Hybrid Taguchi-genetic Algorithm for Selecting and Scheduling a Balanced Project Portfolio

Abstract

The importance of maintaining a balancing portfolio makes the selection of projects a crucial management issue in many organizations and a complex decision-making process. In this study, we consider the problems associated with selecting and scheduling a set of R&D projects to maximize the overall net present value. The level of effort focused on each of the key strategies in the selected projects must be maintained, without violating the available annually budget. This study faces the problems frequently arising in real world applications, combining three issues (i.e., selection, scheduling, and balance) that have not been simultaneously addressed in previously suggested models. This paper proposes a zero-one integer programming model in conjunction with a genetic algorithm (GA) to overcome these problems. Furthermore, Taguchi Method was employed in the design of the GA parameters to increase the efficiency of the proposed method. A number of small problems were randomly generated to validate the effectiveness of the GA by comparing the solutions with those provided by AMPL. In addition, to evaluate the efficiency of the proposed GA six large problems were generated to identify the most appropriate parameters. From the computational results, we conclude that the proposed GA is capable of efficiently solving problems associated with the management of portfolios.