Forecast Horizons for a Class of Dynamic Lot-Size Problems under Discrete Future Demand

Operations Research, Vol. 55, No. 4, pp. 688-702, July-August 2007

39 Pages Posted: 8 Apr 2008 Last revised: 23 Jan 2009

See all articles by Milind Dawande

Milind Dawande

University of Texas at Dallas - Department of Information Systems & Operations Management

Srinagesh Gavirneni

Cornell University - Samuel Curtis Johnson Graduate School of Management

Sanjeewa Naranpanawe

affiliation not provided to SSRN

Suresh Sethi

University of Texas at Dallas - Naveen Jindal School of Management

Date Written: August 1, 2007

Abstract

We present structural and computational investigations of a new class of weak forecast horizons - minimal forecast horizons under the assumption that future demands are integer multiples of a given positive real number - for a specific class of dynamic lot-size (DLS) problems. Apart from being appropriate in most practical instances, the discreteness assumption offers a significant reduction in the length of a minimal forecast horizon over the one using the classical notion of continuous future demands. We provide several conditions under which a discrete-demand forecast horizon is also a continuous-demand forecast horizon. We also show that the increase in the cost resulting from using a discrete minimal forecast horizon instead of the classical minimal forecast horizon is modest. The discreteness assumption allows us to characterize forecast horizons as feasibility/optimality questions in 0-1 mixed-integer programs. On an extensive test bed, we demonstrate the computational tractability of the integer programming approach. Owing to its prevalence in practice, our computational experiments emphasize the special case of integer future demands.

Keywords: inventory/production, planning horizons, programming, integer, applications

JEL Classification: M11, C61

Suggested Citation

Dawande, Milind and Gavirneni, Srinagesh and Naranpanawe, Sanjeewa and Sethi, Suresh, Forecast Horizons for a Class of Dynamic Lot-Size Problems under Discrete Future Demand (August 1, 2007). Operations Research, Vol. 55, No. 4, pp. 688-702, July-August 2007, Available at SSRN: https://ssrn.com/abstract=1117696

Milind Dawande (Contact Author)

University of Texas at Dallas - Department of Information Systems & Operations Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Srinagesh Gavirneni

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Sanjeewa Naranpanawe

affiliation not provided to SSRN ( email )

Suresh Sethi

University of Texas at Dallas - Naveen Jindal School of Management ( email )

800 W. Campbell Road, SM30
Richardson, TX 75080-3021
United States

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