Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.
Keywords: Cellular manufacturing, bi-level programming, NBL-GA, NBL-PSO, workers’ interest
@article{RO_2021__55_S1_S167_0,
author = {Behnia, Bardia and Shirazi, Babak and Mahdavi, Iraj and Paydar, Mohammad Mahdi},
title = {Nested {Bi-level} metaheuristic algorithms for cellular manufacturing systems considering workers{\textquoteright} interest},
journal = {RAIRO. Operations Research},
pages = {S167--S194},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
doi = {10.1051/ro/2019075},
mrnumber = {4237377},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2019075/}
}
TY - JOUR AU - Behnia, Bardia AU - Shirazi, Babak AU - Mahdavi, Iraj AU - Paydar, Mohammad Mahdi TI - Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest JO - RAIRO. Operations Research PY - 2021 SP - S167 EP - S194 VL - 55 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2019075/ DO - 10.1051/ro/2019075 LA - en ID - RO_2021__55_S1_S167_0 ER -
%0 Journal Article %A Behnia, Bardia %A Shirazi, Babak %A Mahdavi, Iraj %A Paydar, Mohammad Mahdi %T Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest %J RAIRO. Operations Research %D 2021 %P S167-S194 %V 55 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2019075/ %R 10.1051/ro/2019075 %G en %F RO_2021__55_S1_S167_0
Behnia, Bardia; Shirazi, Babak; Mahdavi, Iraj; Paydar, Mohammad Mahdi. Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest. RAIRO. Operations Research, Tome 55 (2021), pp. S167-S194. doi: 10.1051/ro/2019075
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