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A Hybrid Model of Mathematical Modeling and Simulation for Improving the Petrochemical Products Transmission in a Green Supply Chain

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Abstract

The research on a hybrid model of mathematical modeling and simulation for enhancing the transmission of petrochemical products within a green supply chain is situated at the intersection of environmental sustainability. This study aims to address the pressing need for more efficient and environmentally friendly practices in the petrochemical industry, where the demand for greener solutions is increasing due to regulatory requirements and stakeholder expectations. The proposed framework provides a fully quantitative basis for decision-making, enabling petrochemical companies to make informed choices that prioritize meeting customer demands, reducing costs, and mitigating greenhouse gas emissions. Here, a model was developed to enhance the efficiency of the petroleum condensate supply chain through mathematical programming, enabling the formulation and implementation of strategic and tactical strategies. The aim is to minimize investment and operational expenses as well as greenhouse gas emissions associated with petroleum and gas pipelines, ensuring compliance with pressure and transmission network criteria. Moreover, efforts are made to reduce the environmental impact by minimizing pollutant emissions within the supply chain. By utilizing a practical case study, all potential decisions are thoroughly evaluated to address the environmental considerations of the supply chain. The proposed model demonstrates a high level of reliability and accuracy when compared to simulation modeling and the NSGA-II meta-heuristic algorithm, effectively estimating the desired objectives.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

    Hamidreza Mahmoudi, Morteza Bazrafshan & Mohaddeseh Ahmadipourroudposht

Authors
  1. Hamidreza Mahmoudi
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  2. Morteza Bazrafshan
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  3. Mohaddeseh Ahmadipourroudposht
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Corresponding author

Correspondence to Morteza Bazrafshan.

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Ethical Approval and Competing Interests

• All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

• This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue and has not self-plagiarism

• The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

Conflicts of Interest

The authors declare no conflict of interest.

Additional information

Hamidreza Mahmoudi received her Ph.D. degree in Islamic Azad University of Lahijan branch in 2024 in industrial engineering. Her research interests include supply chain management and simulation.

Mohadeseh Ahmadipour is assistant professor of department of industrial engineering, Lahijan branch, Islamic Azad University. His research interests include BPM, simulation and also knowledge management. He received his Ph.D. from Tarbiat Modares University of Tehran, Iran, 2015.

Mohadeseh Ahmadipour is assistant professor of department of industrial engineering, Lahijan branch, Islamic Azad University. Her research interests include decisionmaking theory, facility planning, quality management and supply chain management. She received her Ph.D. from Universiti Teknologi Malaysia (UTM).

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Mahmoudi, H., Bazrafshan, M. & Ahmadipourroudposht, M. A Hybrid Model of Mathematical Modeling and Simulation for Improving the Petrochemical Products Transmission in a Green Supply Chain. J. Syst. Sci. Syst. Eng. (2024). https://doi.org/10.1007/s11518-024-5630-z

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  • DOI: https://doi.org/10.1007/s11518-024-5630-z

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