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Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm

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Abstract

Recently, there is a growing interest in the industry to replace traditional straight assembly lines with U-shaped lines for more flexibility and higher productivity. Due to mathematical and computational complexity, assembly line balancing problems are known to be NP hard in nature. Therefore, many meta-heuristics have been proposed to find optimal solution for these problems. This paper presents a new hybrid evolutionary algorithm to solve stochastic U-type assembly line balancing problems, with the aim of minimizing the number of work stations, idle time at each station, and non-completion probabilities of each station (probability of the station time exceeding cycle time). The proposed algorithm is a combination of computer method for sequencing operations for assembly lines (COMSOAL), task assignment rules heuristic, and newly introduced imperialist competitive algorithm (ICA). Unlike the current evolutionary algorithms that are computer simulation of natural processes, ICA is inspired from socio-political evolution processes. Since appropriate design of the parameters has a significant impact on the algorithm efficiency, various parameters of the ICA are tuned by means of the Taguchi method. For the evaluation of the proposed hybrid algorithm, the performance of the proposed method is examined over benchmarks from the literature and compared with the best algorithm proposed before. Computational results demonstrate the efficiency and robustness of our algorithm.

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Authors and Affiliations

  1. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, GC, Tehran, Iran

    M. Bagher, M. Zandieh & H. Farsijani

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  1. M. Bagher
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  2. M. Zandieh
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  3. H. Farsijani
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Correspondence to M. Zandieh.

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Bagher, M., Zandieh, M. & Farsijani, H. Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm. Int J Adv Manuf Technol 54, 271–285 (2011). https://doi.org/10.1007/s00170-010-2937-3

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  • DOI: https://doi.org/10.1007/s00170-010-2937-3

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