Optimization of the Distribution Plan and Multi-product Capacity using Genetic Algorithm

유전 알고리즘을 이용한 다 제품 생산용량 및 분배계획 최적화

  • Cha, Youngcheol (Graduate School of Consulting, Kumoh Nationa lInstitute of Technology) ;
  • Lee, Gapsoo (Graduate School of Consulting, Kumoh Nationa lInstitute of Technology) ;
  • Lee, Jonghwan (School of Industrial Engineering, KumohNationa lInstitute of Technology) ;
  • Wie, Do-Yeong (School of Industrial Engineering, KumohNationa lInstitute of Technology)
  • 차영철 (금오공과대학교 컨설팅대학원) ;
  • 이갑수 (금오공과대학교 컨설팅대학원) ;
  • 이종환 (금오공과대학교 산업공학부) ;
  • 위도영 (금오공과대학교 산업공학부)
  • Received : 2014.04.04
  • Accepted : 2014.06.20
  • Published : 2014.06.28


Supply Chain Management(SCM) is getting important, because size of the company is getting bigger and the kinds of product are various. In the case of manufacturing corporation, for the optimization of SCM, we have to make production and distribution plan by considering the various fluctuation in the aspect of integration. In this paper, first, It proposed the reasonable operational way of the SCM about when the customer's demanding is various and demanding expectation fluctuates in capacity standardization of producer stage. Second, the paper proposed the management way for demanding by considering confirmed demanding information, related inventory expense and demanding shortage expense when we make production and distribution plan. The paper applied the genetic algorithm proved for current usefulness. it proposed the optimal operational way for SCM by dividing into 2 ways for dealing with the duration of confirmed demanding information and various fluctuation.


Supply Chain Management;Genetic Algorithm;Optimization;Production Plan;Distribution Plan


  1. B. J. Jung, Y. H. Lee, G. T. Park, Supply Chain Management, SukJung, 2007.
  2. Y. H. Ji, S. J. Lim, K. S. Kim, "A Study on Determination of Factory Production Capacity in the Supply Chain Considering Uncertain Demand", Journal of the Korea Society for simulation, Vol. 12, No. 1, pp. 35-48, 2003.
  3. J. Lim, "Production planning and distribution planning using demand based mixed genetic algorithm in a supply chain", Seoul National University, 2003.
  4. S. W. Jung, Y. J. Jang, J. W. Park, "A Study on production and distribution planning problems using hybrid genetic algorithm", Journal of the Korean Operations Research and Management Science Society, Vol. 26, No. 4, pp. 133-141, 2001.
  5. S. J. Lim, S. J. Jung, K. S. Kim, M. W. Park, "A study on the production and distribution problem in a supply chain network using genetic algorithm", Journal of the Korea society for simulation, Vol. 12, No. 1, pp. 59-71, 2003.
  6. B. J, Park, H. R. Choi, M. H. Kang, "A Multi-agent System based on Genetic Algorithm for Integration Planning in a Supply Chain Management", Journal of Intelligence and Information System, Vol. 13, No. 3, pp. 47-61, 2007.
  7. G. H. Kim, J. B. Jo, C. S. Go, J. Y. Kim, Network model and Multi-Objective GA, HanSan, 2010.
  8. K. J. Park, "Multi-Objective Optimization of a Supply Chain Using NSGA-II", Journal of the Society of Korea Industrial and Systems Engineering, Vol. 2008, No. 1, pp. 264-270, 2008.