Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

Abstract

Background and Purpose:In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.Keywords: 0-1 multi-objective constrained knapsack problem; project management portfolio problem; multi-objective evolution-based optimization algorithms; collaborative and cooperative meta-heuristics

Project portfolio risk management: a structured literature review with future directions for research

“…Modern Portfolio Theory [62][63][64][65][66] This model uses the trade-off between risk and returns to find the efficient frontier. It is based on the traditional Markowitz mean-variance model, but other risk measures have been implemented in order to optimize a portfolio of non-divisible projects.…”

Project portfolio risk management. Bibliometry and collaboration Scientometric domain analysis

“…PPRM originates from the foundational concept of project portfolios, stemming from Markowitz's Modern Portfolio Theory (MPT) [ [27] , [28] , [29] ]. Initially, PPRM centered on infusing Risk Assessment into Project Portfolio selection, akin to financial portfolio structuring [ 28 ], primarily addressing the initial phase of Project Portfolio Management, known as Project Portfolio Structuring.…”

On Island Model Performance for Cooperative Real-Valued Multi-objective Genetic Algorithms

“…We propose using an island model cooperation wherein each algorithm works independently from the others and after a certain number of iterations, the algorithms exchange their best solutions. The proposed approach has been applied in various problems and there are results proving its high performance [11,12].…”