A Genetic Algorithm for Solving an Optimization Problem: Decision Making in Project Management

Abstract

In this paper, an optimization problem belonging to project management family is determined, where the objective is to maximize the benefit of a certain projects. The selection of projects to be performed with a limited budget is a decision process which is characterized by its difficulty in case of high scale problems. In this paper, an approach based on genetic algorithm is presented for solving high scale of the tackled problem with the goal of maximization the benefits. First, a mathematical programming model is presented to represent the problem. Then, a heuristic method was proposed based on genetic algorithm and random neighborhood search techniques. The study realized here simulate a practical situation as an optimization problem and highlighted the effectiveness of genetic algorithm and random search techniques for solving it. The presented method is competitive since it is able to present high quality solutions in acceptable solution time. As shown in the computation results section the proposed genetic algorithm: Decision-Making in Project Management average solution needs 13.7 s, random neighborhood search needs 14.1 s, and greedy procedure needs so small time. In spite of genetic algorithm: Decision-Making in Project Management required more solution time than others algorithms greedy algorithm and random neighborhood algorithm, but the solution quality is better. In addition, the presented work highlights the effectiveness of optimization solution procedures in decision-making to justify the investment budget and so maximizing benefits of organizations, personnel, and others.