The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study

By B. Verweij, Shabbir Ahmed, Anton J. Kleywegt, George L. Nemhauser, Alexander Shapiro. Computational optimization and applications, 2003

517 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/ef6b8c0db984b09bfb07ed31a83919dc8aee10d4

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In Industrial and Manufacturing Engineering

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Citations in the corpus

Monte Carlo sampling-based methods for stochastic optimization (2014, background): “[236]), scheduling problems (Morton and Popova [161], Turner et al.”
On Complexity of Stochastic Programming Problems (2005, methodology): “This was verified in a number of numerical experiments (e.g., [LSW05, MMW99, SAGS05, VAKNS03 ]).”
Stochastic Approximation approach to Stochastic Programming (2013, methodology): “, [7, 8, 19]) show that the SAA method coupled with a good (deterministic) algorithm could be reasonably efficient for solving certain classes of two stage stochastic programming problems.”
Reliability constrained multi-area adequacy planning using stochastic programming with sample-average approximations (2008, methodology): “The derivation of lower and upper bound confidence interval was presented in [12] and has been applied in [1] and [ 8 ].”
Dealing with asynchronicity in parallel Gaussian Process based global optimization (2010, methodology): “Numerical experiments with the SAA method are reported in [50, 51, 83], for example.”
Validation analysis of mirror descent stochastic approximation method (2012, methodology): “, [5,6,17]) show that the SAA method coupled with a good deterministic algorithm for minimizing the constructed SAA problem could be reasonably efficient for solving certain classes of SP problems.”
Convex optimization under inexact first-order information (2009, methodology): “, [36, 40, 74]) show that the SAA method coupled with a good (deterministic) algorithm could be reasonably efficient for solving certain classes of two stage stochastic programming problems.”
Robust Stochastic Approximation Approach to Stochastic Programming (2008, methodology): “Recent theoretical studies (cf. [ 11, 25, 26]) and numerical experiments (see, e.g., [12, 13, 29]) show that the SAA method coupled with a good (deterministic) algorithm could be reasonably eﬃcient for solving certain classes of two-stage stochastic programming problems.”
Lectures on Stochastic Programming: Modeling and Theory (2009, methodology): “Numerical experiments with the SAA approach, applied to linear and discrete (integer) stochastic programming problems, can be also found in more recent publications [3],[146],[149],[265].”
A review of dynamic vehicle routing problems (2013, background): “Uncertainty may affect any of the input data, yet the three most studied cases are [33]: stochastic customers, where a customer needs to be serviced with a given probability [15,147]; stochastic times, in which either service or travel times are modeled by random variables [79,85,146]; and lastly, s”