Computing Minimal Forecast Horizons: An Integer Programming Approach

Journal of Mathematical Modelling and Algorithms, Vol. 5, No. 2, pp. 239-258, June 2006

23 Pages Posted: 12 May 2008 Last revised: 6 Nov 2008

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

Abstract

In this paper, we use integer programming (IP) to compute minimal forecast horizons for the classical dynamic lot-sizing problem (DLS). As a solution approach for computing forecast horizons, integer programming has been largely ignored by the research community. It is our belief that the modelling and structural advantages of the IP approach coupled with the recent significant developments in computational integer programming make for a strong case for its use in practice. We formulate some well-known sufficient conditions, and necessary and sufficient conditions (characterizations) for forecast horizons as feasibility/optimality questions in 0-1 mixed integer programs. An extensive computational study establishes the effectiveness of the proposed approach.

Keywords: Multiperiod Problems, Forecast Horizons, Rolling Horizons, Decision Horizons, Planning Horizons, Solution Horizons, Forecasting, Lot Size Models, Operations Management, integer programming

Suggested Citation

Dawande, Milind and Gavirneni, Srinagesh and Naranpanawe, Sanjeewa and Sethi, Suresh, Computing Minimal Forecast Horizons: An Integer Programming Approach. Journal of Mathematical Modelling and Algorithms, Vol. 5, No. 2, pp. 239-258, June 2006. Available at SSRN: https://ssrn.com/abstract=1128752

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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