An algorithm for binary linear chance-constrained problems using IIS
Gianpiero Canessa
, Julian Gallego
, Lewis Ntaimo
,
2019, Computational Optimization and Applications, 72(3), pp.589-608
Chance-constrained programming
Infeasible irreducible subsystems
Integer programming
Abstract
We propose an algorithm based on infeasible irreducible subsystems (IIS) 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.