My research interests are: The use of population-based techniques for solving optimization problems is continuously increasing, as these kind of problems appears in a number of situations in real life. Many of these problems are of a combinatorial nature, this means that we have to find the ideal permutation or subset of the parameters involved to reach the optimal solution. As the number of parameters increases, the difficulty to find the optimal solutions becomes harder, and if we are conscious that a large amount of problems have more than one objective to optimize, this task grows to be too much harder. There are many theoretical combinatorial problems that can be directly applied to real-life, one of them being the vehicle routing problem (VRP), which is relevant to distribution systems and transportation logistics, like post, parcel and delivery service. The VRP's main objective is to obtain the lowest-cost set of routes to deliver demand to customers, where cost can mean many things, like travel distance, number of routes, delivery time, makespan, workload balance, etc. This means that we can consider the VRP as a multi-objective problem. |