Decomposition for adjustable robust linear optimization subject to uncertainty polytope

• Published in: Computational management science Vol. 13; no. 2; pp. 219 - 239
• Main Authors: Ayoub, Josette, Poss, Michael
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2016; Springer Nature B.V; Springer Verlag
• Subjects: Adjustable; Algorithms; Approximation; Budgeting; Business and Management; Computer Science; Decomposition; Linear programming; Operations Research; Operations Research/Decision Theory; Optimization; Optimization techniques; Polytopes; Separation; Studies; Uncertainty; United States > US; Adjustable robust optimization; Network design; Uncertainty polytope; Benders decomposition; Mixed-integer linear programming

We present in this paper a general decomposition framework to solve exactly adjustable robust linear optimization problems subject to polytope uncertainty. Our approach is based on replacing the polytope by the set of its extreme points and generating the extreme points on the fly within row generat...