Using multiobjective metaheuristics to solve VRP with uncertain demands

In real life optimization problems, it is very important to have high quality solutions (optimal). But when uncertainty becomes part of the optimization problem, solutions should be optimal and robust to the uncertain environmental changes. This paper focuses on finding robust optimal solution for the vehicle routing problem with stochastic demands VRPSD. In this case when the uncertainty of the customers demands enters this problem, the classical methods of VRP can not be used to obtain optimal solutions. We need new methods with new strategies to have robust optimal solution. For that we propose two bi-objective models, depending on the multi-objective evolutionary algorithms MOEAs: IBEA, MOGA and NSGAII. We compare the robustness degree of the two models and also we compare the performance of the three MOEAs over these two models.