This paper analyzes the trade-off between (demand) substitution costs and (production) changeover costs in a discrete-time production-inventory setting using a two-product dynamic lot-sizing model with changeover, inventory carrying, and substitution costs. We first show that the problem is...

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...

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...

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...

For most multi-period decision-making problems, it is generally well-accepted that the influence of information about later periods on the optimal decision in the current period reduces as we move farther into the future. If and when this influence reduces to zero, the corresponding problem...

This paper analyzes the tradeoff between (demand) substitution costs and (production) changeover costs in a discrete-time production-inventory setting using a two-product dynamic lot-sizing model with changeover, inventory carrying, and substitution costs. We first show that the problem is...