Handling stochastic constraints in discrete optimization via simulation

Authors:
Chuljin Park;Seong-Hee Kim
Affiliations:
Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA
Venue:
Proceedings of the Winter Simulation Conference
Year:
2011

Citing 6
Cited 0

Accelerating the convergence of random search methods for discrete stochastic optimization

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Feature Article: Optimization for simulation: Theory vs. Practice

INFORMS Journal on Computing
A combined procedure for optimization via simulation

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Discrete Optimization via Simulation Using COMPASS

Operations Research
Nested Partitions Method, Theory and Applications

Nested Partitions Method, Theory and Applications
Finding feasible systems in the presence of constraints on multiple performance measures

ACM Transactions on Modeling and Computer Simulation (TOMACS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a discrete optimization via simulation problem with stochastic constraints on secondary performance measures where both objective and secondary performance measures need to be estimated by simulation. To solve the problem, we present a method called penalty function with memory (PFM), which determines a penalty value for a solution based on history of feasibility check on the solution. PFM converts a DOvS problem with stochastic constraints into a series of new optimization problems without stochastic constraints so that an existing DOvS algorithm can be applied to solve the new problem.