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Practical considerations in selecting the best set of simulated systems

Practical considerations in selecting the best set of simulated systems
Practical considerations in selecting the best set of simulated systems
In many practical problems, simulation models are used to support complex decision-making processes comparing hundreds or thousands of solutions. These problems typically have a key objective but the final decision may be dependent on other factors, which cannot be incorporated into the simulation model. In such cases, decision-makers may request a short list of ‘good’ solutions, which work well for the main objective and satisfy one or more chance constraints. While fully sequential ranking and selection procedures can be effective at solving these problems, surveys of experimentation practice suggest that they are under-utilized, potentially due to difficulties automating commercial software. We develop an approach with just two stages of replications. The approach, which has been designed to cope with the use of common random numbers, draws on ideas from indifference zones and makes use of bootstrapping to find a subset of high quality solutions. A Python implementation is freely available.
Simulation
2191-2200
IEEE
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
fece343c-106d-461d-a1dd-71c1772627ca
Rabe, Markus
Juan, Angel
Mustafee, Navonil
Skoogh, Anders
Johansson, B.
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Monks, Thomas
fece343c-106d-461d-a1dd-71c1772627ca
Rabe, Markus
Juan, Angel
Mustafee, Navonil
Skoogh, Anders
Johansson, B.

Currie, Christine and Monks, Thomas (2018) Practical considerations in selecting the best set of simulated systems. Rabe, Markus, Juan, Angel, Mustafee, Navonil, Skoogh, Anders and Johansson, B. (eds.) In Proceedings of the Winter Simulation Conference. IEEE. pp. 2191-2200 .

Record type: Conference or Workshop Item (Paper)

Abstract

In many practical problems, simulation models are used to support complex decision-making processes comparing hundreds or thousands of solutions. These problems typically have a key objective but the final decision may be dependent on other factors, which cannot be incorporated into the simulation model. In such cases, decision-makers may request a short list of ‘good’ solutions, which work well for the main objective and satisfy one or more chance constraints. While fully sequential ranking and selection procedures can be effective at solving these problems, surveys of experimentation practice suggest that they are under-utilized, potentially due to difficulties automating commercial software. We develop an approach with just two stages of replications. The approach, which has been designed to cope with the use of common random numbers, draws on ideas from indifference zones and makes use of bootstrapping to find a subset of high quality solutions. A Python implementation is freely available.

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Accepted/In Press date: 11 June 2018
Published date: 9 December 2018
Venue - Dates: Winter Simulation Conference 2018, Gothenburg, Sweden, 2018-12-09 - 2018-12-12
Keywords: Simulation

Identifiers

Local EPrints ID: 421984
URI: http://eprints.soton.ac.uk/id/eprint/421984
PURE UUID: 8f6b9844-bba1-4d30-b35e-174a0b2ed784
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Thomas Monks: ORCID iD orcid.org/0000-0003-2631-4481

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Date deposited: 12 Jul 2018 16:30
Last modified: 27 Jun 2019 04:01

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