Discrete-valued, stochastic-constrained simulation optimization with compass

Authors:
Hélcio Vieira;Junior Karl Heinz Kienitz;Mischel Carmen Neyra Belderrain
Affiliations:
Technological Institute of Aeronautics, Praça Marechal Eduardo Gomes, Saão José dos Campos, Brazil;Technological Institute of Aeronautics, Praça Marechal Eduardo Gomes, Saão José dos Campos, Brazil;Technological Institute of Aeronautics, Praça Marechal Eduardo Gomes, Saão José dos Campos, Brazil
Venue:
Proceedings of the Winter Simulation Conference
Year:
2011

Citing 8
Cited 0

Simulation Modeling and Analysis

Simulation Modeling and Analysis
Feature Article: Optimization for simulation: Theory vs. Practice

INFORMS Journal on Computing
Discrete Optimization via Simulation Using COMPASS

Operations Research
A framework for locally convergent random-search algorithms for discrete optimization via simulation

ACM Transactions on Modeling and Computer Simulation (TOMACS)
A kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions

Journal of Global Optimization
Optimizing Call Center Staffing Using Simulation and Analytic Center Cutting-Plane Methods

Management Science
Staffing Multiskill Call Centers via Linear Programming and Simulation

Management Science
Large-deviation sampling laws for constrained simulation optimization on finite sets

Proceedings of the Winter Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose an improvement in the random search algorithm called COMPASS to allow it to deal with a single stochastic constraint. Our algorithm builds on two ideas: (a) a novel simulation allocation rule and (b) the proof that this new simulation allocation rule does not affect the asymptotic local convergence of the COMPASS algorithm. It is shown that the stochastic-constrained COMPASS has a competitive performance in relation to other well known algorithms found in the literature for discrete-valued, stochastic-constrained simulation problems.