Discrete Forecast Horizons for Two-Product Variants of the Dynamic Lot-Size Problem

International Journal of Production Economics, Vol. 120, pp. 430-436, 2009

18 Pages Posted: 4 Nov 2008 Last revised: 13 Aug 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: November 2, 2008

Abstract

Motivated by the recent success of integer programming based procedures for computing discrete forecast horizons, we consider two-product variants of the classical dynamic lot-size model. In the first variant, we impose a warehouse capacity constraint on the total ending inventory of the two products in any period. In the second variant, the two products have both individual and joint setup costs for production. To our knowledge, there are no known procedures for computing forecast horizons for these variants.

Under the assumption that future demands are discrete, we characterize forecast horizons for these two variants as feasibility/optimality questions in 0-1 mixed integer programs. A detailed computational study establishes the effectiveness of our approach and enables us to gain valuable insights into the behavior of minimal forecast horizons.

Suggested Citation

Dawande, Milind and Gavirneni, Srinagesh and Naranpanawe, Sanjeewa and Sethi, Suresh, Discrete Forecast Horizons for Two-Product Variants of the Dynamic Lot-Size Problem (November 2, 2008). International Journal of Production Economics, Vol. 120, pp. 430-436, 2009, Available at SSRN: https://ssrn.com/abstract=1293944

Milind Dawande

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 (Contact Author)

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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