Abstract
One of the main conflicts in a car production plant is to deliver the orders received daily in a timely manner, which are not uniform and involve a large amount of human and material resources. The car sequencing problem is a NP-Hard problem that consists of finding the sequence of cars that minimizes the number of constraint violations in an assembly line. The problem can be approached from a mono-objective or multi-objective point of view. The objective of this paper is to treat a case study of this problem, presented at ROADEF 2005, from the multi-objective Pareto approach, taking the NSGAII algorithm as a basis for a proposal scheme and verifying its feasibility. A systematic and general improvement of the quality of the final Pareto fronts is verified, and the results of the implementation of a strategy scheme that consists of the initialization of the population guided by local search, and specialized crossover and mutation operators are reported. These results allow us to give continuity to the generation of an optimization proposal for the vehicle sequencing problem.
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