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A Material Planning Model for Mixed Model Assembly Lines

  • E. Kozan
  • P. Preston
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 43)

Abstract

A stochastic material planning (SMP) model is developed to incorporate uncertainties in timing and amount in demand, and availability of correct parts when needed to satisfy production. SMP combines a master production schedule (MPS), which determines the optimal production schedule based on inventory, backorder, overtime and slack time costs. SMP uses the bill of material (BOM) to generate parts requirements for weekly production plan determined by MPS which is solved by mixed integer programming. The structure of the BOM is quite complex due to the number and type of variants, and timely use of SMP information assists in the ordering of stock to reduce the risk of delays in production due to stock outs. The SMP model is used to reduce this complexity and to improve the accuracy of a multi-product production plant. The SMP is based on and implemented in a truck production plant, is calculated in a MS-Access database.

Keywords

Operations scheduling production planning and inventory control stochastic modeling 

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References

  1. Bhal, H.C. and Ritzman, L.P. An Integrated Model for Master Scheduling, Lot Sizing and Capacity Requirements Planning. Journal of the Operational Research Society 1984; 35: 389–399.Google Scholar
  2. Burdett, R. L. and Kozan E. Evolutionary algorithms for Flowshop Sequencing with Non-Unique Jobs, International Transactions in Operations Research 2000; 7.5: 401–418.CrossRefGoogle Scholar
  3. Campbell G.M. Master production scheduling under rolling planning horizons with fixed order intervals. Decision-Sciences 1992; 23(2): 312–331.CrossRefGoogle Scholar
  4. Chu, S.C. (1995). A Mathematical Programming Approach Towards Optimised Master Production Scheduling. International Journal of Production Economics 1995; 38(2):269–279.CrossRefGoogle Scholar
  5. Das S.K. Master Scheduling with Incremental Capacity Allocation and a Rolling Horizon. Management Science 1993; 21(3): 353–361.Google Scholar
  6. Gessner, R. A. Master Production Schedule Planning. John Willy and Sons, New York, 1986.Google Scholar
  7. Kimms A. Stability Measures for Rolling Schedules with Applications to Capacity Expansion Planning-Master Production Scheduling and Lot Sizing. Omega 1998; 26(3): 355–366.CrossRefGoogle Scholar
  8. Lin N.P. and Krajewski, L.J. A Model for Master Production Scheduling in Uncertain Environments. Decision-Sciences 1992; 23(4): 839–861.CrossRefGoogle Scholar
  9. Lin N.P., Krajewski, L.; Leong, G.K. and Benton, W.C. The effects of environmental factors on the design of master production scheduling systems. Journal of Operations Management 1994; 11(4): 367–384.CrossRefGoogle Scholar
  10. Sridharan V; Berry, W.L. and Udayabhanu V. Freezing the master Production schedule under rolling planning horizons. Management Science 1987; 33(9): 1137–1149.CrossRefGoogle Scholar
  11. Sridharan, V.; Berry, W.L. and Udayabhanu V. Measuring master Production schedule stability under rolling planning horizons. Decision Sciences 1988; 19(2): 147–166.CrossRefGoogle Scholar
  12. Tallon, W. J. A Comparative Analysis of Master Production Scheduling Techniques for Assemble-to-Order Products. Decision-Sciences 1989; 20(3): 492–506.CrossRefGoogle Scholar
  13. Venkataraman, R. and Smith, S.B. Disaggregation to a Rolling Horizon Master Production Schedule with Medium Batch-Size Production Restriction. International Journal of Production Research 1996; 34(6): 1517–1537.CrossRefGoogle Scholar
  14. Vercellis, C. (1991). Multi-Criteria Models for Capacity Analysis and Aggregate Planning in Manufacturing Systems. International Journal of Production Economics 1991; 23(1): 261–272.CrossRefGoogle Scholar
  15. Yeung, J.H.Y.; Wong, W.C.K and Ma, L. Parameters Affecting the Effectiveness of MRP Systems: a Review. International Journal of Production Research 1998; 36(2), 313–331.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • E. Kozan
    • 1
  • P. Preston
    • 1
  1. 1.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia

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