Setup tasks scheduling during production resettings
Résumé
Production waste reduction during production resettings is critical in the industry as it is the key point of flexibility increase. One way to improve production waste is to assign setup tasks efficiently to operators. This article describes a possible model of this problem adapted to a real-life application. This model is based on an unrelated parallel machines problem that takes into consideration the skills of the operators and the production line structure. The nature of the data and some industrial constraints help us simplifying this problem into an assignment problem. As often in the industry, the objective is to maximise production. Because most production lines are complex series parallel lines, it is often possible to maintain production even if not all machines are running. This particularity makes the criterion hard to express as it depends on the line structure. In this article we describe three heuristics to solve this problem: a hill climbing algorithm, a genetic algorithm and a memetic algorithm that combines the advantages of the two previous algorithms. The neighbourhood used for these algorithms is based on multiple exchanges of tasks between operators.