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Ordinal Optimization with Generalized Linear Model
2020 Winter Simulation Conference (WSC), 2020Given a number of stochastic systems, we consider an ordinal optimization problem to find an optimal allocation of a finite sampling budget, which maximizes the likelihood of selecting the "best" system, where the "best" is defined as the one with the highest mean.
Dohyun Ahn, Dongwook Shin
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Overview of ordinal optimization
Proceedings of 1994 33rd IEEE Conference on Decision and Control, 2002Concerns optimisation in the general sense of making things better, the principal driver behind all of prescriptive scientific and engineering endeavor. In particular, discrete-event dynamic systems are considered. The approach followed is based on the principles that much computation can be shared in the case of structurally similar systems; that ...
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Multi-objective ordinal optimization for simulation optimization problems
Automatica, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Teng, S., Hay Lee, L., Peng Chew, E.
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An ordinal optimization approach to optimal control problems
Automatica, 1999The paper deals with a discrete stochastic optimization problem of the type: Find \[ \min_{\theta \in \Theta} J(\theta), \] where \( J(\theta) ={\mathbb E} [L (x(t;\theta, \xi))]\); \(L (x(t;\theta, \xi))\) represents some sample performance function evaluated through the realization of a system trajectory \(x((t;\theta, \xi)) \) under the design ...
Deng, Mei, Ho, Yu-Chi
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Ordinal Optimization for Dynamic Network Reconfiguration
Electric Power Components and Systems, 2011Abstract Motivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the ...
R. El Ramli, M. Awad, R. A. Jabr
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Iterative ordinal optimization and its applications
Proceedings of the 36th IEEE Conference on Decision and Control, 2002Ordinal optimization (OO) is a method of speeding up the process of stochastic optimization via parametric simulation. However, OO still has limitations as it stands. One drawback is the fact that the search space for many problems can be huge due to combinatorial explosion.
null Mei Deng, null Yu-Chi Ho
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Ordinal SSA by Iterative Optimization
1987We consider how to solve an ordinal SSA problem by computation. First, an arbitrary configuration is chosen and each of its points is defined to represent a particular object of observation. Then, the distances are measured, and their order is compared with the order of the data. By permuting the distances into the order of the data, we obtain the rank
I. Borg, J. Lingoes
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On Kaplun's optimal co-ordinates
Journal of Fluid Mechanics, 1982Optimal co-ordinates were introduced by Kaplun (1954) as a result of his study on the role of co-ordinate systems in boundary-layer theory. In this paper the basic ideas of optimal co-ordinates are examined and many restrictions of Kaplun's optimal co-ordinates are removed.
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COMBINING EVOLUTION STRATEGY WITH ORDINAL OPTIMIZATION
International Journal of Information Technology & Decision Making, 2013In this paper, we combine evolution strategy (ES) with ordinal optimization (OO), abbreviated as ES + OO, to solve real-time combinatorial stochastic simulation optimization problems with huge discrete solution space. The first step of ES + OO is to use an artificial neural network (ANN) to construct a surrogate model to roughly evaluate the objective ...
SHIH-CHENG HORNG, SHIN-YEU LIN
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Constrained Ordinal Optimization—A Feasibility Model Based Approach
Discrete Event Dynamic Systems, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guan, Xiaohong +3 more
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