Joint Resource Allocation for Input Data Collection and Simulation

16 Pages Posted: 29 Aug 2020

See all articles by Jingxu Xu

Jingxu Xu

University of California, Berkeley, Department of Industrial Engineering and Operations Research

Peter Glynn

Stanford University

Zeyu Zheng

University of California, Berkeley

Date Written: July 23, 2020

Abstract

Simulation is often used to evaluate and compare performances of stochastic systems, where the underlying stochastic models are estimated from real-world input data. Collecting more input data can derive closer-to-reality stochastic models while generating more simulation replications can reduce stochastic errors. With the objective of selecting the system with the best performance, we propose a general framework to analyze the joint resource allocation problem for collecting input data and generating simulation replications. Two commonly arised features, correlation in input data and common random numbers in simulation, are jointly exploited to save cost and enhance efficiency. In presence of both features, closed-form joint resource allocation solutions are given for the comparison of two systems.

Suggested Citation

Xu, Jingxu and Glynn, Peter and Zheng, Zeyu, Joint Resource Allocation for Input Data Collection and Simulation (July 23, 2020). Available at SSRN: https://ssrn.com/abstract=3658811 or http://dx.doi.org/10.2139/ssrn.3658811

Jingxu Xu (Contact Author)

University of California, Berkeley, Department of Industrial Engineering and Operations Research ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

Peter Glynn

Stanford University ( email )

Stanford, CA 94305
United States

Zeyu Zheng

University of California, Berkeley ( email )

4125 Etcheverry Hall
Berkeley, CA 94720
United States

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