A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

  • Saeid Kalantari Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Iran https://orcid.org/0000-0002-3210-6163
  • Hamed Kazemipoor Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Iran
  • Farzad Movahedi Sobhani Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Iran
  • Seyed Mohammad Hadji Molana Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Iran
Keywords: Closed-loop supply chain network, sustainability, neutrosophic optimization, neutrosophic logic, supply chain management, optimization

Abstract

In this paper, a stable CLSC problem is modeled in conditions of uncertainty and indeterminacy. The SCN is designed to maximize NPV and minimize carbon releases by maintaining environment friendly policies and accounting for the increase. To achieve a suitable model for designing a stable CLSCN and making important decisions such as selecting the right suppliers, selecting the type of transport, initialing the facility, the optimal flow between facilities, and accomplishing an efficient solution to the problem decision making, the neutrosophic optimization method is used. The results of experiments that discuss and evaluate different scenarios confirm the efficiency and validity of the proposed model. The findings also show that the effective improvement of the obtained solutions by reducing the solution time up to twenty percent can be responsible for large-scale problems in different scenarios. This paper uses a neutrosophic optimization method to solve the problem of designing a stable CLSCN under uncertainty and indeterminacy.

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References

Acquaye, A., Ibn-Mohammed, T., Genovese, A., Afrifa, G. A., Yamoah, F. A., & Oppon, E. (2018). A quantitative model for environmentally sustainable supply chain performance measurement. European Journal of Operational Research, 269(1), 188-205. DOI: https://doi.org/10.1016/j.ejor.2017.10.057

Akram, M., Bashir, A., & Edalatpanah, S. A. (2021). A hybrid decision-making analysis under complex q-rung picture fuzzy Einstein averaging operators. Computational and Applied Mathematics, 40(8), 1-35.

Alegoz, M., Kaya, O., & Bayindir, Z. P. (2020). Closing the loop in supply chains: Economic and environmental effects. Computers & Industrial Engineering, 142, 106366.

Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications, 73, 82-91. DOI: https://doi.org/10.1016/j.eswa.2016.12.024

Ansari, Z. N., & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of cleaner production, 142, 2524-2543. DOI: https://doi.org/10.1016/j.jclepro.2016.11.023

Atanassov, K. (1986). Intuitionistic fuzzy sets. fuzzy sets and systems 20 (1), 87-96. DOI: https://doi.org/10.1016/S0165-0114(86)80034-3

Ayvaz, B., Bolat, B., & Aydın, N. (2015). Stochastic reverse logistics network design for waste of electrical and electronic equipment. Resources, conservation and recycling, 104, 391-404. DOI: https://doi.org/10.1016/j.resconrec.2015.07.006

Darbari, J. D., Kannan, D., Agarwal, V., & Jha, P. C. (2019). Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem. Annals of operations research, 273(1), 693-738. DOI: https://doi.org/10.1007/s10479-017-2701-2

Das, S. K., Edalatpanah, S. A., & Mandal, T. (2021). Development of unrestricted fuzzy linear fractional programming problems applied in real case. Fuzzy Information and Engineering, 13(2), 184-195.

De Vargas Mores, G., Finocchio, C. P. S., Barichello, R., & Pedrozo, E. A. (2018). Sustainability and innovation in the Brazilian supply chain of green plastic. Journal of cleaner production, 177, 12-18. DOI: https://doi.org/10.1016/j.jclepro.2017.12.138

De, M., & Giri, B. C. (2020). Modelling a closed-loop supply chain with a heterogeneous fleet under carbon emission reduction policy. Transportation research part e: logistics and transportation review, 133, 101813.

Deli, İ., Uluçay, V., & Polat, Y. (2022). N-valued neutrosophic trapezoidal numbers with similarity measures and application to multi-criteria decision-making problems. Journal of Ambient Intelligence and Humanized Computing, 13(9), 4493-4518.

Deveci, M., Simic, V., & Torkayesh, A. E. (2021). Remanufacturing facility location for automotive Lithium-ion batteries: An integrated neutrosophic decision-making model. Journal of Cleaner Production, 317, 128438.

Edalatpanah, S. A. (2019). A nonlinear approach for neutrosophic linear programming. Journal of applied research on industrial engineering, 6(4), 367-373.

Edalatpanah, S. A. (2020). Neutrosophic structured element. Expert systems, 37(5), e12542.

Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy sets and systems, 341, 69-91. DOI: https://doi.org/10.1016/j.fss.2017.03.019

Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Hybrid optimizers to solve a tri-level programming model for a tire closed-loop supply chain network design problem. Applied Soft Computing, 70, 701-722.

Feng, S., & Chen, C. P. (2018). Fuzzy broad learning system: A novel neuro-fuzzy model for regression and classification. IEEE transactions on cybernetics, 50(2), 414-424.

Ghahremani-Nahr, Javid, Hamed Nozari, and Mehrnaz Bathaee. "Robust Box Approach for Blood Supply Chain Network Design under Uncertainty: Hybrid Moth-Flame Optimization and Genetic Algorithm." International Journal of Innovation in Engineering 1.2 (2021): 40-62.

