eCite Digital Repository

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


Ngo, TS and Bui, NA and Tran, TT and Le, PC and Bui, DC and Nguyen, TD and Phan, LD and Kieu, QT and Nguyen, BS and Tran, SN, Some algorithms to solve a bi-objectives problem for team selection, Applied Sciences, 10, (8) Article 2700. ISSN 2076-3417 (2020) [Refereed Article]


Copyright Statement

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

DOI: doi:10.3390/APP10082700


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 Details

Item Type:Refereed Article
Keywords:DC, DCA, genetic algorithm, MIQP, team selection, compromise programming, optimization
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Education and Training
Objective Group:Learner and learning
Objective Field:Learner and learning not elsewhere classified
UTAS Author:Tran, SN (Dr Son Tran)
ID Code:139111
Year Published:2020
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2020-05-27
Last Modified:2020-06-12
Downloads:19 View Download Statistics

Repository Staff Only: item control page