A capacitated lot-sizing problem with limited number of changes

This work deals with single-level lot sizing decisions with capacity constraints. Lot sizing decisions give rise to the problem of identifying when and how much of an article to produce such that setup, production and holding costs are minimized. Setting up the machine for producing a product different from the previous one, would cause a changeover cost due, for example, to clean or install the appropriate tools for the machine. We focus on the Continuous Setup Lot Sizing Problem (CSLP) which allows partial capacity usage, in the multi-item case. Only a single item can be produced per period per resource. In the literature and to our knowledge, the problem of number of change limited is not addressed. Thus, we developed an extension of the typical model in the multi-item, multi-resource case and solved the problem with a genetic algorithm (GA) and an ant colony optimization (ACO).