Industrial Engineering and Management Systems

  • 제9권3호
  • /
  • Pages.227-241
  • /
  • 2010
  • /
  • 1598-7248(pISSN)
  • /
  • 2234-6473(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지
DOI QR코드

DOI QR Code

A Genetic Algorithm for Directed Graph-based Supply Network Planning in Memory Module Industry

  • Wang, Li-Chih (Department of Industrial Engineering and Enterprise Information Tunghai Univeristy) ;
  • Cheng, Chen-Yang (Department of Industrial Engineering and Enterprise Information Tunghai Univeristy) ;
  • Huang, Li-Pin (Department of Industrial Engineering and Enterprise Information Tunghai Univeristy)
  • 투고 : 2010.03.29
  • 심사 : 2010.07.30
  • 발행 : 2010.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

키워드

참고문헌

  1. Altiparmak, F. and Gen, M. (2006), A genetic algorithm approach for multi-objective optimization of supply chain networks, Computers and Industrial Engineering, 51, 197-216.
  2. Arntzen, B. C. and Brown, G. G. (1995), Global supply chain management at digital equipment corporation, Interfaces, 25, 69-93. https://doi.org/10.1287/inte.25.1.69
  3. Chen, S. Y. and Chern, C. C. (1999), Shortest path for a supply chain network. The 4th International Conference, Asia-Pacific Region of Decision Sciences Institute.
  4. Cheng, C. P., Liu C. W., and Liu C. C. (2000), Unit commitment by lagrangian relaxation and genetic algorithm, IEEE, 15(2).
  5. Chern, C.-C. and Hsieh, J.-S. (2007), A heuristic algorithm for master planning that satisfies multiple objectives, Computers and Operations Research, 3419-3513.
  6. Guinet, A. (2001), Multi-site planning: a transshipment problem, International Journal of Production Economics, 74, 21-32. https://doi.org/10.1016/S0925-5273(01)00104-9
  7. Kanyalkar, A. P. and Adil, G. K. (2008), A robust optimisation model for aggregate and detailed planning of a multi-site procurement-production-distribution system, International Journal of Production Research, 1-22.
  8. Kawtummachai, R. and Hop, N. V. (2005), Order allocation in a multiple-supplier environment, International Journal of Production Economics, 231-238.
  9. Lendermann, P. and Gan, B. P. (2001), Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization, Winter Simulation Conference.
  10. Lin, J. T. and Chen, Y.-Y. (2007), A multi-site supply network planning problem considering variable time buckets-a TFT-LCD industry case, International Journal of Advanced Manufacturing Technology, 33.
  11. Moon, C., Kim, J., and Hur, S. (2002), Integrated process planning and scheduling with minimizing total tardiness in multi plants supply chain, Computers and Industrial Engineering.
  12. Nie L. S., Xu X. F., and Zhan D. C. (2006), Collaborative planning in supply chains based on Lagrangian relaxation and genetic algorithm, Computer Integrated Manufacturing Systems, 11.
  13. Timpe, C. H. and Kallrath, J. (2000), Optimal planning in large multi-site production networks, European Journal of Operational Research, 126, 422-435. https://doi.org/10.1016/S0377-2217(99)00301-X
  14. Watson, K. and Polito, T. (2003), Comparison of DRP and TOC financial performance within a multiproduct, multi-echelon physical distribution environment, International Journal of Production Research, 41, 741-765. https://doi.org/10.1080/0020754031000065511
  15. Wu, D. (2004), Multi-item, multi-facility supply chain planning: models, complexities, and algorithms, Computational Optimization and Applications, 28, 325-356. https://doi.org/10.1023/B:COAP.0000033967.18695.9d

피인용 문헌

  1. A genetic algorithm for dynamic inbound ordering and outbound dispatching problem with delivery time windows vol.44, pp.7, 2012, https://doi.org/10.1080/0305215X.2011.617816
  2. Generation of Business Process Reference Model Considering Multiple Objectives vol.11, pp.3, 2010, https://doi.org/10.7232/iems.2012.11.3.233
  3. Flexible supply network planning for hybrid shipment: a case study of memory module industry vol.54, pp.2, 2016, https://doi.org/10.1080/00207543.2015.1133939