Multi-objective optimization and risk assessment in system engineering project planning by Ant Colony Algorithm

This article addresses the multi-objective optimization in system engineering project planning. At the earliest steps of a system engineering process, many choices can be done taking into account experience feedback. An approach that permits to a decision maker to perform the selection of scenarios during a project is presented. The objectives to optimize are the total cost of the project, its total duration and the global risk. An algorithm based on the Ant Colony Algorithm (ACO) and the Pareto front principles is used. It permits to explore the objective space and to propose to the decision makers Pareto-optimal solutions that are distributed on the Pareto front. The method permits to select solutions (different scenarios for a project) taking into account the risk that a problem arises. The ACO has been developed with the Ruby language and some experiments have been done in order to validate the approach.