Abstract
Until now, the industrial car sequencing problem, as defined during the ROADEF 2005 Challenge, has been tackled by organizing objectives in a hierarchy. In this paper, we suggest tackling this problem in a Pareto sense for the first time. We thus suggest the adaptation of the PMSMO, an elitist evolutionary algorithm which distinguishes itself through a fitness calculation that takes into account the history of solutions found so as to diversify the compromise solutions along the Pareto frontier. A comparison of the performance is carried out using a well-known published algorithm, the NSGAII, and proves an advantage for the PMSMO. As well, we aim to demonstrate the relevance of handling applied problems such as the car sequencing problem using a multi-objective approach.