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Progressive Interval Heuristics for Multi-Item Capacitated Lot-Sizing Problems

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Author Info

  • Awi Federgruen

    (Graduate School of Business, Columbia University)

  • Joern Meissner

    (Department of Management Science, Lancaster University Management School)

  • Michal Tzur

    (Department of Industrial Engineering, Tel Aviv University)

Abstract

We consider a family of N items which are produced in or obtained from the same production facility. Demands are deterministic for each item and each period within a given horizon of T periods. If in a given period an order is placed, setup costs are incurred. The aggregate order size is constrained by a capacity limit. The objective is to find a lot-sizing strategy that satisfies the demands for all items over the entire horizon without backlogging, and which minimizes the sum of inventory carrying, fixed and variable order costs. All demands, cost parameters and capacity limits may be time-dependent. In the basic (JS)-model, the setup cost of an order does not depend on the composition of the order. The (JIS)-model allows for item-dependent setup costs in addition to the joint setup costs. We develop and analyze a class of so-called progressive interval heuristics. A progessive interval heuristic solves a (JS) or (JIS) problem over a progressively larger time-interval, always starting with period 1, but fixing the setup variables of a progressively larger number of periods at their optimal values in earlier iterations. Different variants in this class of heuristics allow for different degrees of flexibility in adjusting continuous variables determined in earlier iterations of the algorithm. For the (JS)-model and the two basic implementations of the progressive interval heuristics, we show under some mild parameter conditions, that the heuristics can be designed to be epsilon-optimal for any desired value of epsilon > 0 with a running time that is polynomially bounded in the size of the problem. They can also be designed to be simultaneously asymptotically optimal and polynomially bounded. A numerical study covering both the (JS) and the (JIS) model, shows that a progressive interval heuristic generates close-to-optimal solutions with modest computational effort and that it can be effectively used to solve large-scale problems.

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Bibliographic Info

Paper provided by Department of Management Science, Lancaster University in its series Working Papers with number MRG/0001.

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Length: 13 pages
Date of creation: Sep 2002
Date of revision: Nov 2004
Publication status: Published in Operations Research Vol 55, No 3 (May-June 2007), pp 490-502.
Handle: RePEc:lms:mansci:mrg-0001

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Postal: LANCASTER LA1 4YX
Web page: http://www.lums.lancs.ac.uk/departments/ManSci/
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Related research

Keywords: supply chain management; inventory models; lot sizing; time partitioning;

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References

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  1. Gabriel R. Bitran & Horacio H. Yanasse, 1982. "Computational Complexity of the Capacitated Lot Size Problem," Management Science, INFORMS, vol. 28(10), pages 1174-1186, October.
  2. William W. Trigeiro & L. Joseph Thomas & John O. McClain, 1989. "Capacitated Lot Sizing with Setup Times," Management Science, INFORMS, vol. 35(3), pages 353-366, March.
  3. Michael Florian & Morton Klein, 1971. "Deterministic Production Planning with Concave Costs and Capacity Constraints," Management Science, INFORMS, vol. 18(1), pages 12-20, September.
  4. Dong X. Shaw & Albert P. M. Wagelmans, 1998. "An Algorithm for Single-Item Capacitated Economic Lot Sizing with Piecewise Linear Production Costs and General Holding Costs," Management Science, INFORMS, vol. 44(6), pages 831-838, June.
  5. Gaetan Belvaux & Laurence A. Wolsey, 2001. "Modelling Practical Lot-Sizing Problems as Mixed-Integer Programs," Management Science, INFORMS, vol. 47(7), pages 993-1007, July.
  6. M. Florian & J. K. Lenstra & A. H. G. Rinnooy Kan, 1980. "Deterministic Production Planning: Algorithms and Complexity," Management Science, INFORMS, vol. 26(7), pages 669-679, July.
  7. Stadtler, Hartmut, 2003. "Multilevel lot sizing with setup times and multiple constrained resources: Internally rolling schedules with lot-sizing windows," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 20204, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  8. van Nunen, J. A. E. E. & Wessels, J., 1978. "Multi-item lot size determination and scheduling under capacity constraints," European Journal of Operational Research, Elsevier, vol. 2(1), pages 36-41, January.
  9. Suerie, Christopher & Stadtler, Hartmut, 2003. "The Capacitated lot-sizing problem with linked lot sizes," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 20206, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  10. Stephen C. Graves, 1982. "Using Lagrangean Techniques to Solve Hierarchical Production Planning Problems," Management Science, INFORMS, vol. 28(3), pages 260-275, March.
  11. Christopher Suerie & Hartmut Stadtler, 2003. "The Capacitated Lot-Sizing Problem with Linked Lot Sizes," Management Science, INFORMS, vol. 49(8), pages 1039-1054, August.
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Cited by:
  1. Kerem Akartunalı & Andrew Miller, 2012. "A computational analysis of lower bounds for big bucket production planning problems," Computational Optimization and Applications, Springer, vol. 53(3), pages 729-753, December.
  2. Kristin Uggen & Marte Fodstad & Vibeke Nørstebø, 2013. "Using and extending fix-and-relax to solve maritime inventory routing problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 21(2), pages 355-377, July.
  3. Jenny Carolina Saldaña Cortés, 2011. "Programación semidefinida aplicada a problemas de cantidad económica de pedido," DOCUMENTOS CEDE 008735, UNIVERSIDAD DE LOS ANDES-CEDE.

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