Fattahi, P., Samouei, P. (2016). A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers. Journal of Optimization in Industrial Engineering, 9(20), 9-18. doi: 10.22094/joie.2016.246
Parviz Fattahi; Parvaneh Samouei. "A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers". Journal of Optimization in Industrial Engineering, 9, 20, 2016, 9-18. doi: 10.22094/joie.2016.246
Fattahi, P., Samouei, P. (2016). 'A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers', Journal of Optimization in Industrial Engineering, 9(20), pp. 9-18. doi: 10.22094/joie.2016.246
Fattahi, P., Samouei, P. A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers. Journal of Optimization in Industrial Engineering, 2016; 9(20): 9-18. doi: 10.22094/joie.2016.246
A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers
This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm. This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm
Highlights
A multi-objective particle swarm optimization algorithm for worker assignment and mixed-model assembly line balancing problem is developed.
It is assumed that task times depend on the worker’s skill level.
The efficiency of the proposed algorithm is compared with multi-objective simulated annealing algorithm
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