20.500.12556/DKUM-70350 Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving Algoritmi za optimizacijo več ciljev z metaheuristiko otoka za učinkovito reševanje problema vodenja projektov 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. Ozadje in namen: V vsaki organizaciji vodenje projektov odpira številne in različne probleme odločanja, katerih velik del je mogoče učinkovito rešiti s pomočjo posebnih sistemov za podporo odločanju. Takšni problemi vedno predstavljajo izziv, saj za njihovo kompleksnost ni časovno ali računsko učinkovitega algoritma. V članku obravnavamo problem optimalnih finančnih naložb. V naši rešitvi upoštevamo naslednje organizacijske vire in značilnosti projekta: dobiček, stroške in tveganja. Zasnova / metodologija / pristop: Problem odločanja je formuliran kot večkriterialni problem 0-1 nahrbtnika. To pomeni, da moramo poiskati nedominantno množico alternativnih rešitev kot kompromis med maksimiranjem dohodkov in zmanjševanjem tveganj. Obenem pa morajo alternative zadoščati omejitvam. To vodi k omejenemu problemu dvokriterialne optimizacije v Boolovem prostoru. Da bi obvladali posebnostmi in visoko zapletenost problema, smo kot alternativo običajnim tehnikam uporabili evolucijske algoritme z meta-hevristiko otoka. Rezultati: Problem smo formulirali kot neomejeno dvokriterijsko optimizacijo in ga rešili z različnimi heurističnimi optimizacijami, ki temeljijo na evoluciji. Nato smo predlagali meta-hevristiko, ki združuje specifične algoritme in dosega njihovo interakcijo na sodelovalni način. Dobljeni rezultati so pokazali, da je hevristika otoka presegla rezultate, dobljene na podlagi vrednosti specifične metrike, s čimer se je pokazala reprezentativnost Paretovih prednjih aproksimacij. Bolj reprezentativni približki omogočajo nosilcem odločanja več alternativnih projektnih portfeljev, ki ustrezajo različnim ocenam tveganja in dobička. Ker so ti kriteriji v nasprotju, pri izbiri alternative z ocenjenim visokim dobičkom nosilci odločanja sledijo strategiji z ocenjenim tveganjem in obratno. Zaključek: V članku smo problem reševanja portfeljev projektov formulirali kot problem večciljne optimizacije 0-1 nahrbtnika z omejitvami. Analiza algoritma potrjuje, da uporaba meta-hevristike otoka bistveno izboljšala učinkovitost genetskih algoritmov in tako predstavlja učinkovito orodje za upravljanje centrov za finančno odgovornost. 0-1 multi-objective constrained knapsack problem project management portfolio problem multi-objective evolution-based optimization algorithms collaborative and cooperative meta-heuristics večkriterialni problem 0-1 nahrbtnika problem portfelja projektov večciljni evolucijski algoritmi za optimizacijo sodelovalna meta-hevristika true false true Angleški jezik Slovenski jezik Znanstveno delo 2018-05-04 14:55:07 2018-05-04 14:56:01 2023-07-24 13:33:41 2019-07-11 18:04:14 2017 0 0 str. 364-373 št. 4 Letn. 50 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1581-1832 251341568 298472192 10.1515/orga-2017-0027 1581-1832 URN:SI:UM:DK:DGQWIBIB Organizacija_2017_Brester,_Ryzhikov,_Semenkin_Multi-objective_Optimization_Algorithms_with_the_Island_Metaheuristic_for_Effective_Projec.pdf Organizacija_2017_Brester,_Ryzhikov,_Semenkin_Multi-objective_Optimization_Algorithms_with_the_Island_Metaheuristic_for_Effective_Projec.pdf 1 326C88C4FEF4CDEEEEBD6979FFDF8C71 a286d13d84d59e6dfe395b0e486ef7add4f8a5e221ca3fcd5d17fd10c99c0163 ede10722-7c0f-11eb-bb7a-00155d0001ca 20.500.12556/dkum/ed42e923-1cd2-48d2-9869-b765cdb4ec11 https://dk.um.si/Dokument.php?lang=slv&id=124339 https://www.degruyter.com/view/j/orga.2017.50.issue-4/orga-2017-0027/orga-2017-0027.xml 1 https://dk.um.si/Dokument.php?lang=slv&id=124338 Fakulteta za organizacijske vede Organizacija 0 0 0