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This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel
lot-sizing problem in material requirement planning (MRP) systems. Three evolutionary algorithms (simulated annealing (SA),
particle swarm optimization (PSO) and genetic algorithm (GA)) are provided. For evaluating the performances of algorithms,
the distribution of total cost (objective function) and the average computational time are compared. As a result, both GA
and PSO have better cost performances with lower average total costs and smaller standard deviations. When the scale of the
multilevel lot-sizing problem becomes larger, PSO is of a shorter computational time. 相似文献
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