• Route selection

This thesis introduces a Genetic Algorithm for the holistic optimized manufacturing route selection. Two different approaches, such as the adaptive Production Planning and Control, and the Flexible Job Shop Problem, are combined. The new method given by this combination allows to develop an evaluation of manufacturing routes, by optimizing the production system as a whole, but not by developing a rigid schedule, in order to recalculate alternative work plans everty time that changes affect the production environent. Through a comparison with a Machine Assignament Rule, the results show that the proposed algorithm works as expected in a maximum number of components, within the production layout, which ranges from one hundred to one thousand, in terms of total throughput. However, there aren't benefits in terms of avarage cost and avarage waste rate. Nevertheless, if the objective function of the Genetic Algorithm is given by time, cost and quality separately, the algorithm performs in the right way, in therm of total thoughput, avarage cost, and quality. In addition, the advantage in using this method, is related to the fact that it can face production changes and route constraints, through the iterative setting up of all the possible production routes for each job in a certain moment of the production.