Many real-world optimization problems, especially in the field of logistics, have to face a lot of difficulties. Indeed, they are often characterized by large and complex search spaces, multiple conflicting objective functions, and a host of uncertainties that have to be taken into account. Metaheuristics are natural candidates to solve those problems and make them preferable to classical optimization methods. However, the development of efficient metaheuristics results in a complex engineering process. The core subject of this work lies in the design, implementation and experimental analysis of metaheuristics for multiobjective optimization, together with their applications to logistic problems from routing and scheduling. Firstly, a unified view of such approaches is presented and then integrated into a software framework for their implementation, ParadisEO-MOEO. Next, some cooperative approaches combining metaheuristics for multiobjective optimization are proposed. At last, the issue of uncertainty handling is discussed in the context of multiobjective optimization.
展开▼
