Events2Join

Ranking and selection under input uncertainty


Data-Driven Ranking and Selection Under Input Uncertainty

Abstract. We consider a simulation-based ranking and selection (R&S) problem with input uncertainty, in which unknown input distributions can be ...

Ranking and selection under input uncertainty: A budget allocation ...

Ranking and selection under input uncertainty: A budget allocation formulation. Abstract: A widely acknowledged challenge in ranking and selection is how to ...

Data-driven Ranking and Selection under Input Uncertainty - arXiv

Title:Data-driven Ranking and Selection under Input Uncertainty ... Abstract:We consider a simulation-based Ranking and Selection (R&S) problem ...

Fixed Confidence Ranking and Selection Under Input Uncertainty

When it comes to simulation-based Ranking and Selection (R&S), ignoring IU can lead to the failure of many existing procedures. In this paper, we study a new ...

Data-Driven Ranking and Selection Under Input Uncertainty

input uncertainty, in which unknown input distributions can be estimated using input data arriving in batches of varying sizes over time.

Data-Driven Ranking and Selection Under Input Uncertainty

We consider a simulation-based ranking and selection (R&S) problem with input uncertainty, in which unknown input distributions can be estimated using input ...

Ranking and Selection under Input Uncertainty: A Budget Allocation ...

Page, eds. RANKING AND SELECTION UNDER INPUT UNCERTAINTY: A BUDGET ALLOCATION. FORMULATION. Di Wu. Enlu Zhou. School of Industrial & Systems Engineering.

data-driven ranking and selection under input uncertainty - arXiv

We consider a simulation-based Ranking and Selection (R&S) problem with input uncertainty, where unknown input distributions can be ...

Fixed Confidence Ranking and Selection under Input Uncertainty

In stochastic simulation, input uncertainty (IU) is caused by the error in estimating input distributions using finite real-world data. When it comes to ...

INPUT UNCERTAINTY AND INDIFFERENCE-ZONE RANKING ...

(2014). Corlu and Biller (2013) consider input uncertainty in subset selection, another approach to. R&S that does not involve an IZ parameter. Fan et al ...

[PDF] Data-Driven Ranking and Selection Under Input Uncertainty

Data-Driven Ranking and Selection Under Input Uncertainty · Di Wu, Yuhao Wang, Enlu Zhou · Published in Operational Research 28 August 2017 · Computer Science, ...

Fixed confidence ranking and selection under input uncertainty

In stochastic simulation, input uncertainty (IU) is caused by the error in estimating input distributions using finite real-world data.

Optimizing Input Data Acquisition for Ranking and Selection

This paper concerns a Bayesian ranking and selection (R&S) problem under input uncertainty when all solutions are simulated with common input models estimated ...

Ranking and Selection Under Input Uncertainty - CityU Scholars

In this project, we are interested in R&S problems in the presenceof input uncertainty, and propose to develop a series of new and efficient ...

Data-Driven Ranking and Selection Under Input Uncertainty ...

We consider a simulation-based ranking and selection (R&S) problem with input uncertainty, in which unknown input distributions can be estimated using input ...

[PDF] Ranking and selection under input uncertainty: A budget ...

A new formulation is proposed that captures the tradeoff between collecting input data and running simulations and develops an algorithm for two-stage ...

Data-Driven Ranking and Selection Under Input Uncertainty

In “Data-Driven Ranking and Selection Under Input Uncertainty,” Wu, Wang, and Zhou consider such a simulation-based ranking and selection ...

Fixed Budget Ranking and Selection under Input Uncertainty

If the input model is estimated from finite historical data, then the simulation output suffers from input uncertainty, and blindly applying traditional ...

Selection of the Most Probable Best under Input Uncertainty — Penn ...

We consider a ranking and selection problem whose configuration depends on a common input model estimated from finite real-world observations. To find a ...

Stochastic simulation under input uncertainty: A Review

Ignoring input uncertainty often leads to poor estimates of the system performance, especially when there is limited amount of historical data to make inference ...