Solving the industrial car sequencing problem in a Pareto sense

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 PMS^MO, 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 PMS^MO. As well, we aim to demonstrate the relevance of handling applied problems such as the car sequencing problem using a multi-objective approach.