An algorithm for binary linear chance-constrained problems using IIS

Gianpiero Canessa (), Julian A. Gallego, Lewis Ntaimo and Bernardo K. Pagnoncelli
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Gianpiero Canessa: Universidad Adolfo Ibañez
Julian A. Gallego: AT Kearney Inc
Lewis Ntaimo: Texas A&M University
Bernardo K. Pagnoncelli: Universidad Adolfo Ibañez

Computational Optimization and Applications, 2019, vol. 72, issue 3, No 3, 589-608

Abstract: Abstract We propose an algorithm based on infeasible irreducible subsystems to solve binary linear chance-constrained problems with random technology matrix. By leveraging on the problem structure we are able to generate good quality upper bounds to the optimal value early in the algorithm, and the discrete domain is used to guide us efficiently in the search of solutions. We apply our methodology to individual and joint binary linear chance-constrained problems, demonstrating the ability of our approach to solve those problems. Extensive numerical experiments show that, in some cases, the number of nodes explored by our algorithm is drastically reduced when compared to a commercial solver.

Keywords: Chance-constrained programming; Infeasible irreducible subsystems; Integer programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-018-00055-9

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