- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system’s conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies.
In this paper, the integration of the standard industrial processes existing in the literature (system engineering and project management processes) in a global process was described. At first, a detailed version of this proposed integrated process was described by defining the functioning of the various sub-processes and actors involved in the project. This process is fed by knowledge and/or experience bases as well as by experts. It is supported mainly by the multi-criteria decision support tool based on the MONACO algorithm that optimize three objectives of the triplet (cost, duration, risk). The experiments done with a model of a large project graph have shown that the MONACO algorithm gives better results in a reasonable computational time with the learning mechanism than the MOACO one.
Compared to the standard ACO approaches, the proposed MONACO algorithm uses dynamic weights to take into account the paths taken by each ant using the initial consumed capitals of cost, duration and risk. Another specificity of this algorithm is the consideration of risk as a third objective to optimize besides cost and duration which is an overall view of risk in the MONACO algorithm. Moreover, the proposed approach developed in this article is very useful to engineers, project managers, risk managers, etc. It allows to select, at the earliest phases of a system engineering project, one Pareto-optimal project scenario that will be scheduled and realized.