Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Heuristic-Based Algorithm for Production Planning Considering Allocation Rate Conformance to Prevent Unstable Production Chain

  • Kim, Taehun (Department of Industrial and Management Engineering, Pohang University of Science and Technology) ;
  • Ji, Bongjun (Department of Industrial and Management Engineering, Pohang University of Science and Technology) ;
  • Cho, Hyunbo (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
  • Received : 2015.12.14
  • Accepted : 2015.12.20
  • Published : 2015.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study solved the problem of unstable production chains by considering allocation rate conformance. We proposed two phased algorithm suitable for solving production planning that considers allocation rate conformance; the first phase was heuristic initial solution generation, and the second phase was tabu-search based solution improvement. By using three data sets which have different sizes of data and three different criteria, the results of proposed algorithm were compared with MIP results. The proposed algorithm showed the best production plan in terms of allocation rate conformance, and it was appropriate for other criteria; it solved the problem of unstable production chains by solving concentrated and unfair allocation.

Keywords

References

  1. Cachon, G. P. (2003), Supply chain coordination with contracts, Handbooks in Operations Research and Management Science, 11, 229-340.
  2. Chen, Y. Y. (2010), An Analytical Framework for Multi-Site Supply Chain Planning Problems, World Academy of Science, Engineering and Technology, 41, 1135-1141.
  3. Ehrgott, M. (2005), Multicriteria optimization, Springer.
  4. Gelders, L. F. and Wassenhove, L. N. V. (1981), Production planning: a review, European Journal of Operational Research, 7(2), 101-110. https://doi.org/10.1016/0377-2217(81)90271-X
  5. Glover, F. (1990), Tabu search: A tutorial, Interfaces, 20(4), 74-94. https://doi.org/10.1287/inte.20.4.74
  6. Hwang, C. L. and Masud, A. S. M. (1979), Multiple objective decision making-methods and applications: a state-of-the-art survey, Springer, Berlin.
  7. Kibira, Deogratias, et al. (2015), Analysis of Standards Towards Simulation-Based Integrated Production Planning, Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth, Springer International Publishing, 39-48.
  8. Kim, T. et al. (2015), Decomposing Packaged Services Towards Configurable Smart Manufacturing Systems, Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth, Springer International Publishing, 74-81.
  9. Lin, J. T. and Chen, Y.Y. (2007), A multi-site supply network planning problem considering variable time buckets-A TFT-LCD industry case, The International Journal of Advanced Manufacturing Technology, 33(9), 1031-1044. https://doi.org/10.1007/s00170-006-0537-z
  10. Moon, C., Kim, J. and Hur, S. (2002), Integrated process planning and scheduling with minimal total tardiness in multi-plants supply chain, Computers and Industrial Engineering, 43, 331-349. https://doi.org/10.1016/S0360-8352(02)00078-5
  11. Roux, W., Dauzere-Peres, S., and Lasserre, J. B. (1999), Planning and scheduling in a multi-site environment, Production Planning and Control, 10, 19-28. https://doi.org/10.1080/095372899233389
  12. 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
  13. Vercellis, C. (1999), Multi-plant production planning in capacitated self-configuring two-stage serial systems, European Journal of Operational Research, 119, 451-460. https://doi.org/10.1016/S0377-2217(99)00146-0
  14. Wagner, S. M. and Bode, C. (2006), An empirical investigation into supply chain vulnerability, Journal of Purchasing and Supply Management, 12(6), 301-312. https://doi.org/10.1016/j.pursup.2007.01.004

Cited by

  1. Sequencing the Mixed Model Assembly Line with Multiple Stations to Minimize the Total Utility Work and Idle Time vol.15, pp.1, 2016, https://doi.org/10.7232/iems.2016.15.1.001