research-article

Simulation-based optimization over discrete sets with noisy constraints

Authors:

Eunji LimAuthors Info & Claims

WSC '11: Proceedings of the Winter Simulation Conference

Pages 4013 - 4025

Published: 11 December 2011 Publication History

Abstract

We consider a constrained optimization problem over a discrete set where noise--corrupted observations of the objective and constraints are available. The problem is challenging because the feasibility of a solution cannot be known for certain, due to the noisy measurements of the constraints. To tackle this issue, we propose a new method that converts constrained optimization into the unconstrained optimization problem of finding a saddle point of the Lagrangian. The method applies stochastic approximation to the Lagrangian in search of the saddle point. The proposed method is shown to converge, under suitable conditions, to the optimal solution almost surely (a.s.) as the number of iterations grows. We present the effectiveness of the proposed method numerically in two settings: (1) inventory control in a periodic review system, and (2) staffing in a call center.

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Cited By

Kiss TDagdeviren HTaylor SAnagnostou AFantini N(2015)Business models for cloud computingProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888925(2656-2667)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888925
Jian NHenderson S(2015)An introduction to simulation optimizationProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888819(1780-1794)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888819
Rai SEttam RHill RKuhl M(2013)Simulation-based optimization using simulated annealing for optimal equipment selection within print production environmentsProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676122(1097-1108)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676122

Index Terms

Simulation-based optimization over discrete sets with noisy constraints
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
    2. Simulation types and techniques
      1. Discrete-event simulation

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Information

Published In

cover image ACM Conferences

WSC '11: Proceedings of the Winter Simulation Conference

December 2011

4336 pages

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
SCS: Shanghai Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Winter Simulation Conference

Publication History

Published: 11 December 2011

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WSC'11

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IIE
INFORMS-SIM
SCS
SIGSIM

WSC'11: Winter Simulation Conference 2011

December 11 - 14, 2011

Arizona, Phoenix

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WSC '11 Paper Acceptance Rate 203 of 270 submissions, 75%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

Kiss TDagdeviren HTaylor SAnagnostou AFantini N(2015)Business models for cloud computingProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888925(2656-2667)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888925
Jian NHenderson S(2015)An introduction to simulation optimizationProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888819(1780-1794)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888819
Rai SEttam RHill RKuhl M(2013)Simulation-based optimization using simulated annealing for optimal equipment selection within print production environmentsProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676122(1097-1108)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676122

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