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Some algorithms to solve a bi-objectives problem for team selection


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Ngo, TS, Bui, NA, Tran, TT, Le, PC, Bui, DC, Nguyen, The Duy, Phan, LD, Kieu, QT, Nguyen, BS and Tran, SN 2020 , 'Some algorithms to solve a bi-objectives problem for team selection' , Applied Sciences, vol. 10, no. 8 , pp. 1-19 , doi: 10.3390/APP10082700.

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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 (kh) 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.

Item Type: Article
Authors/Creators:Ngo, TS and Bui, NA and Tran, TT and Le, PC and Bui, DC and Nguyen, The Duy and Phan, LD and Kieu, QT and Nguyen, BS and Tran, SN
Keywords: DC, DCA, genetic algorithm, MIQP, team selection, compromise programming, optimization
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
DOI / ID Number: 10.3390/APP10082700
Copyright Information:

Copyright 2020 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

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