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A branch-and-bound method for discretely-constrained mathematical programs with equilibrium constraints

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  • Yohan Shim
  • Marte Fodstad
  • Steven Gabriel
  • Asgeir Tomasgard

Abstract

We present a branch-and-bound algorithm for discretely-constrained mathematical programs with equilibrium constraints (DC-MPEC). This is a class of bilevel programs with an integer program in the upper-level and a complementarity problem in the lower-level. The algorithm builds on the work by Gabriel et al. (Journal of the Operational Research Society 61(9):1404–1419, 2010 ) and uses Benders decomposition to form a master problem and a subproblem. The new dynamic partition scheme that we present ensures that the algorithm converges to the global optimum. Partitioning is done to overcome the non-convexity of the Benders subproblem. In addition Lagrangean relaxation provides bounds that enable fathoming in the branching tree and warm-starting the Benders algorithm. Numerical tests show significantly reduced solution times compared to the original algorithm. When the lower level problem is stochastic our algorithm can easily be further decomposed using scenario decomposition. This is demonstrated on a realistic case. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Yohan Shim & Marte Fodstad & Steven Gabriel & Asgeir Tomasgard, 2013. "A branch-and-bound method for discretely-constrained mathematical programs with equilibrium constraints," Annals of Operations Research, Springer, vol. 210(1), pages 5-31, November.
    • Handle: RePEc:spr:annopr:v:210:y:2013:i:1:p:5-31:10.1007/s10479-012-1191-5
    DOI: 10.1007/s10479-012-1191-5
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    Cited by:

    1. Yu Su & Niancheng Zhou & Qianggang Wang & Chao Lei & Jian Fang, 2018. "Optimal Planning Method of On-load Capacity Regulating Distribution Transformers in Urban Distribution Networks after Electric Energy Replacement Considering Uncertainties," Energies, MDPI, vol. 11(6), pages 1-25, June.
    2. Hecheng Li, 2015. "A genetic algorithm using a finite search space for solving nonlinear/linear fractional bilevel programming problems," Annals of Operations Research, Springer, vol. 235(1), pages 543-558, December.
    3. Yogendra Pandey & S. K. Mishra, 2018. "Optimality conditions and duality for semi-infinite mathematical programming problems with equilibrium constraints, using convexificators," Annals of Operations Research, Springer, vol. 269(1), pages 549-564, October.
    4. Kerstin Dächert & Sauleh Siddiqui & Javier Saez-Gallego & Steven A. Gabriel & Juan Miguel Morales, 2019. "A Bicriteria Perspective on L-Penalty Approaches – a Corrigendum to Siddiqui and Gabriel’s L-Penalty Approach for Solving MPECs," Networks and Spatial Economics, Springer, vol. 19(4), pages 1199-1214, December.
    5. Emmanuel Ogbe & Xiang Li, 2019. "A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 75(3), pages 595-629, November.

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