Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives

Cuicui Zheng; James Calvin

doi:10.1109/WSC40007.2019.9004840

Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We consider the problem of approximating the minimum cost of a finite set of alternative systems. We can not directly observe the cost of the systems, but we can estimate the cost using simulation. The simulation run lengths are adaptively chosen for each system. We describe an optimization algorithm and establish a bound on the error convergence rate. Compared with a single system, the error grows by an additional factor of the square root of the logarithm of the number of systems and the simulation budget.

Original language	English (US)
Title of host publication	2019 Winter Simulation Conference, WSC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3428-3435
Number of pages	8
ISBN (Electronic)	9781728132839
DOIs	https://doi.org/10.1109/WSC40007.2019.9004840
State	Published - Dec 2019
Externally published	Yes
Event	2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States Duration: Dec 8 2019 → Dec 11 2019

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2019-December
ISSN (Print)	0891-7736

Conference

Conference	2019 Winter Simulation Conference, WSC 2019
Country/Territory	United States
City	National Harbor
Period	12/8/19 → 12/11/19

All Science Journal Classification (ASJC) codes

Software
Modeling and Simulation
Computer Science Applications

Access to Document

10.1109/WSC40007.2019.9004840

Cite this

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title = "Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives",

abstract = "We consider the problem of approximating the minimum cost of a finite set of alternative systems. We can not directly observe the cost of the systems, but we can estimate the cost using simulation. The simulation run lengths are adaptively chosen for each system. We describe an optimization algorithm and establish a bound on the error convergence rate. Compared with a single system, the error grows by an additional factor of the square root of the logarithm of the number of systems and the simulation budget.",

author = "Cuicui Zheng and James Calvin",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Winter Simulation Conference, WSC 2019 ; Conference date: 08-12-2019 Through 11-12-2019",

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doi = "10.1109/WSC40007.2019.9004840",

language = "English (US)",

series = "Proceedings - Winter Simulation Conference",

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Zheng, C & Calvin, J 2019, Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives. in 2019 Winter Simulation Conference, WSC 2019., 9004840, Proceedings - Winter Simulation Conference, vol. 2019-December, Institute of Electrical and Electronics Engineers Inc., pp. 3428-3435, 2019 Winter Simulation Conference, WSC 2019, National Harbor, United States, 12/8/19. https://doi.org/10.1109/WSC40007.2019.9004840

Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives. / Zheng, Cuicui; Calvin, James.
2019 Winter Simulation Conference, WSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3428-3435 9004840 (Proceedings - Winter Simulation Conference; Vol. 2019-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Y1 - 2019/12

N2 - We consider the problem of approximating the minimum cost of a finite set of alternative systems. We can not directly observe the cost of the systems, but we can estimate the cost using simulation. The simulation run lengths are adaptively chosen for each system. We describe an optimization algorithm and establish a bound on the error convergence rate. Compared with a single system, the error grows by an additional factor of the square root of the logarithm of the number of systems and the simulation budget.

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Using Simulation to Approximate the Minimum Cost of a Finite Set of Alternatives

Abstract

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Conference

All Science Journal Classification (ASJC) codes

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