We present a review of methods for optimizing stochastic systems using simulation. The focus is on gradient-based techniques for optimization with respect to continuous decision parameters and on random search methods for optimization with respect to discrete decision parameters.
International audienceThe papers in this special issue seek to report cutting edge research on stoch...
A methodology for optimization of simulation models is presented. The methodology is based on a gene...
We consider optimizing a stochastic system, given only a simulation model that is parameterized by c...
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation o...
Simulation optimization is a very powerful tool in analysis and optimization of complex real systems...
In this chapter, we describe, the structure of the stochastic optimization solver SQG (Stochastic Qu...
We extend the idea of model-based algorithms for deterministic optimization to simulation optimizati...
Both the simulation research and software communities have been interested in optimization via simul...
Abstract Both the simulation research and software communities have been interested in optimization ...
It is frequently the case that deterministic optimization models could be made more practical by exp...
We present a general framework for applying simulation to optimize the behavior of discrete event sy...
Model-based optimization methods are effective for solving optimization problems with little structu...
We consider optimizing a stochastic system, given only a simulation model that is parameterized by c...
The optimization of stochastic Discrete Event Systems (DESs) is a critical and diffcult task. Beside...
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimen...
International audienceThe papers in this special issue seek to report cutting edge research on stoch...
A methodology for optimization of simulation models is presented. The methodology is based on a gene...
We consider optimizing a stochastic system, given only a simulation model that is parameterized by c...
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation o...
Simulation optimization is a very powerful tool in analysis and optimization of complex real systems...
In this chapter, we describe, the structure of the stochastic optimization solver SQG (Stochastic Qu...
We extend the idea of model-based algorithms for deterministic optimization to simulation optimizati...
Both the simulation research and software communities have been interested in optimization via simul...
Abstract Both the simulation research and software communities have been interested in optimization ...
It is frequently the case that deterministic optimization models could be made more practical by exp...
We present a general framework for applying simulation to optimize the behavior of discrete event sy...
Model-based optimization methods are effective for solving optimization problems with little structu...
We consider optimizing a stochastic system, given only a simulation model that is parameterized by c...
The optimization of stochastic Discrete Event Systems (DESs) is a critical and diffcult task. Beside...
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimen...
International audienceThe papers in this special issue seek to report cutting edge research on stoch...
A methodology for optimization of simulation models is presented. The methodology is based on a gene...
We consider optimizing a stochastic system, given only a simulation model that is parameterized by c...