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Assignment of collaborators to multiple business problems using genetic algorithm

Author

Listed:
  • Keunho Choi

    (Korea Workers’ Compensation and Welfare Service)

  • Gunwoo Kim

    (Hanbat National University)

  • Yongmoo Suh

    (Korea University)

  • Donghee Yoo

    (Gyeongsang National University)

Abstract

As firms encounter new problems in the fast-changing business environment, they have to find collaborators with problem-solving expertise. Since this optimization problem takes place in a firm as the business environment changes, genetic algorithm (GA), which has shown outstanding performance in obtaining a sub-optimal solution relatively quickly, seems to be the right solution, one that is superior to goal-programming, multi-attribute decision making, and branch and bound. We therefore propose a GA-based approach to solving the problem of assigning collaborators to multiple business problems. Our solution worked well in several experiments.

Suggested Citation

  • Keunho Choi & Gunwoo Kim & Yongmoo Suh & Donghee Yoo, 0. "Assignment of collaborators to multiple business problems using genetic algorithm," Information Systems and e-Business Management, Springer, vol. 0, pages 1-19.
    • Handle: RePEc:spr:infsem:v::y::i::d:10.1007_s10257-016-0328-5
    DOI: 10.1007/s10257-016-0328-5
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    References listed on IDEAS

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    1. Wu, Desheng Dash & Zhang, Yidong & Wu, Dexiang & Olson, David L., 2010. "Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach," European Journal of Operational Research, Elsevier, vol. 200(3), pages 774-787, February.
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    4. Feng, Bo & Jiang, Zhong-Zhong & Fan, Zhi-Ping & Fu, Na, 2010. "A method for member selection of cross-functional teams using the individual and collaborative performances," European Journal of Operational Research, Elsevier, vol. 203(3), pages 652-661, June.
    5. Ip, W. H. & Yung, K. L. & Wang, Dingwei, 2004. "A branch and bound algorithm for sub-contractor selection in agile manufacturing environment," International Journal of Production Economics, Elsevier, vol. 87(2), pages 195-205, January.
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