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

Brester Christina, Ryzhikov Ivan and Semenkin Eugene ()
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Brester Christina: Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation
Ryzhikov Ivan: Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation
Semenkin Eugene: Reshetnev Siberian State University of Science and Technology, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation

Organizacija, 2017, vol. 50, issue 4, 364-373

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 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:50:y:2017:i:4:p:364-373:n:6

DOI: 10.1515/orga-2017-0027

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