摘要
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.
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.
基金
the National Natural Science Foundation of China(No.70971017)
the Humanities and Social Sciences Project of Ministry of Education(No.10YJC630009)
the Social Science Fund of Zhejiang Province(No.10CGGL21YBQ)
the Natural Science Foundation of Zhejiang Province(No.Y1100854)
