# A Dynamic Sustainable Competitive Petroleum Supply Chain Model for   Various Stakeholders with Shared Facilities

**Authors:** Nazanin Moradinasab, Hassan Jafarzadeh, M. R. Amin-Naseri, Cody H., Fleming

arXiv: 1907.11789 · 2019-07-30

## TL;DR

This paper introduces a multi-objective, multi-level dynamic model for the petroleum supply chain that considers various stakeholders, aiming to optimize profit, job creation, and environmental impact through detailed planning.

## Contribution

It presents a novel MILP model for a dynamic, sustainable petroleum supply chain considering multiple stakeholders and objectives, validated with real data and sensitivity analysis.

## Key findings

- Sensitivity analysis identified key cost factors affecting outcomes.
- The model effectively balances economic and environmental objectives.
- Application demonstrated using real petroleum supply chain data.

## Abstract

Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and their profit and minimize the air pollution simultaneously. To meet these objectives, an effective detailed and precise planning is needed. On the other hand, the dynamic environment and the presence of various stakeholders add to the complexity of planning and design of petroleum supply chain. Therefore, the multi-period, multi-objective, multi-level and multi-product dynamic sustainable competitive petroleum supply chain (DSCPSC) model taking into consideration the various stakeholders have been proposed in this paper. The proposed model is an MILP model and GAMS 24.1.2 software has been used to run it for a part of real petroleum supply chain data. Sensitivity analysis was then performed to determine the sensitivity of the results to the variation of the coefficients in objective function. Sensitivity analysis reveals that the highest variations of the objective function were observed with respect to the variable costs, facility installation costs and pipeline transportation costs.

## Full text

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## Figures

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## References

37 references — full list in the complete paper: https://tomesphere.com/paper/1907.11789/full.md

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Source: https://tomesphere.com/paper/1907.11789