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CLC number: TP273

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2009-01-08

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.4 P.520-529

http://doi.org/10.1631/jzus.A0820284


An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem


Author(s):  Hassan REZAZADEH, Mehdi GHAZANFARI, Mohammad SAIDI-MEHRABAD, Seyed JAFAR SADJADI

Affiliation(s):  Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

Corresponding email(s):   hassan.rezazadeh@gmail.com

Key Words:  Dynamic facility layout problem (DFLP), Particle swarm optimization (PSO), Optimization, Heuristic method


Hassan REZAZADEH, Mehdi GHAZANFARI, Mohammad SAIDI-MEHRABAD, Seyed JAFAR SADJADI. An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem[J]. Journal of Zhejiang University Science A, 2009, 10(4): 520-529.

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author="Hassan REZAZADEH, Mehdi GHAZANFARI, Mohammad SAIDI-MEHRABAD, Seyed JAFAR SADJADI",
journal="Journal of Zhejiang University Science A",
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pages="520-529",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820284"
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%A Seyed JAFAR SADJADI
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A1 - Hassan REZAZADEH
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DOI - 10.1631/jzus.A0820284


Abstract: 
We extended an improved version of the discrete particle swarm optimization (DPSO) algorithm proposed by Liao et al.(2007) to solve the dynamic facility layout problem (DFLP). A computational study was performed with the existing heuristic algorithms, including the dynamic programming (DP), genetic algorithm (GA), simulated annealing (SA), hybrid ant system (HAS), hybrid simulated annealing (SA-EG), hybrid genetic algorithms (NLGA and CONGA). The proposed DPSO algorithm, SA, HAS, GA, DP, SA-EG, NLGA, and CONGA obtained the best solutions for 33, 24, 20, 10, 12, 20, 5, and 2 of the 48 problems from (Balakrishnan and Cheng, 2000), respectively. These results show that the DPSO is very effective in dealing with the DFLP. The extended DPSO also has very good computational efficiency when the problem size increases.

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