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 algo...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.展开更多
基金the National Natural Science Foundation of China(No.70971017)the Humanities and Social Sciences Project of Ministry of Education(No.10YJC630009)+1 种基金the Social Science Fund of Zhejiang Province(No.10CGGL21YBQ)the Natural Science Foundation of Zhejiang Province(No.Y1100854)
文摘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.
