|Title:||Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving|
|Authors:||ID Brester, Christina (Author)|
ID Ryzhikov, Ivan (Author)
ID Semenkin, Eugene (Author)
|Files:|| Organizacija_2017_Brester,_Ryzhikov,_Semenkin_Multi-objective_Optimization_Algorithms_with_the_Island_Metaheuristic_for_Effective_Projec.pdf (993,98 KB)|
|Work type:||Scientific work (r2)|
|Typology:||1.01 - Original Scientific Article|
|Organization:||FOV - Faculty of Organizational Sciences in Kranj|
|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|
|Year of publishing:||2017|
|Publication status in journal:||Published|
|Article version:||Publisher's version of article|
|Number of pages:||str. 364-373|
|Numbering:||Letn. 50, št. 4|
|ISSN on article:||1581-1832|
|Publication date in DKUM:||04.05.2018|
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