J Shanghai Jiaotong Univ Sci

Journal of shanghai Jiaotong University (Science) ›› 2012, Vol. 17 ›› Issue (1): 39-044.doi: 10.1007/s12204-012-1227-7

• Articles • Previous Articles     Next Articles

Evolutionary Algorithms for Solving Unconstrained Multilevel Lot-Sizing Problem with Series Structure

Evolutionary Algorithms for Solving Unconstrained Multilevel Lot-Sizing Problem with Series Structure

HAN Yi (韩 毅), CAI Jian-hu (蔡建湖), IKOU Kaku, LI Yan-lai (李延来) CHEN Yi-zeng (陈以增), TANG Jia-fu (唐加福)   

  1. (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)
  2. (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)
  • Received:2010-07-02 Online:2012-02-29 Published:2012-03-21
  • Contact: CAI Jian-hu (蔡建湖), E-mail:caijh@zjut.edu.cn

Abstract: 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.

Key words: simulated annealing (SA)| genetic algorithm(GA)|particle swarm optimization (PSO)| multilevel lot-sizing Problem

摘要： 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.

关键词: simulated annealing (SA), genetic algorithm simulated annealing (SA)| genetic algorithm(GA)|particle swarm optimization (PSO)| multilevel lot-sizing Problem

CLC Number: