Discrete forecast horizons for two-product variants of the dynamic lot-size problem

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.