화학공학소재연구정보센터
Korean Chemical Engineering Research, Vol.56, No.6, 804-810, December, 2018
중소 바이오연료 기업의 물류 문제 해결을 위한 진화적 알고리즘 기반 배송 방법론
An Evolutionary Algorithm based Distribution Methodology for Small-scale Biofuel Energy Companies
E-mail:
초록
본 논문은 중소 바이오 연료 기업의 공급 사슬 망이 기존 화석연료업체와 경쟁하기 위해, 최소의 비용으로 수요에 대응할 수 있는 공급 계획을 수립할 수 있는방법론을 제안한다. 최소비용으로 공급하기 위해 수요처들 사이의 최적 경로와 공급량을 동시에 고려하여 공급 계획을 수립하였다. 이렇게 수립된 모델의 해를계산하기 위해 진화론적 방법을 이용하였다. 제안된 방법론을 이용할 때 중소기업의 어려움을 고려하여 과도한 투자비의 부담이 큰 상용 소프트웨어 대신 문제고유의 특성을 고려하여 최적 경로를 계산할 수 있다. 서울의 각 지역별로 바이오 연료를 공급하는 사례를 통해 제안된 방법을 수치적으로 설명하였다.
Most biofuel companies are in a small scale with short experience of operating the entire supply chain. In order to compete with existing fossil fuel competitors, renewable companies should be more responsive to demand. It is financially important to reflect this in the decision supporting system of the company. This paper addresses an evolutionary algorithm based methodology for the distribution problem of renewable energies. A numerical example was presented to illustrate the applicability of the proposed methodology with some remarks.
  1. IEA, “Technology Roadmap : Biofuels for Transport,” (2011).
  2. Kim JK, Ind. Eng. Chem, 16(2) (2013)
  3. Oh YT, Trans. Korean Soc. Mech. Eng. B, 22(4), 481 (1998)
  4. Kim SW, “An Analysis on the Actual Status of SMEs and Supply Chain in Renewable Energy Sectors,” KOSBI(2010).
  5. Park JY, “Operations and Supply Chain Management,” Aidbook(2012).
  6. Kang KH, Lee BK, Lee YH, JKIIE, 30(3), 224 (2004)
  7. Danzig GB, Ramser JH, MANAGE SCI, 6(1), 80 (1959)
  8. Schrijver A, Handbooks Oper. Res. Management Sci., 12, 1 (2005)
  9. Gilmore PC, Gomory RE, Oper. Res., 9(6), 849 (1961)
  10. Garey MR, Johnson DS, Computers and Intractability, San Francisco(1979).
  11. Lenstra JK, Rinnooy Kan AHG, Networks, 11(2), 221 (1981)
  12. Savelsbergh MWP, “Vehicle Routing and Computer Graphics,” CEMC, Amsterdam(1984).
  13. Bullnheimer B, Hartl RF, Strauss C, Ann. Oper. Res., 89, 319 (1999)
  14. Baker BM, Ayechew MA, Comput. Oper. Res., 30, 787 (2003)
  15. Gendreau M, Hertz A, Laporte G, Management Sci., 40(10), 1276 (1994)
  16. Gendreau M, Oper. Res., 40(3), 469 (1996)
  17. Magnanti TL, Networks, 11(2), 179 (1981)
  18. Laporte G, Eur. J. Oper. Res., 59(3), 345 (1992)
  19. Lenstra JK, Rinnooy Kan AHG, Networks, 11(2), 221 (1981)
  20. Clark G, Wright JW, Oper. Res., 12(4), 568 (1964)
  21. Reimann M, Doerner K, Hartl RF, Comput. Oper. Res., 31(4), 563 (2004)
  22. Goldberg D, Lingle R, “Alleles, Loci, and The Traveling Salesman Problem,” ICGA, 154-159(1985).
  23. Bullnheimer B, Hartl RF, Strauss C, “Applying the Ant System to the Vehicle Routing Problem,” Meta-Heuristics(1997).
  24. Donati, Alberto V, et al., Eur. J. Oper. Res., 185(3), 1174 (2008)
  25. Hong MD, Yu YH, Jo GS, “An Ant Colony Optimization Heuristic to solve the VRP with Time Window,” The KIPS Transcation partB 17B, 5(2010).
  26. Ko JT, Yu YH, Jo GS, J. Intell. Inform. Syst., 15(1), 1 (2009)
  27. Colorni A, Dorigo M, Maniezzo B, Distributed Optimization by Ant Colonies, 134-142(1991).
  28. Dorigo M, Gambardella LM, IEEE Trans. Evol. Comput., 1(1), 53 (1997)
  29. Bullnheimer B, Hartl RF, Strauss C, "Applying the Ant System to the Vehicle Routing Problem,” MIC97, 1-12(1997).
  30. Gambardella LM, Taillard E, Agazzi G, MACS-VRPTW, London, UK, 63-76(1999).
  31. Colorni A, Dorigo M, Maniezzo V, “Ant System for Job-shop Scheduling,” Belgian Journal of Operations Research, 1-14(1994).
  32. Garcia, Oscar C, Fernandez de Viana I, Herrera F, Mathware Soft Comput, 9(3), 177 (2002)
  33. Stutzle T, Hoos H, IEEE Conf. Evol. Comput. 308-313(1997).
  34. Gambardella LM, Taillard E, Agazzi G, MACS-VRPTW, London, UK, pp.63-76(1999).
  35. Geem ZW, Sim KB, Appl. Math Comput., 217(8), 3881 (2010)
  36. Johnson DS, Demers A, Ullman JD, Garey MR, Graham RL, Siam. J. Comput., 3(4), 299 (1974)
  37. Vester J, “A Tabu Search Heuristic for the Vehicle Routing Problem with Two-Dimensional Loading Constraints,” Erasmus University, Rotterdam(2015).