Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty

This study presents a multi-objective mathematical model for integrating upstream and midstream segments of crude oil supply chain in the context of environmental indicators. An actual case study in the Persian Gulf is considered. Upstream and midstream segments are integrated into the presented mod...

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Vydané v:Journal of cleaner production Ročník 152; s. 295 - 311
Hlavní autori: Azadeh, Ali, Shafiee, Farideh, Yazdanparast, Reza, Heydari, Jafar, Fathabad, Abolhassan Mohammadi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 20.05.2017
Predmet:
algorithms
case studies
environmental indicators
Environmental issues
mathematical models
Multi-objective evolutionary algorithm based on decomposition (MOEA-D)
Multi-objective particle swarm optimization (MOPSO)
Non-dominated sorting genetic algorithm (NSGA-II)
oil fields
Oil supply chain
Persian Gulf
petroleum
planning
supply chain
uncertainty
Persian Gulf
Multi-objective particle swarm optimization (MOPSO)
Non-dominated sorting genetic algorithm (NSGA-II)
Multi-objective evolutionary algorithm based on decomposition (MOEA-D)
Oil supply chain
Environmental issues
ISSN:0959-6526, 1879-1786
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Shrnutí:This study presents a multi-objective mathematical model for integrating upstream and midstream segments of crude oil supply chain in the context of environmental indicators. An actual case study in the Persian Gulf is considered. Upstream and midstream segments are integrated into the presented model due to their significant interaction. Also, oilfield development and transformation planning are considered simultaneously along with green aspects. The bi-objective optimization considers net present value (NPV) and environmental issues. A unique multi-objective evolutionary algorithm based on decomposition (MOEA-D) approach is employed to solve the proposed mixed integer nonlinear programming model. The results of MOEA-D are compared with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). The results indicate the superiority of the MOEA-D approach for large size problems. •Integration of upstream and midstream of crude oil supply chain with environmental indicators.•Decisions in upstream are made based on midstream segments.•A unique evolutionary algorithm based on decomposition.•It is superior to NSGA-II and MOPSO.•It is practical approach for decision makers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2017.03.105