Gholizadeh, H., & Fazlollahtabar, H. (2020). Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry. Computers & Industrial Engineering, 147, 106653.

Gholizadeh, H., Jahani, H., Abareshi, A., & Goh, M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers & Industrial Engineering, 157, 107324.

Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural computing and applications, 32(8), 3967-3985.

Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International journal of production economics, 140(1), 149-159. DOI: https://doi.org/10.1016/j.ijpe.2012.01.035

Golpîra, H., Sadeghi, H., & Bahramara, S. (2021). Electricity supply chain coordination: Newsvendor model for optimal contract design. Journal of Cleaner Production, 278, 123368.

Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of cleaner production, 142, 371-384. DOI: https://doi.org/10.1016/j.jclepro.2016.03.126

Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research, 263(1), 108-141. DOI: https://doi.org/10.1016/j.ejor.2017.04.009

Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production, 242, 118317.

Heydari, J., & Rafiei, P. (2020). Integration of environmental and social responsibilities in managing supply chains: a mathematical modeling approach. Computers & Industrial Engineering, 145, 106495.

Homayouni, Z., Pishvaee, M. S., Jahani, H., & Ivanov, D. (2021). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Annals of Operations Research, 1-41.

Jahani, H., Abbasi, B., & Alavifard, F. (2017, December). Supply chain network reconfiguration in new products launching phase. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 95-99). IEEE. DOI: https://doi.org/10.1109/IEEM.2017.8289858

Kumar Das, S., Edalatpanah, S. A., & Dash, J. K. (2021). A novel lexicographical-based method for trapezoidal neutrosophic linear programming problem. Neutrosophic Sets and Systems, 46(1), 12.

Kumar, R., Edalatpanah, S. A., Gayen, S., & Broumi, S. (2021). Answer Note “A novel method for solving the fully neutrosophic linear programming problems: Suggested modifications”. Neutrosophic Sets and Systems, Vol. 39, 2021, 147.

Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Infinite Study.

Liu, P., & Cheng, S. (2020). An improved MABAC group decision-making method using regret theory and likelihood in probability multi-valued neutrosophic sets. International Journal of Information Technology & Decision Making, 19(05), 1353-1387.

Liu, Z., Li, K. W., Li, B. Y., Huang, J., & Tang, J. (2019). Impact of product-design strategies on the operations of a closed-loop supply chain. Transportation Research Part E: Logistics and Transportation Review, 124, 75-91.

Low, Y. S., Halim, I., Adhitya, A., Chew, W., & Sharratt, P. (2016). Systematic framework for design of environmentally sustainable pharmaceutical supply chain network. Journal of Pharmaceutical Innovation, 11(3), 250-263. DOI: https://doi.org/10.1007/s12247-016-9255-8

Luo, S. Z., Xing, L. N., & Ren, T. (2022). Performance Evaluation of Human Resources Based on Linguistic Neutrosophic Maclaurin Symmetric Mean Operators. Cognitive Computation, 14(2), 547-562.

Lupiáñez, F. G. (2009). Interval neutrosophic sets and topology. Kybernetes, 38 No. 3/4, 621-624. DOI: https://doi.org/10.1108/03684920910944849

Mao, X., Guoxi, Z., Fallah, M., & Edalatpanah, S. A. (2020). A neutrosophic-based approach in data envelopment analysis with undesirable outputs. Mathematical problems in engineering, 2020.

Mendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345-377. DOI: https://doi.org/10.1109/5.364485

Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420-433. DOI: https://doi.org/10.1016/j.asoc.2014.10.035

Mohabbati-Kalejahi, N., & Vinel, A. (2021). Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location. Transportation Research Record, 2675(6), 306-329.

Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242, 118452.

Molodtsov, D. (1999). Soft set theory—first results. Computers & mathematics with applications, 37(4-5), 19-31. DOI: https://doi.org/10.1016/S0898-1221(99)00056-5

Morganti, E., & Gonzalez-Feliu, J. (2015). City logistics for perishable products. The case of the Parma's Food Hub. Case Studies on Transport Policy, 3(2), 120-128. DOI: https://doi.org/10.1016/j.cstp.2014.08.003

Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers & Industrial Engineering, 148, 106716.

Pamucar, D., Yazdani, M., Obradovic, R., Kumar, A., & Torres‐Jiménez, M. (2020). A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem. International Journal of Intelligent Systems, 35(12), 1934-1986.

Pattanayak, R. M., Behera, H. S., & Panigrahi, S. (2022). A non-probabilistic neutrosophic entropy-based method for high-order fuzzy time-series forecasting. Arabian Journal for Science and Engineering, 47(2), 1399-1421.

Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers & Chemical Engineering, 104, 38-55. DOI: https://doi.org/10.1016/j.compchemeng.2017.04.005

Pham, T. H. (2021). Optimality conditions and duality for multiobjective semi-infinite programming with data uncertainty via Mordukhovich subdifferential. Yugoslav Journal of Operations Research, 31(4), 495-514.

