Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review

Abstract

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.

Using the deterministic approach model for project portfolio selection problem (PPSP) solutions

“…In the interim, employment opportunities will be created for the citizens of each community in terms of jobs and small businesses for the economic development of the communities and the entire region, and which will also improve their quality of life. In the study of [ 49 ] less than 50 projects have been proposed using goal programming in project portfolio selection problems. However, following conclusions were made from this study by using AHP integrated with GP model: The Analytic hierarchy process (AHP) and Goal programming (GP) model could be used to plan project selection according to priorities.…”