Optimization of logistic systems using fuzzy weighted aggregation

Logistic scheduling problems are often multi-criteria optimization problems, with many contradictory objectives and constraints, which cannot be properly described by conventional cost functions. The use of fuzzy decision making may improve the performance of this type of systems, since it allows an easier and suitable description of the confluence of the different criteria of the scheduling process. This paper introduces the application of fuzzy weighted aggregation to formulate the logistic system optimization problem. Further, this paper also extends the application of this framework to different types of optimization methodologies: dispatching rules, if it is used as a performance index; or meta-heuristics, such as genetic algorithms (GA) or ant colony optimization (ACO), if it is used as an objective function. Simulation results show that the fuzzy combination of criteria improves the scheduling results whatever optimization methodology is used.

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