Evolved genetic algorithms with fuzzy aggregation applied to priorities in logistic systems

This paper addresses the problem of optimizing the schedule of logistic processes using genetic algorithms and fuzzy decision making. We consider here the problem of dynamically assign components to orders and choose the solution that is able to deliver more orders at the correct date, respecting at the same time the priority degree of the orders. A compromise between these conflicting goals is achieved by using a genetic algorithm to optimize a fuzzy weighted function. The simulation results show that the proposed genetic algorithm evolved with fuzzy optimization presents good results for this type of problems.