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

doi:10.1016/j.jclepro.2017.03.105

Journal of Cleaner Production

Volume 152, 20 May 2017, Pages 295-311

https://doi.org/10.1016/j.jclepro.2017.03.105 Get rights and content

Highlights

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Integration of upstream and midstream of crude oil supply chain with environmental indicators.
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Decisions in upstream are made based on midstream segments.
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A unique evolutionary algorithm based on decomposition.
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It is superior to NSGA-II and MOPSO.
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It is practical approach for decision makers.

Abstract

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.

Section snippets

Motivation and significance

Although oil industry plays an important role in world energy demand, oil and other fossil fuels have been the main cause of global warming and environmental problems in past decades. Since many industries’ need to oil is inevitable, it is important to minimize its environmental impacts while maximizing the profit. The main environmental impact of oil industry is emission of gases such as Carbon Dioxide $({CO}_{2})$ , Sulfur Dioxide $({SO}_{2})$ and Nitrogen Oxide (NO) which affect environment directly and

GSCM characteristics

Oil supply chain starts at wells, where crude oil is extracted. Then, in order to separate impurities, it will be transferred to production platforms (pp) with pipelines. Then it will be moved to refineries (f) and external markets (m) by pipelines and tankers based on the determined demand. In refineries, the crude oil will be converted into oil derivations. At the end, refineries products will be transferred to depots by mentioned transportation modes.

As mentioned previously, this study

Proposed model

In the current study, a mixed integer nonlinear programming model is proposed. Two objective functions are considered in the model. The first objective function is presented in Equation (1) which maximizes the profit using net present value method. Equations (2a)–(9) calculate the value of objective function. $max : NPV = \sum_{t} \frac{{TCF}_{t}}{{(1 + i)}^{t - 1}}$

Total cash flow in each period $({TCF}_{t})$ is the outcome of subtracting the total depreciable capital $(F T D C_{t})$ from net earnings $({NE}_{t})$ by considering the salvage value of

Case study

A real case in Persian Gulf is used to verify the validity and application of the proposed mathematical model in this study. Among all accessible technologies for extraction, oil production and storage, and oil derivation production are selected to improve economic and environmental performance in the proposed model. Various features associated with wells ( $W$ ), production platforms ( $P P$ .), international markets ( $m$ .), refineries ( $f$ .), depots ( $c$ .), transportation modes and several productions and

Experimental results

This section is mainly focused on indicating how well the proposed Meta-heuristic algorithms performed. To do so, 12 small size problems are firstly solved by GAMS software, NSGA II, MOEA-D and MOPSO, then the obtained results are compared and presented in Table 5. Moreover, in order to evaluate the performance of stated meta-heuristic approaches for the problem, 8 different large-size problems are considered and solved using NSGA II, MOEA-D and MOPSO. In order to compare the performance of

Conclusions

Oil industry plays an important role in today’s economic and industrial world. Since most of the resources of crude oil is located in some specific countries, it is considered as a highly strategic and critical resource. Therefore, in this study we have reviewed strategic and tactical decisions in oil industry. This study proposes a mixed integer nonlinear programming model, with two different objective functions. The first objective function tries to maximize the net present value of the

Acknowledgement

The authors are grateful for the valuable comments and suggestions from the respected reviewers. They have enhanced the strength and significance of our paper. This study was supported by grants from the University of Tehran (Grant No. 8106013/1/17 and 29917-01-01). The authors are grateful for the support provided by the College of Engineering, University of Tehran, Iran.