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Systematic evolutionary algorithm for general multilevel Stackelberg problems with bounded decision variables (SEAMSP)

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  • Ashenafi Woldemariam
  • Semu Kassa

Abstract

Multilevel Stackelberg problems are nested optimization problems which reply optimally to hierarchical decisions of subproblems. These kind of problems are common in hierarchical decision making systems and are known to be NP-hard. In this paper, a systematic evolutionary algorithm has been proposed for such types of problems. A unique feature of the algorithm is that it is not affected by the nature of the objective and constraint functions involved in the problem as long as the problem has a solution. The convergence proof of the proposed algorithm is given for special problems containing non-convex and non-differentiable functions. Moreover, a new concept of $$(\varepsilon ,\delta )$$ ( ε , δ ) -approximation for Stackelberg solutions is defined. Using this definition comparison of approximate Stackelberg solutions has been studied in this work. The numerical results on various problems demonstrated that the proposed algorithm is very much promising to multilevel Stackelberg problems with bounded constraints, and it can be used as a benchmark for a comparison of approximate results by other algorithms. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Ashenafi Woldemariam & Semu Kassa, 2015. "Systematic evolutionary algorithm for general multilevel Stackelberg problems with bounded decision variables (SEAMSP)," Annals of Operations Research, Springer, vol. 229(1), pages 771-790, June.
    • Handle: RePEc:spr:annopr:v:229:y:2015:i:1:p:771-790:10.1007/s10479-015-1842-4
    DOI: 10.1007/s10479-015-1842-4
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    Cited by:

    1. Natnael Nigussie Goshu & Semu Mitiku Kassa, 2024. "A solution method for stochastic multilevel programming problems. A systematic sampling evolutionary approach," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(1), pages 149-174.
    2. Burcu Beykal & Styliani Avraamidou & Ioannis P. E. Pistikopoulos & Melis Onel & Efstratios N. Pistikopoulos, 2020. "DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems," Journal of Global Optimization, Springer, vol. 78(1), pages 1-36, September.
    3. Tilahun, Surafel Luleseged, 2019. "Feasibility reduction approach for hierarchical decision making with multiple objectives," Operations Research Perspectives, Elsevier, vol. 6(C).

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