Robust discrete optimization and network flows

By D. Bertsimas, Melvyn Sim. Mathematical programming, 2003

1,852 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/062dcc05797a513384c0c676e9afa960a00a78fe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Robust Linear Optimization With Recourse (2009, methodology): “This issue of over-conservatism hindered the adoption of robust techniques in optimization problems until the mid-1990s, when Ben-Tal and Nemirovski [3, 4, 5], El Ghaoui and Lebret [21] and El Ghaoui et al. [22] started investigating tractable robust counterparts of linear, semidefinite and other…”
Infrastructure Planning for Electric Vehicles with Battery Swapping (2013, background): “The theory of robust optimization (see, e.g., [5] [6] [10] [11]) may help achieve this objective.”
A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty (2008, methodology): “We follow the robust optimization methodology as introduced by Ben-Tal and Nemirovski (1998, 1999) and ElGhaoui et al. (1998) for linear quadratic and general convex programs, that were subsequently extended to integer programming by Bertsimas and Sim (2003).”
Modeling Heterogeneous Risk-Taking Behavior in Route Choice: A Stochastic Dominance Approach (2011, background): “Bertsimas and Sim (2003) studied a robust optimization model for general network flow problems without considering correlations, and proposed a polynomial algorithm to find robust shortest paths.”
Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach (2014, methodology): “, / ∈ 2 have to be added to restraints of the model [36].”
A Robust Optimization Perspective on Stochastic Programming (2007, background): “The proofs of (a), (b), and (c) are shown in Bertsimas and Sim (2006). (d) It is well known that the dual norm of the Euclidian norm is also the Euclidian norm, i.”
A hybrid multi-stage stochastic programming-robust optimization model for maximizing the supply chain of a forest-based biomass power plant considering uncertainties (2016, methodology): “These approaches range from scenario analysis to more advanced techniques such as stochastic programming and robust optimization (Birge and Louveaux, 1997, Bertsimas and Sim, 2003).”
Theory and Applications of Robust Optimization (2010, background): “Bertsimas and Sim [33], however, present a model for cost uncertainty in which each coeﬃcient c j is allowed to vary within the interval [¯ c j , ¯ c j + d j ], with no more than Γ ≥ 0 coeﬃcients allowed to vary.”
Offering Strategy Via Robust Optimization (2011, methodology): “Moreover, we combine these confidence intervals for price forecasts with robust optimization techniques (as explained in [3]) to achieve efficacious offering curves.”