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

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