Hybrid simulation–optimization methods: A taxonomy and discussion
The possibilities of combining simulation and optimization are vast and the appropriate
design highly depends on the problem characteristics. Therefore, it is very important to have …
design highly depends on the problem characteristics. Therefore, it is very important to have …
Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines
This study develops a standardised checklist approach to improve the reporting of discrete-
event simulation, system dynamics and agent-based simulation models within the field of …
event simulation, system dynamics and agent-based simulation models within the field of …
Simulation optimization: a review on theory and applications
W Long-Fei, SHI Le-Yuan - Acta Automatica Sinica, 2013 - Elsevier
Simulation optimization is a very powerful tool in analysis and optimization of complex real
systems. In this paper, a tutorial introduction and review of simulation optimization are given …
systems. In this paper, a tutorial introduction and review of simulation optimization are given …
A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks
As revealed by the no free lunch theorem, no single algorithm can outperform any others on
all classes of optimization problems. To tackle this issue, methods for recommending an …
all classes of optimization problems. To tackle this issue, methods for recommending an …
Diagnostic tools for evaluating and comparing simulation-optimization algorithms
DJ Eckman, SG Henderson… - INFORMS Journal on …, 2023 - pubsonline.informs.org
Simulation optimization involves optimizing some objective function that can only be
estimated via stochastic simulation. Many important problems can be profitably viewed …
estimated via stochastic simulation. Many important problems can be profitably viewed …
ASTRO-DF: A class of adaptive sampling trust-region algorithms for derivative-free stochastic optimization
We consider unconstrained optimization problems where only “stochastic” estimates of the
objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo …
objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo …
An introduction to multiobjective simulation optimization
The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective
optimization problem in which multiple simultaneous and conflicting objective functions can …
optimization problem in which multiple simultaneous and conflicting objective functions can …
Constrained optimization in expensive simulation: Novel approach
JPC Kleijnen, W Van Beers… - European journal of …, 2010 - Elsevier
This article presents a novel heuristic for constrained optimization of computationally
expensive random simulation models. One output is selected as objective to be minimized …
expensive random simulation models. One output is selected as objective to be minimized …
Stochastically constrained ranking and selection via SCORE
Consider the context of constrained Simulation Optimization (SO); that is, optimization
problems where the objective and constraint functions are known through dependent Monte …
problems where the objective and constraint functions are known through dependent Monte …
Optimal sampling laws for stochastically constrained simulation optimization on finite sets
Consider the context of selecting an optimal system from among a finite set of competing
systems, based on a “stochastic” objective function and subject to multiple “stochastic” …
systems, based on a “stochastic” objective function and subject to multiple “stochastic” …