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Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm

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  • Kuo, R.J.
  • Lee, Y.H.
  • Zulvia, Ferani E.
  • Tien, F.C.

Abstract

Bi-level linear programming, consisting of upper level and lower level objectives, is a technique for modeling decentralized decision. This study presents a hybrid of immune genetic algorithm and vector-controlled particle swarm optimization (IGVPSO) to solve the bi-level linear programming problem (BLPP). It is applied to a supply chain model that is a BLPP. Using four problems from the literature and the supply chain distribution models, the computational results indicate that the proposed method is superior to some algorithms.

Suggested Citation

  • Kuo, R.J. & Lee, Y.H. & Zulvia, Ferani E. & Tien, F.C., 2015. "Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1013-1026.
    • Handle: RePEc:eee:apmaco:v:266:y:2015:i:c:p:1013-1026
    DOI: 10.1016/j.amc.2015.06.025
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    References listed on IDEAS

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