한국시뮬레이션학회논문지 (Journal of the Korea Society for Simulation)

한국시뮬레이션학회 (The Korea Society for Simulation)
권호 표지
DOI QR코드

DOI QR Code

자동차 선적 및 납기를 위한 동적 최적화

A Dynamic Optimization for Automotive Vehicle Shipment and Delivery

  • Yee, John (Department of the Treasury, Sungkyunkwan University)
  • 투고 : 2014.11.14
  • 심사 : 2014.12.16
  • 발행 : 2014.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

완성차의 빠르고 안전한 납기를 위해 자동차 업계는 많은 노력을 기울여 왔다. 생산 후 선적까지 완성차가 조립공장내에 체재하는 시간의 감축을 통해 총 주문시간을 줄일 수 있음과 동시에 총수송비용 또한 줄일 수 있다. 전통적인 선적계획법들은 조립공장내에서 발생하는 동적인 사건들을 다루는데 한계가 있다. 본 논문은 이러한 선적과정중에 발생할 수 있는 동적 사건들을 해결하는 다중 에이전트 기반 선적 방법을 제시하고 있다. 시뮬레이션을 이용한 실험 결과를 통해 이러한 방법이 주문시간, 작업효율, 품질 및 수송효율의 증진을 가져올 수 있음을 보였다.

The automotive industry has made much efforts to deliver finished vehicles to customers with speed and reliability. Decreasing the time a vehicle stays within an assembly plant from production release to shipment contributes to reduce the total order lead-time and consequently, the total transportation cost as well. Conventional shipment planning algorithms are limited in accommodating the dynamics of assembly plant operations as to finished vehicle shipment. This paper presents a market-based multi-agent shipment planning algorithm to optimize the performance of vehicle shipment process, capturing the operationally disruptive events. Experimental results using simulation show that the algorithm improves vehicle shipment performance with respect to lead time, labor efficiency, finished product quality, and transportation efficiency.

키워드

참고문헌

  1. Costy, T., Truss, L., and Tew, J., "Integrated supply chain management exploratory project mid-term report", GM Research Report ESL-44, 2000.
  2. Cormen, T., Leiserson, C., Rivest, R., and Stein, C., Introduction to Algorithms (Second Edition). The MIT Press, 2001.
  3. Kim, J. D., Tang, K., Kumara, S., Yee, S. T., and Tew, J., "Value analysis of location-enabled radio frequency identification information on delivery chain performance", International Journal of Production Economics, Vol. 112, No. 1, pp. 403-415, 2007.
  4. Schieber, B., Bar-Noy, A., Bhatia, R., and Naor, J., "Minimizing service and operation costs of periodic scheduling", Math. Oper. Res., Vol. 27, pp. 518-544, 2002. https://doi.org/10.1287/moor.27.3.518.314
  5. Vaserstein, L. N. and Byrne, C. C., Introduction To Linear Programming. Prentice Hall/Pearson Education, 2003.
  6. Weiss, G., Multiagent Systems - A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, Massachusetts; London, England, 1999.
  7. Wooldridge, M. J., An Introduction to Multi-Agent Systems. Chichester, UK: Wiley, 2002.
  8. Hong, Y., Chen, G., and Bushnell, L., "Distributed observers design for leader-following control of multi-agent networks", Automatica, Vol. 44, No. 3, pp. 846-850, 2008. https://doi.org/10.1016/j.automatica.2007.07.004
  9. Nedic, A. and Ozdaglar, A., "Distributed subgradient methods for multi-agent optimization", IEEE Transactions on Automatic Control, Vol. 54, No. 1, pp.48-61, 2009. https://doi.org/10.1109/TAC.2008.2009515
  10. Nedic, A., Ozdaglar, A., and Parrilo, P.A., "Constrained consensus and optimization in multi-agent networks", IEEE Transactions on Automatic Control, Vol. 55, No. 4, pp. 922-938, 2010. https://doi.org/10.1109/TAC.2010.2041686
  11. Davidson, P., Johansson, S. J., Persson, J. A., and Wernstedt, F., "Agent-based approaches and classical optimization techniques for dynamic distributed resource allocation: a preliminary study", Working paper, Department of Software Engineering and Computer Science Blekinge Institute of Technology, Soft Center, 372 25 Ronneby, Sweden, 2003.
  12. Tang, K. and Kumara, S., "Double auction market mechanism: a distributed negotiation protocol to model an e-procurement problem", The proceedings of IEEE Automation Science and Engineering, 2004.
  13. Oh, S. C., Yee, S.T., and Kim, T. W., "Agent-based shipment algorithm for capacitated vehicle routing problem with load balancing", Journal of the Korean Institute of Industrial Engineers, Vol. 32, No. 3, 200-209, 2006.
  14. Huang, G. Q., Lau, J. S. K., and Mak, K. L., "The impacts of sharing production information on supply chain dynamics: a review of the literature", International. Journal of. Production Research, Vol. 410, No. 7, pp. 1483-1517, 2003.
  15. Lee, H. L., So, K. C., and Tang, C. S., "The value of information sharing in a two-level supply chain", Management Science, Vol. 46, pp. 626-643, 2000. https://doi.org/10.1287/mnsc.46.5.626.12047
  16. Sterman, J. D., Business Dynamics: System Thinking and Modeling for a Complex World (Boston, MA: McGraw-Hill), 2000.
  17. Anderson, B. M., Gremban, K. D., and Young, B. A., "Shipyard operational improvement through process management", Ship Production Symposium, 1997.
  18. Anderson, E. G., Fine, C. H., and Deployer, G. G., "Upstream volatility in the supply chain. the machine tool industry as a case study", Production and Operations Management, 9, pp. 239-261, 2000.
  19. De Souza, R., Song, Z. C., Liu, C. Y., "Supply chain dynamics and optimization", Integrated Manufacturing Systems, Vol. 11, pp. 348-364, 2000. https://doi.org/10.1108/09576060010335627
  20. Brandolese, A., Brun, A., and Portioli-Straudacher, A., "A multi-agent approach for the capacity allocation problem", International Journal of Production Economics, Vol. 66, pp. 269-285, 2000. https://doi.org/10.1016/S0925-5273(00)00004-9
  21. Gjerdrum, J., Shah, N., and Papageorgiou, L. G., "A combined optimization and agentbased approach to supply chain modeling and performance assessment", Production Planning and Control, Vol. 12, No. 1, pp. 81-88, 2001. https://doi.org/10.1080/09537280150204013
  22. Beamon, B. M. and Chen, V. C. P., "Performance analysis of conjoined supply chains", International Journal of Production Research, Vol. 39, pp. 3195-3218, 2001. https://doi.org/10.1080/00207540110053156
  23. Banerjee, S., Banerjee, A., Burton, J., and Bistline, W., "Controlled partial shipments in two-echelon supply chain networks: a simulation study", International Journal of Production Economics, Vol. 71, pp. 91-100, 2001. https://doi.org/10.1016/S0925-5273(00)00108-0