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Open AccessArticle

Some Algorithms to Solve a Bi-Objectives Problem for Team Selection

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Information and Communication Technology Department, FPT University, Hanoi 100000, Vietnam
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Department of Mathematics, FPT University, Hanoi 100000, Vietnam
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The Discipline of ICT, University of Tasmania, Launceston, TAS 7250, Australia
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2700; https://doi.org/10.3390/app10082700
Received: 28 March 2020 / Revised: 10 April 2020 / Accepted: 10 April 2020 / Published: 14 April 2020
In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among ( k h ) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the “deep” and “wide” aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness. View Full-Text
Keywords: DC; DCA; genetic algorithm; MIQP; team selection; compromise programming DC; DCA; genetic algorithm; MIQP; team selection; compromise programming
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Ngo, T.S.; Bui, N.A.; Tran, T.T.; Le, P.C.; Bui, D.C.; Nguyen, T.D.; Phan, L.D.; Kieu, Q.T.; Nguyen, B.S.; Tran, S.N. Some Algorithms to Solve a Bi-Objectives Problem for Team Selection. Appl. Sci. 2020, 10, 2700.

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