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Optimising a Horizontally Integrated Push/Pull Hybrid Production System in a Foundry

  • Paul Corry
  • Erhan Kozan
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 43)

Abstract

This study investigates a horizontally integrated push/pull hybrid production system (HIHPS) of a large foundry in the Asia-Pacific region. Components are produced according to a pushing philosophy and are stored in a buffer when complete. Assembled goods draw components from the buffer and are made to order. The objective is to minimise the sum of ordering, inventory and tardiness costs. Currently, the inventory decisions are made based on the experience of personnel within the foundry. Operations research provides an opportunity to improve efficiency by using computational techniques to determine good inventory decisions. Traditional inventory and lot-sizing problems determine optimal policies using mathematical analysis made possible by simplifying assumptions. These assumptions are invalid for the HIHPS problem studied in this paper so that mathematical analysis is extremely difficult if not impossible. As an alternative, simulation is combined with simulated annealing to determine a good inventory policy. Experimental results are used to evaluate the performance of this approach and to identify strategies for developing even better techniques.

Key words

Scheduling heuristics push/pull hybrid production system simulated annealing 

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References

  1. Bookbinder, J. H. & Cakanyildirim, M. Random lead times and expedited orders in (Q, r) inventory systems. European Journal of Operational Research, 1999: 115, 300–313.CrossRefGoogle Scholar
  2. Browne, J, Harhen, J & Shivnan, J. Production Management Systems: A CIM Perspective. Reading MA: Addison Wesley, 1988.Google Scholar
  3. Cochran, J. K. & Kim, S. S. Optimum junction point location and inventory levels in serial hybrid push/pull production systems. International Journal of Production Research, 1997, 36.4: 1141–1155Google Scholar
  4. Gavirneni, S. An efficient heuristic for inventory control when the customer is using a (s,S) policy. Operations Research Letters 28, 2001, 187–192.CrossRefGoogle Scholar
  5. Graves, S. C., Rinnoy Kan, A. H. G. & Zipkin, P. H. (Eds). Logistics of production and inventory. Handbooks in operations research and management science (4). Netherlands, Elsevier Science Publishers, 1993.Google Scholar
  6. Karmarkar, U. S. Integrating MRP with kanban/pull system (Working Paper Series #QM8615). Graduate School of Management, University of Rochester 1986,Google Scholar
  7. Kimura, O. & Terada, H. Design and analysis of pull system, a method of multistage production control. International Journal of Production Research, 1981, 19: 241–253.CrossRefGoogle Scholar
  8. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science, 1983, 220: 671–680CrossRefGoogle Scholar
  9. Sarker, B. R. & Fitzsimmons, J. A. The performance of push and pull systems: a simulation and comparative study. International Journal of Production Research, 1989, 27: 1715–1732.CrossRefGoogle Scholar
  10. Sugimori, Y., Kusunoki, K., Cho, F. & Uchikawa, S. Toyota production system and Kanban system materialization of just-in-time and respect-for-human system. International Journal of Production Research, 1977, 15, 553–564.CrossRefGoogle Scholar
  11. Takahashi, K., Hiraki, S. & Soshiroda, M. Pull-Push integration in production ordering systems. International Journal of Production Economics, 1994, 33: 155–161.CrossRefGoogle Scholar
  12. Winston, W. L. Operations Research: Applications and Algorithms. California: International Thomson Publishing, 1994.Google Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Paul Corry
    • 1
  • Erhan Kozan
    • 1
  1. 1.School of Mathematical Sciences Queensland University of TechnologyAustralia

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