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Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure

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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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Authors and Affiliations

  1. College of Economics and Management, Zhejiang University of Technology, Hangzhou, 310023, China

    Yi Han  (韩 毅) & Jian-hu Cai  (蔡建湖)

  2. Research Center for Technology Innovation and Enterprise Internationalization, Zhejiang University of Technology, Hongzhou, 310023, China

    Yi Han  (韩 毅)

  3. Department of Management Science and Engineering, Akita Prefectural University, Honjo, 015-0055, Japan

    Kaku Ikou

  4. School of Logistics, Southwest Jiaotong University, Chengdu, 610031, China

    Yan-lai Li  (李延来)

  5. College of International Business and Management, Shanghai University, Shanghai, 200444, China

    Yi-zeng Chen  (陈以增)

  6. Institute of Systems Engineering, Northeastern University, Shenyang, 110004, China

    Jia-fu Tang  (唐加福)

Authors
  1. Yi Han  (韩 毅)
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  2. Jian-hu Cai  (蔡建湖)
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  3. Kaku Ikou
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  4. Yan-lai Li  (李延来)
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  5. Yi-zeng Chen  (陈以增)
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  6. Jia-fu Tang  (唐加福)
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Corresponding author

Correspondence to Jian-hu Cai  (蔡建湖).

Additional information

Foundation item: 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), and the Natural Science Foundation of Zhejiang Province (No. Y1100854)

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Han, Y., Cai, Jh., Ikou, K. et al. Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure. J. Shanghai Jiaotong Univ. (Sci.) 17, 39–44 (2012). https://doi.org/10.1007/s12204-012-1227-7

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  • DOI: https://doi.org/10.1007/s12204-012-1227-7

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