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Some metaheuristic algorithms for solving multiple cross-functional team selection problems.

Son Tung Ngo1,2, Jafreezal Jaafar1, Aziz Abdul Izzatdin1

  • 1Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

This study presents an efficient method for complex team selection problems, combining compromise programming with genetic algorithms and ant colony optimization. The new approach significantly improves solution quality and reduces execution time compared to existing solvers.

Keywords:
Ant colony optimizationCPLEX-MIQPCombinatorial optimizationCompromise programmingGenetic algorithmMulti objective optimizationTeam selection

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Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • Team selection is a complex, NP-Hard combinatorial problem requiring efficient search algorithms.
  • Multi-goal decision-making complicates traditional team selection processes.
  • Existing methods struggle to balance solution quality with reasonable execution times.

Purpose of the Study:

  • To introduce a novel model for selecting multiple cross-functional teams (CFTs).
  • To address diverse skill requirements, optimizing for both depth and breadth of candidate skills.
  • To develop an efficient optimization method for complex team formation scenarios.

Main Methods:

  • A hybrid approach combining Compromise Programming (CP) with metaheuristic algorithms (Genetic Algorithm - GA, Ant Colony Optimization - ACO).
  • Development and comparison of the proposed algorithms against the MIQP-CPLEX solver.
  • Validation using datasets of 500 programming contestants with 37 skills and randomized distributions.

Main Results:

  • The proposed GA and ACO-based algorithms significantly outperformed the MIQP-CPLEX solver.
  • Improvements were observed in both solution quality and execution time.
  • The developed method proved effective for multi-criteria decision-making, outperforming Multi-Objective Evolutionary Algorithms (MOEA).

Conclusions:

  • The integrated CP and metaheuristic approach offers a superior solution for complex team selection.
  • This method enhances efficiency and solution quality in multi-objective optimization for team formation.
  • The study validates the effectiveness of advanced computational methods for practical team selection challenges.