Designing a green supply chain pricing model with a multi-criteria decision-making approach and game theory (case study: home appliance industry)

Document Type : Original Article

Authors

1 PhD Candidate of Industrial Management, Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran. Email: Somayeh.sazegari@gmail.com

2 Associate professor. Department of Management ,Dehaghan Branch, Islamic Azad University, Dehaghan. Email: smrdavoodi@ut.ac.ir

3 Assistant Professor, Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran. Email: goli.a@eng.ui.ac.ir

Abstract

pressure to continuously reduce the negative effects of pollutant emissions resulting from their supply chains. The aim of this research is to design a green supply chain pricing model using a multi-criteria decision-making approach and game theory (case study: the home appliance industry). In this study, after conducting a literature review and identifying key indicators for predicting green supply chain pricing, screening of the indicators was carried out in three stages using the fuzzy Delphi method. Out of 20 indicators, 13 were selected based on the opinions of 13 experts. According to the preference selection approach, Company D was identified as the leading company in the game theory due to its highest priority. Ultimately, based on game theory, scenarios among four members of the supply chain were evaluated, and the best and worst scenarios were identified. Additionally, to test the structural validity of the proposed model, information related to the green supply chain of nine selected home appliance companies was executed using MATLAB software. Finally, the analysis of this research based on game theory reveals the challenges currently faced by Company A. In recent years, Iran has encountered multiple challenges regarding sustainable growth. The results indicate that Company A can only achieve a better optimal status if its installed capacity is at least 20% larger than its current level. This will help Company A optimize production and reduce negative environmental impacts.

Keywords

Main Subjects

References

  1. Abbasi, S., & Choukolaei, H. A. (2023). A systematic review of green supply chain network design literature focusing on carbon policy. Decision Analytics Journal, 6, 100189.‏ https://doi.org/10.1016/j.dajour.2023.100189

    Ansari, Z. N., and R. Kant.(2017). A State-of-Art Literature Review Reflecting 15 Years of Focus on Sustainable Supply Chain Management. Journal of Cleaner Production 142: 2524–2543. https://doi.org/10.1016/j.jclepro.2016.11.023

    Atabaki, M. S., Khamseh, A. A., & Mohammadi, M. (2019). A priority-based firefly algorithm for network design of a closed-loop supply chain with price-sensitive demand. Computers & industrial engineering, 135, 814-837.‏ https://doi.org/10.1016/j.cie.2019.06.054

    Aydin, R., Kwong, C. K., & Ji, P. (2016). Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products. Journal of Cleaner Production, 114, 286-298.‏ https://doi.org/10.1016/j.jclepro.2015.05.116

    Akbari, A. H., & Jafari, M. (2025). Development of a Deep Reinforcement Learning Algorithm in a Dynamic Cellular Manufacturing System Considering Order Rejection, Case Study: Stone Paper Factory. Engineering Management and Soft Computing, 10(2), 204-222. 10.22091/jemsc.2025.11853.1230

    Büyüközkan, G., & Çifçi, G. (2011). A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Computers in industry, 62(2), 164-174.‏ https://doi.org/10.1016/j.compind.2010.10.009

    Calik, E., & Bardudeen, F. (2016). A measurement scale to evaluate sustainable innovation performance in manufacturing organizations. Procedia Cirp, 40, 449-454.‏ https://doi.org/10.1016/j.procir.2016.01.091

    Dai, Z., & Ye, C. (2022). Analysis and evaluation of key elements of optimal regulation of green supply chain from the perspective of low carbon. Wireless Communications and Mobile Computing, 2022, 1-11.‏  https://doi.org/10.1155/2022/8196756

    Feng, Z., & Chen, W. (2018). Environmental regulation, green innovation, and industrial green development: An empirical analysis based on the Spatial Durbin model. Sustainability, 10(1), 223.‏ https://doi.org/10.3390/su10010223

    Golini, R., Moretto, A., Caniato, F., Caridi, M., & Kalchschmidt, M. (2017). Developing sustainability in the Italian meat supply chain: an empirical investigation. International Journal of Production Research, 55(4), 1183-1209.‏ https://doi.org/10.1080/00207543.2016.1234724

    Gosling, J., Jia, F., Gong, Y., & Brown, S. (2016). The role of supply chain leadership in the learning of sustainable practice: toward an integrated framework. Journal of Cleaner Production, 137, 1458-1469. https://doi.org/10.1016/j.jclepro.2014.10.029

