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

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posted on 2025-01-08, 03:53 authored by TS Ngo, NA Bui, TT Tran, PC Le, DC Bui, TD Nguyen, LD Phan, QT Kieu, BS Nguyen, Son TranSon Tran
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.

History

Journal

Applied Sciences (Switzerland)

Volume

10

Article number

2700

Pagination

1-19

Location

Basel, Switzerland

Open access

  • Yes

ISSN

2076-3417

eISSN

2076-3417

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

8

Publisher

MDPI