Oil industry value chain simulation with learning agents

被引：7

作者：

Fuller, Daniel Barry ^{[1
]}

Martins Ferreira Filho, Virgilio Jose ^{[1
]}

de Arruda, Edilson Fernandes ^{[1
]}

机构：

[1] Univ Fed Rio de Janeiro, Grad Sch & Res Engn, Alberto Luiz Coimbra Inst, Ind Engn Program, POB 68507, BR-21941972 Rio De Janeiro, RJ, Brazil

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2018年 / 111卷

关键词：

Simulation; Oil; Agent; Machine learning; MARITIME TRANSPORTATION; PETROLEUM-PRODUCTS; MODEL; LOGISTICS; OPERATIONS; SYSTEM;

D O I：

10.1016/j.compchemeng.2018.01.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：199 / 209

页数：11

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