Evolutionary algorithms under noise and uncertainty: A location-allocation case study

Evolutionary approaches are metaheuristics that can deal with the effect of noise and uncertainty in data using different strategies. In this paper is depicted the method used to cope with these elements in a dynamical location-allocation problem. The use of Monte Carlo sampling and statistical historical data that can be applied to a single and multi-objective problems and within an online and offline scenario is tested and evaluated.