Polo, A., Peña, N., Muñoz, D., Cañón, A., & Escobar, J. W. (2019). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega, 88, 110-132.

Pourmehdi, M., Paydar, M. M., & Asadi-Gangraj, E. (2020). Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology. Journal of Cleaner Production, 277, 123298.

Rayappan, P., & Mohana, K. (2021). Spherical fuzzy cross entropy for multiple attribute decision making problems. Journal of Fuzzy Extension and Applications, 2(4), 355-363.

Ross, T. J. (2005). Fuzzy logic with engineering applications. John Wiley & Sons.

Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135.

Saedinia, R., Vahdani, B., Etebari, F., & Nadjafi, B. A. (2019). Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer. Transportation Research Part E: Logistics and Transportation Review, 123, 121-141.

Sel, Ç. & Bilgen, B. (2015). Quantitative models for supply chain management within dairy industry: a review and discussion. European Journal of Industrial Engineering, 9(5), 561-594. DOI: https://doi.org/10.1504/EJIE.2015.071772

Smarandache, F. (1999). A unifying field in Logics: Neutrosophic Logic. In Philosophy (pp. 1-141). American Research Press.

Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & industrial engineering, 109, 191-203. DOI: https://doi.org/10.1016/j.cie.2017.04.038

Sorourkhah, A., & Edalatpanah, S. A. (2022). Using a Combination of Matrix Approach to Robustness Analysis (MARA) and Fuzzy DEMATEL-Based ANP (FDANP) to Choose the Best Decision. International Journal of Mathematical, Engineering and Management Sciences, 7(1), 68.

Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of cleaner production, 113, 662-673. DOI: https://doi.org/10.1016/j.jclepro.2015.10.074

Torkayesh, A. E., Tavana, M., & Santos-Arteaga, F. J. (2022). A multi-distance interval-valued neutrosophic approach for social failure detection in sustainable municipal waste management. Journal of Cleaner Production, 336, 130409.

Torra, V. (2010). Hesitant fuzzy sets. International journal of intelligent systems, 25(6), 529-539. DOI: https://doi.org/10.1002/int.20418

Ulucay, V., Deli, I., & Şahin, M. (2018). Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making. Neural Computing and Applications, 29(3), 739-748. DOI: https://doi.org/10.1007/s00521-016-2479-1

Umoh, U., Eyoh, I., Isong, E., Ekong, A., & Peter, S. (2020). Using interval type-2 fuzzy logic to analyze igbo emotion words. Journal of Fuzzy Extension and Applications, 1(3), 206-226.

Vellapandi, R., & Gunasekaran, S. (2020). A new decision making approach for winning strategy based on muti soft set logic. Journal of fuzzy extension and applications, 1(2), 119-129.

Voskoglou, M. (2020). Assessment and linear programming under fuzzy conditions. arXiv preprint arXiv: 2011.10640.

Wan, N., & Hong, D. (2019). The impacts of subsidy policies and transfer pricing policies on the closed-loop supply chain with dual collection channels. Journal of Cleaner Production, 224, 881-891.

Wang, H., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). Single valued neutrosophic sets. Infinite study.

Wang, K. J., & Lee, C. H. (2015). A revised ant algorithm for solving location–allocation problem with risky demand in a multi-echelon supply chain network. Applied Soft Computing, 32, 311-321. DOI: https://doi.org/10.1016/j.asoc.2015.03.046

Wei, G. (2016). Picture fuzzy cross-entropy for multiple attribute decision making problems. Journal of Business Economics and Management, 17(4), 491-502. DOI: https://doi.org/10.3846/16111699.2016.1197147

Wei, G., Wu, J., Guo, Y., Wang, J., & Wei, C. (2021). An extended COPRAS model for multiple attribute group decision making based on single-valued neutrosophic 2-tuple linguistic environment. Technological and Economic Development of Economy, 27(2), 353-368.

Yager, R. R. (2013). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965. DOI: https://doi.org/10.1109/TFUZZ.2013.2278989

Yang, M., Fu, M., & Zhang, Z. (2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change, 169, 120795.

Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282-305.

Yavari, M., & Zaker, H. (2020). Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Computers & Chemical Engineering, 134, 106680.

Ye, J. (2014). Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. Journal of intelligent & fuzzy systems, 26(1), 165-172.

Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable closed-loop supply chain design problem: A hybrid genetic algorithm approach. Mathematics, 8(1), 84.

Zahedi, A., Salehi-Amiri, A., Hajiaghaei-Keshteli, M., & Diabat, A. (2021). Designing a closed-loop supply chain network considering multi-task sales agencies and multi-mode transportation. Soft Computing, 25(8), 6203-6235.

Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Media.

Published
2022-10-05
How to Cite
Kalantari, S., Kazemipoor, H., Movahedi Sobhani, F., & Hadji Molana, S. M. (2022). A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies. Decision Making: Applications in Management and Engineering, 5(2), 46-77. https://doi.org/10.31181/dmame03051020224k