| Title: | Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving |
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| Authors: | ID Brester, Christina (Author)
ID Ryzhikov, Ivan (Author)
ID Semenkin, Eugene (Author) |
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| Files: |  Organizacija_2017_Brester,_Ryzhikov,_Semenkin_Multi-objective_Optimization_Algorithms_with_the_Island_Metaheuristic_for_Effective_Projec.pdf (993,98 KB)
MD5: 326C88C4FEF4CDEEEEBD6979FFDF8C71
PID: 20.500.12556/dkum/ed42e923-1cd2-48d2-9869-b765cdb4ec11
 https://www.degruyter.com/view/j/orga.2017.50.issue-4/orga-2017-0027/orga-2017-0027.xml
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FOV - Faculty of Organizational Sciences in Kranj
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| 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. |
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| Keywords: | 0-1 multi-objective constrained knapsack problem, project management portfolio problem, multi-objective evolution-based optimization algorithms, collaborative and cooperative meta-heuristics |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2017 |
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| Number of pages: | str. 364-373 |
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| Numbering: | Letn. 50, št. 4 |
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| PID: | 20.500.12556/DKUM-70350  |
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| ISSN: | 1581-1832 |
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| ISSN on article: | 1581-1832 |
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| COBISS.SI-ID: | 298472192 |
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| DOI: | 10.1515/orga-2017-0027 |
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| NUK URN: | URN:SI:UM:DK:DGQWIBIB |
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| Publication date in DKUM: | 04.05.2018 |
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| Views: | 1571 |
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| Downloads: | 313 |
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| Metadata: |    |
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| Categories: | Misc.
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