Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure |
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| Authors: | Yi Han Jian-hu Cai Kaku Ikou Yan-lai Li Yi-zeng Chen Jia-fu Tang |
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| Institution: | [1]College of Economics and Management, Zhejiang University of Technology, Hangzhou 310023, China [2]Research Center for Technology Innovation and Enterprise Internationalization, Zhejiang University of Technology, Hongzhou 310023, China [3]Department of Management Science and Engineering, Akita Prefectural University, Honjo 015-0055, Japan [4]School of Logistics, Southwest Jiaotong University, Chengdu 610031, China [5]College of International Business and Management, Shanghai University, Shanghai 200444, China [6]Institute of Systems Engineering, Northeastern University, Shenyang 110004, China |
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| Abstract: | 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. |
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