Advertisement

Load Dependent Lead Times — From Empirical Evidence to Mathematical Modeling

  • Julia Pahl
  • Stefan Voß
  • David L. Woodruff

Summary

As organizations move from creating plans for individual production lines to entire supply chains it is increasingly important to recognize that decisions concerning utilization of production resources impact the lead times that will be experienced. In this paper we give some insights into why this is the case by looking at the queuing that results in delays. In this respect, special mention should be made that it is difficult to experience related empirical data, especially for tactical planning issues. We use these insights to survey and suggest optimization models that take into account load dependent lead times and related “complications.”

Keywords

Supply Chain Management Load Dependent Lead Times Lead Times Tactical Planning Aggregate Planning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

5 References

  1. Asmundsson, J., Rardin, R. L., Uzsoy, R. (2002): Tractable Nonlinear Capacity Models for Aggregate Production Planning, Working Paper, School of Industrial Engineering, Purdue University, West Lafayette.Google Scholar
  2. Asmundsson, J., Rardin, R. L., Uzsoy, R. (2003): An Experimental Comparison of Linear Programming Models for Production Planning Utilizing Fixed Lead Time & Clearing Functions, Working Paper, School of Industrial Engineering, Purdue University, West Lafayette.Google Scholar
  3. Buzacott, J. A., Shantikumar, J. G. (1993): Stochastic Models of Manufacturing Systems, Englewood Cliffs, New York.Google Scholar
  4. Caramanis, M. C., Ahn, O. M. (1999): Dynamic Lead Time Modeling for JIT Production Planning, Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, May 10–15, v2, 1450–1455.Google Scholar
  5. Chen, H., Harrison, M. J., Mandelbaum, A., van Ackere, A., Wein, L. M. (1988): Empirical Evaluation of a Queuing Network Model for Semiconductor Wafer Fabrication, Operations Research, 36(2): 202–215.Google Scholar
  6. Ettl, M., Feigin, G. E., Lin, G. Y., Yao, D. D. (2000): A Supply Network Model with Base-Stock Control and Service Requirements, Operations Research, 48(2): 216–232.CrossRefGoogle Scholar
  7. Graves, S. (1986): A Tactical Planning Model for Job Shops, Operations Research, 34(4): 522–533.zbMATHGoogle Scholar
  8. Hackman, S. T., Leachman, R. C. (1989): A General Framework for Modeling Production, Management Science, 35(4): 478–495.Google Scholar
  9. Karmarkar, U. S. (1987): Lot Sizes, Lead Times and In-Process Inventories, Management Science, 33(3): 409–418.MathSciNetzbMATHGoogle Scholar
  10. Karmarkar, U. S. (1989): Capacity Loading and Release Planning with Work-In-Process (WIP) and Lead Times, Journal of Manufacturing and Operations Management, 2(2): 105–123.Google Scholar
  11. Karmarkar, U. S. (1993): Manufacturing Lead Times, Order Release and Capacity Loading, in: Graves, S., Rinnooy Kan, A., Zipkin, P. (eds.): Logistics of Production and Inventory, Handbooks in Operations Research and Management Science, Vol. 4, Amsterdam: p. 287–329.Google Scholar
  12. Karmarkar, U. S., Kekre, S., Kekre, S. (1985): Lotsizing in Multi-Item Multi Machine Job Shops, IIE Transactions, 17(3): 290–297.Google Scholar
  13. Lautenschläger, M. (1999): Mittelfristige Produktionsprogrammplanung mit auslastungsabhängigen Vorlaufzeiten (“Tactical Production Planning with Workload Dependent Forward Production Times”), PhD Thesis, TU Darmstadt.Google Scholar
  14. Missbauer, H. (1998): Bestandsregelung als Basis für eine Neugestaltung von PPSSystemen (“Inventory Control as a Basis for a New Concept for PPS-Systems”), Physica, Heidelberg.Google Scholar
  15. Spearman, M.L. (1991): An Analytic Congestion Model for Closed Production Systems with IFR Processing Times, Management Science, 37(8): 1015–1029.zbMATHCrossRefGoogle Scholar
  16. Srinivasan, A., Carey, M., Morton, T. E. (1990): Resource Pricing and Aggregate Scheduling in Manufacturing Systems, Working Paper, GSIA, 1988 (Revised December 1990).Google Scholar
  17. Suri, R., Sanders, J.L. (1993): Performance Evaluation of Production Networks, in: Graves, S., Rinnooy Kan, A., Zipkin, P. (eds.): Logistics of Production and Inventory, Handbooks in Operations Research and Management Science, Vol. 4, Amsterdam: p. 199–286.Google Scholar
  18. Tatsiopoulos, I.P., Kingsman, B.G. (1983): Lead Time Management, European Journal of Operational Research, 14(4): 351–358.CrossRefGoogle Scholar
  19. Voß, S., Woodruff D.L. (2003): An Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Springer, Berlin.Google Scholar
  20. Zäpfel, G., Missbauer, H. (1993): New Concepts for Production Planning and Control, European Journal of Operational Research, 67(3): 297–320.Google Scholar
  21. Zijm, W. H. M., Buitenhek, R. (1996): Capacity Planning and Lead Time Management, International Journal of Production Economics, 46–47: 165–179.Google Scholar
  22. Zipkin, P. H. (1986): Models for Design and Control of Stochastic, Multi-Item Batch Production Systems, Operations Research, 34(1): 91–104.MathSciNetzbMATHGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2005

Authors and Affiliations

  • Julia Pahl
    • 1
  • Stefan Voß
    • 2
  • David L. Woodruff
    • 3
  1. 1.Institute of Information Systems (Wirtschaftsinformatik)University of HamburgHamburgGermany
  2. 2.Institute of Information SystemsUniversity of HamburgHamburgGermany
  3. 3.Graduate School of ManagementUniversity of California, DavisDavisUSA

Personalised recommendations