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Oil industry value chain simulation with learning agents

Oil industry value chain simulation with learning agents
Oil industry value chain simulation with learning agents

Simulation is an important tool to evaluate many systems, but it often requires detailed knowledge of each specific system and a long time to generate useful results and insights. A large portion of the required time stems from the need to define operational rules and build valid models that represent them properly. To shorten this model construction time, a learning-agent-based model is proposed. This technique is recommended for cases where optimal policies are not known or hard and costly to unequivocally determine, as it enables the simulation agents to learn good policies “by themselves”. A model is built with this technique and a representative case study of oil industry value chain simulation is presented as a proof of concept.

Agent, Machine learning, Oil, Simulation
0098-1354
199-209
Fuller, Daniel Barry
c1beeb41-ad67-4126-b493-ea31466a85b9
Ferreira Filho, Virgílio José Martins
6853ee2e-9675-43be-afcb-77d1677716e9
de Arruda, Edilson Fernandes
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fuller, Daniel Barry
c1beeb41-ad67-4126-b493-ea31466a85b9
Ferreira Filho, Virgílio José Martins
6853ee2e-9675-43be-afcb-77d1677716e9
de Arruda, Edilson Fernandes
8eb3bd83-e883-4bf3-bfbc-7887c5daa911

Fuller, Daniel Barry, Ferreira Filho, Virgílio José Martins and de Arruda, Edilson Fernandes (2018) Oil industry value chain simulation with learning agents. Computers and Chemical Engineering, 111, 199-209. (doi:10.1016/j.compchemeng.2018.01.008).

Record type: Article

Abstract

Simulation is an important tool to evaluate many systems, but it often requires detailed knowledge of each specific system and a long time to generate useful results and insights. A large portion of the required time stems from the need to define operational rules and build valid models that represent them properly. To shorten this model construction time, a learning-agent-based model is proposed. This technique is recommended for cases where optimal policies are not known or hard and costly to unequivocally determine, as it enables the simulation agents to learn good policies “by themselves”. A model is built with this technique and a representative case study of oil industry value chain simulation is presented as a proof of concept.

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More information

Published date: 4 March 2018
Keywords: Agent, Machine learning, Oil, Simulation

Identifiers

Local EPrints ID: 446137
URI: http://eprints.soton.ac.uk/id/eprint/446137
ISSN: 0098-1354
PURE UUID: 8a4bb554-014f-4782-a95b-1b039d0aefe7
ORCID for Edilson Fernandes de Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 21 Jan 2021 17:35
Last modified: 28 Apr 2022 02:31

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Contributors

Author: Daniel Barry Fuller
Author: Virgílio José Martins Ferreira Filho

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