Solving Line Balancing and Allocation Multi-Skilled Workers Problem on Parallel Assembly Line under Many-Objective

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ปารเมศ ชุติมา


A multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an evolutionary meta-heuristic which uses the concept of solving problems with many objectives simultaneously by classification problem into various subproblems. This paper applies MOEA/D algorithm in conjunction with the bisection method for solving line balancing and allocation problems of multi-skilled workers whom some are disable on parallel assembly lines under four objectives, i.e. minimize cycle time, minimize the number of stations, minimize different workload between workstations, and minimize index of task-unrelated. The experiment results show that MOEA/D obtains better performance than the Combinatorial Optimization with Coincidence (COIN) algorithm in terms of convergence which is the main concern of algorithm comparison. Although, its spread and CPU time are inferior to COIN.

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ชุติมา ป., “Solving Line Balancing and Allocation Multi-Skilled Workers Problem on Parallel Assembly Line under Many-Objective”, sej, vol. 13, no. 1, pp. 63–82, Aug. 2018.
Research Articles


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