Oil industry value chain simulation with learning agents
Authored by Daniel Barry Fuller, Ferreira Filho Virgilio Jose Martins, Arruda Edilson Fernandes de
Date Published: 2018
DOI: 10.1016/j.compchemeng.2018.01.008
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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. (c) 2018 Elsevier Ltd.
All rights reserved.
Tags
Agent
Simulation
Uncertainty
Machine learning
Maritime transportation
Model
Supply chains
Operations
Logistics
Framework
System
Oil
Crude-oil
Petroleum-products