Showing 1 - 10 of 16
This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....
Persistent link: https://www.econbiz.de/10014185812
This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...
Persistent link: https://www.econbiz.de/10014049484
This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespecified target values. Besides the simulation outputs, the...
Persistent link: https://www.econbiz.de/10014212782
Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with...
Persistent link: https://www.econbiz.de/10014166285
In this paper we investigate global optimization for black-box simulations using metamodels to guide this optimization. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic simulation we study the famous 'e fficient global...
Persistent link: https://www.econbiz.de/10014141513
This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design fixes the input...
Persistent link: https://www.econbiz.de/10012956205
This paper uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....
Persistent link: https://www.econbiz.de/10013141684
Efficient Global Optimization (EGO) is a popular method that searches sequentially for the global optimum of a simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic simulation, EGO uses ordinary Kriging (OK), which is a special...
Persistent link: https://www.econbiz.de/10013017371
We study the correct estimation of the true variance of the predictor in stochastic Kriging (SK). First, we obtain macroreplications for a SK metamodel that approximates a single-server simulation model; these macroreplications give independently and identically distributed predictions. This...
Persistent link: https://www.econbiz.de/10013017383
An important goal of simulation is optimization of the corresponding real system. We focus on simulation models with multiple responses (out-puts), selecting one response as the variable to be maximized or minimized while the remaining responses satisfy prespecified thresholds; i.e., we focus on...
Persistent link: https://www.econbiz.de/10013321790