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APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
13 years 11 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál
WCE
2007
13 years 6 months ago
Scenario Generation Employing Copulas
—Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on scenario generation model about future...
Kristina Sutiene, Henrikas Pranevicius
AAAI
2012
11 years 7 months ago
Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning
We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land ...
Akshat Kumar, XiaoJian Wu, Shlomo Zilberstein
MANSCI
2008
128views more  MANSCI 2008»
13 years 5 months ago
Optimizing Call Center Staffing Using Simulation and Analytic Center Cutting-Plane Methods
We consider the problem of minimizing staffing costs in an inbound call center, while maintaining an acceptable level of service in multiple time periods. The problem is complicat...
Júlíus Atlason, Marina A. Epelman, S...
CPAIOR
2008
Springer
13 years 7 months ago
Amsaa: A Multistep Anticipatory Algorithm for Online Stochastic Combinatorial Optimization
The one-step anticipatory algorithm (1s-AA) is an online algorithm making decisions under uncertainty by ignoring future non-anticipativity constraints. It makes near-optimal decis...
Luc Mercier, Pascal Van Hentenryck