Technical note: new results for the capacited lot sizing problem with overtime decisions and setup times
Résumé
The capacitated lot sizing problem with overtime and setup times (CLSPOS) consists of planning the lot sizes of multiple families over a planning horizon with the objective of minimizing overtime and inventory holding costs. Each time that an item's lot size is positive, capacity is consumed by a setup. Capacity is limited and includes regular time capacity as well as overtime. It is assumed that setups do not incur costs other than lost production capacity and therefore, setups contribute to total costs implicitly via overtime costs whenever capacity bottlenecks occur. The CLSPOS is more complicated than the standard capacitated lot sizing problem (CLSP) which involves explicit setup costs, no capacity consuming setups and only regular time capacity. Here, we describe a genetic algorithm (GATA) integrated with tabu search (TS) and simulated annealing (SA) to solve CLSPOS. GATA integrates the powerful characteristics of all three search algorithms, GAs, TS and SA. We compare our results with the ones reported in a previous study and demonstrate that GATA outperforms other heuristics.