Simulation optimization: a review of algorithms and applications
Simulation optimization (SO) refers to the optimization of an objective function subject to
constraints, both of which can be evaluated through a stochastic simulation. To address …
constraints, both of which can be evaluated through a stochastic simulation. To address …
Simulation optimization: a review of algorithms and applications
Simulation optimization refers to the optimization of an objective function subject to
constraints, both of which can be evaluated through a stochastic simulation. To address …
constraints, both of which can be evaluated through a stochastic simulation. To address …
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 …
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” …
Simulation optimization: A concise overview and implementation guide
Simulation optimization (SO) is the problem of optimization in the presence of objective and
constraint functions that can only be observed via a stochastic simulation. SO, owing to its …
constraint functions that can only be observed via a stochastic simulation. SO, owing to its …
Optimal budget allocation policy for tabu search in stochastic simulation optimization
Tabu search (TS) is a powerful method for solving combinatorial optimization problems.
However, when TS is adopted for stochastic simulation optimization, the simulation noises …
However, when TS is adopted for stochastic simulation optimization, the simulation noises …
An introduction to simulation optimization
In this tutorial we give an introduction to simulation optimization, covering its general form,
central issues and common problems, basic methods, and a case study. Our target audience …
central issues and common problems, basic methods, and a case study. Our target audience …