Evaluating variance reduction techniques within a sample average approximation method for a constrained inventory policy optimization problem

Authors:
Yasin Ünlü;Manuel D. Rossetti
Affiliations:
The University of Arkansas, Fayetteville, AR;The University of Arkansas, Fayetteville, AR
Venue:
Proceedings of the Winter Simulation Conference
Year:
2011

Citing 6
Cited 0

An efficient algorithm for computing an optimal (r,Q) policy in continuous review stochastic inventory systems

Operations Research
Simulation Modeling and Analysis

Simulation Modeling and Analysis
The Sample Average Approximation Method for Stochastic Discrete Optimization

SIAM Journal on Optimization
The impact of sampling methods on bias and variance in stochastic linear programs

Computational Optimization and Applications
Scaling up the sample average approximation method for stochastic optimization with applications to trading agents

AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
Monte Carlo bounding techniques for determining solution quality in stochastic programs

Operations Research Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper examines a constrained stochastic inventory optimization problem by means of sample average approximations (SAA). The problem is formulated based on the lead time demand parameters. Lead time demands are sampled by a bootstrap method that is performed by randomly generating demand values over deterministic lead time values. In order to increase the efficiency of solving an SAA replication, a number of variance reduction techniques (VRT) are proposed, namely: antithetic variates, common random numbers and Latin hypercube sampling methods. A set of experiments investigates the quality of these VRTs on the estimated optimality gap and gap variance results for different demand processes. The results indicate that the use of VRTs produces significant improvements over the crude Monte Carlo sampling method on all test cases.