The impact of petrochemical industry economic activities on environmental factors using a fuzzy mathematical programming model

Document Type : Research Paper


1 Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

2 Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran


Due to the nature of activities and processes, the petrochemical industry causes the production of industrial effluents, emissions and wastes that have adverse effects on the environment. The purpose of this study is to investigate the effect of petrochemical economic activities on environmental factors. In this paper, in order to minimize the costs and the amount of pollution caused by the emission of harmful gases, the closed-loop green supply chain model has been used, in which direct and reverse logistics networks have been considered. As a result, a fuzzy mathematical programming model has been developed for when the data are not definitively known. After the demand parameters and the amount of pollution are considered fuzzy, the maximum and bisector mean methods of the area are considered as methods (diffusion) of comparison and ranking of fuzzy definite numbers, and by adding Limitations of these two methods, the model was developed. To solve the model with real data, a plant from the petrochemical industry was selected and the data were prepared for a solution with very good estimates. Finally, the colonial competition algorithm was used to solve it. According to the model, its applicability was shown to reduce the number of environmental pollutants along with the reduction of transportation and waste, and the model for the closed-loop supply chain, which simultaneously considers two direct and inverse logistics networks. It is appropriate.


[1] T. Abdallah, A. Farhat, A. Diabat and S. Kennedy, Green supply chains with carbon trading and environmental
sourcing: Formulation and life cycle assessment, Appl. Math. Model. 36 (2012) 4271–4285.
[2] D. Askarany, H. Yazdifar, and S. Askary, Supply chain management, activity-based costing and organisational
factors, Int. J. Prod. Econ. 127 (2010) 238–248.
[3] C.J. Barrow, Principles and Methods of Environmental Management, Congress Publishing, Tehran, 2001.
[4] H. Chiniforoush and H. Sheikhzadeh, The relationship between organizational performance and green supply chain
in the petrochemical, exploration and production, 69 (2010) 26–33.
[5] H. Fazlollahtabar, I. Mahdavi and A. Mohajeri, Applying fuzzy mathematical programming approach to optimize a
multiple supply network in uncertain condition with comparative analysis, Appl. Soft Comput. 13 (2013) 550–562.
[6] M. Fleischmann, H.R. Krikke, R. Dekker and S.D.P. Flapper, A characterisation of logistics networks for product
recovery, Omega. 28 (2000) 653–666.
[7] J. Hu, Y.-L. Liu, T. W. W. Yuen, M. K. Lim and J. Hu, Do green practices really attract customers? The sharing
economy from the sustainable supply chain management perspective, Resources, Conser. Recyc. 149 (2019) 177–
[8] M. Khairabadi, Designing a Green Supply Chain Model, Master Thesis, Faculty of Management and Economics,
Tarbiat Modares University, Tehran, 2012.[9] Y. Lun, K. H. Lai, C. T. Ng, C. W. Wong and T. E. Cheng, Research in shipping and transport logistics,
International J. Ship. Transport Log. 3 (2011) 1–5.
[10] Y. Ma, Q. Zhang and H. Yin, Environmental management and labor productivity: The moderating role of quality
management, J. Envir. Manag. 255 (2020) 109795.
[11] A. Muriel and D. Simchi-Levi, Supply chain design and planning–applications of optimization techniques for
strategic and tactical models, Handbooks Oper. Res. Manag. Sci. 11 (2003) 15–93.
[12] M. Naseri Taheri, Green supply chain new strategy to gain competitive advantage in the 21st century, Presented
at the New Economy and Trade, 2006, pp. 12–36.
[13] Petrochemical Special Economic Zone Organization, Phase one studies of the Special Economic Zone organization
[14] M. S. Pishvaee, R. Z. Farahani and W. Dullaert, A memetic algorithm for bi-objective integrated forward/reverse
logistics network design, Comput. Oper. Res. 37 (2010) 1100–1112.
[15] M.S. Pishvaee, S.A. Torabi and J. Razmi, Credibility-based fuzzy mathematical programming model for green
logistics design under uncertainty, Comput. Indust. Engin. 62 (2012) 624–632.
[16] M. Ramezani, M. Bashiri and R. Tavakkoli-Moghaddam, A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level, Appl. Math. Model. 37 (2013) 328–344.
[17] F.J. S´aez-Fern´andez, I. Jim´enez-Hern´andez and M.D.S. Ostos-Rey, Seasonality and efficiency of the hotel industry
in the balearic Islands: Implications for Economic and Environmental Sustainability, Sustain. 12 (2020) 3506.
[18] R. Seth and E.K. Tam, Toxic impact assessment of a manufacturing process: illustrative application to the
automotive paint process, Int. J. Envir. Stud. 63 (2006) 453–462.
[19] S. Seuring and M. M¨uller, From a literature review to a conceptual framework for sustainable supply chain
management, J. Cleaner Product. 16 (2008) 1699–1710.
[20] M. Seyed Hashemi, A Multi-Objective Model in the Green Supply Chain and a Meta-Heuristic Algorithm to Solve
the Problem, Master Thesis, Faculty of Industry, Hormozgan University of Science and Research, Hormozgan,
[21] H. Shekari, Identifying, Developing and Prioritizing the Components of Green Productivity Through the Green
Supply Chain Management Approach Using MADM Technique (Case: Shahid Ghandi Telecommunication Company of Yazd), Master Thesis, Faculty of Humanities, Tarbiat Modares University, Tehran, 2005.
[22] J. Shen, An environmental supply chain network under uncertainty, Phys. A: Stat. Mech. Appl. 542 (2020) 123478.
[23] S. Talebzadeh, Supply Chain Operations Reference Model, Presented at the Monthly of Automotive Engineering
and Related Industries, 2008, pp. 3.
[24] E. Teymouri and M. Ahmadi, Supply Chain Management, Iran University of Science and Technology Publishing
Center, Tehran, 2009.
[25] W.C. Yeh and M.-C. Chuang, Using multi-objective genetic algorithm for partner selection in green supply chain
problems, Expert Syst. Appl. 38 (2011) 4244–4253.
[26] R. Zanjirani Frahani and N. Asgari, Modeling the supply chain in the procurement system, Presented at the
Supply Chain Management Quarterly, 2011, pp. 4-22.
[27] Q. Zhu and J. Sarkis, Relationships between operational practices and performance among early adopters of green
supply chain management practices in Chinese manufacturing enterprises, J. Oper. Manag. 22 (2004) 265–289.
Volume 13, Issue 1
March 2022
Pages 539-553
  • Receive Date: 09 June 2021
  • Accept Date: 25 July 2021
  • First Publish Date: 15 September 2021