximum productivity.


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