Light Robustness Matteo Fischetti and Michele Monaci
نویسنده
چکیده
We consider optimization problems where the exact value of the input data is not known in advance and can be affected by uncertainty. For these problems, one is typically required to determine a robust solution, i.e., a possibly suboptimal solution whose feasibility and cost is not affected heavily by the change of certain input coefficients. Two main classes of models have been proposed in the literature to handle uncertainty: stochastic programming models (offering great flexibility, but often too large in size to be handled efficiently), and robust optimization models (easier to solve but sometimes leading to very conservative solutions of little practical use). We invertigate a third way to model uncertainty, leading to a modelling framework that we call Light Robustness. Light Robustness couples robust optimization with a simplified two-stage stochastic programming approach, and has a number of important advantages in terms of flexibility and ease to use. In particular, experiments on both random and real word problems show that Light Robustness is often able to produce solutions whose quality is comparable with that obtained through stochastic programming or robust models, though it requires less effort in terms of model formulation and solution time.
منابع مشابه
Tree Search Stabilization by Random Sampling
We discuss the variability in the performance of multiple runs of Mixed Integer Linear solvers, and we concentrate on the one deriving from the use of different optimal bases of the Linear Programming relaxations. We propose a new algorithm exploiting more than one of those bases and we show that different versions of the algorithm can be used to stabilize and improve the performance of the sol...
متن کاملProximity search for 0-1 mixed-integer convex programming
In this paper we investigate the effects of replacing the objective function of a 0-1 Mixed-Integer Convex Program (MIP) with a “proximity” one, with the aim of enhancing the heuristic behavior of a black-box solver. The relationship of this approach with primal integer methods is also addressed. Promising computational results on different proof-of-concept implementations are presented, sugges...
متن کاملImproving branch-and-cut performance by random sampling
We discuss the variability in the performance of multiple runs of branchand-cut mixed integer linear programming solvers, and we concentrate on the one deriving from the use of different optimal bases of the linear programming relaxations. We propose a new algorithm exploiting more than one of those bases and we show that different versions of the algorithmcanbeused to stabilize and improve the...
متن کاملOn the use of intersection cuts for bilevel optimization
We address a generic Mixed-Integer Bilevel Linear Program (MIBLP), i.e., a bilevel optimization problem where all objective functions and constraints are linear, and some/all variables are required to take integer values. We first propose necessary modifications needed to turn a standard branch-and-bound MILP solver into an exact and finitely-convergent MIBLP solver, also addressing MIBLP unbou...
متن کامل