    Govindan, K.; Khodaverdi, R.; Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J. Clean Prod. 47, 345–354. https://doi.org/10.1016/j.jclepro.2012.04.014

    Grimm, J.H.; Hofstetter, J.S.; Sarkis, J. (2014). Critical factors for sub-supplier management: A sustainable food supply chains perspective. Int. J. Prod. Econ. 152, 159–173. https://doi.org/10.1016/j.ijpe.2013.12.011

    Guarnieri, P.; Sobreiro, V.A.; Nagano, M.S.; Serrano, A.L.M. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case. J. Clean Prod. 96, 209–219. https://doi.org/10.1016/j.jclepro.2014.05.040

    Hashemi, S.H.; Karimi, A.; Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. Int. J. Prod. Econ. 159, 178–191. https://doi.org/10.1016/j.ijpe.2014.09.027

    Hou, G., Wang, Y., & Xin, B. (2019). A coordinated strategy for sustainable supply chain management with product sustainability, environmental effect and social reputation. Journal of Cleaner Production, 228, 1143-1156. https://doi.org/10.1016/j.jclepro.2019.04.096

    Hou, P., Wang, J., Zhang, Q., & Zhang, S. (2023). Implications of risk aversion behavior on the green product promotion strategy under manufacturer encroachment. Applied Mathematics and Computation, 447, 127911.‏ https://doi.org/10.1016/j.amc.2023.127911

    Hsu, C. C., Tan, K. C., & Mohamad Zailani, S. H. (2016). Strategic orientations, sustainable supply chain initiatives, and reverse logistics: Empirical evidence from an emerging market. International journal of operations & production management, 36(1), 86-110.‏ https://doi.org/10.1108/IJOPM-06-2014-0252

    Humphreys, P.; McIvor, R.; Chan, F. (2003). Using case-based reasoning to evaluate supplier environmental management performance. Expert Syst. Appl. 25, 141–153. https://doi.org/10.1016/S0957-4174(03)00042-3

    Jabbour, A.B., Jabbour, C., Govindan, K., Kannan, D., Arantes, A.F.,(2014). Mixed methodology to analyze the relationship between maturity of environmental management and the adoption of green supply chain management in Brazil  https://doi.org/10.1016/j.resconrec.2014.02.004

    Jafari, M., & Akbari, A. H. (2025). Efficient Algorithms for Dynamic Cellular Manufacturing Systems by Considering Blockchain-Enabled (Case Study: Stone Paper Factory). Journal of Advanced Manufacturing Systems. https://doi.org/10.1142/S0219686725500404

    Kalina, I., Novykov, D., Leszczynski, V., Lavrukhina, K., Kukhta, P., & Nitsenko, V. (2022). ENTREPRENEURIAL STRUCTURES OF THE EXTRACTIVE INDUSTRY: FOREIGN EXPERIENCE IN ENVIRONMENTAL PROTECTION. Scientific Bulletin of National Mining University, 37(5).‏ https://doi.org/10.33271/nvngu/2022-5/136

    Kang, K., Gao, S., Gao, T., & Zhang, J. (2021). Pricing and Financing Strategies for a Green Supply Chain With a Risk-Averse Supplier. IEEE Access, 9, 9250-9261.‏ 10.1109/ACCESS.2021.3050130

    Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. Int. J. Prod. Econ. 195, 391–418. https://doi.org/10.1016/j.ijpe.2017.02.020

    Li, B., Zhu, M., Jiang, Y., & Li, Z. (2016). Pricing policies of a competitive dual-channel green supply chain. Journal of Cleaner Production, 112, 2029-2042.‏ https://doi.org/10.1016/j.jclepro.2015.05.017

    Li, M., & Shan, M. (2023). Pricing and green promotion effort strategies in dual-channel green supply chain: considering e-commerce platform financing and free-riding. Journal of Business & Industrial Marketing, 38(11), 2310-2323.‏  https://doi.org/10.1108/JBIM-07-2022-0303

    Li, P., Rao, C., Goh, M., & Yang, Z. (2021). Pricing strategies and profit coordination under a double echelon green supply chain. Journal of Cleaner Production, 278, 123694. https://doi.org/10.1016/j.jclepro.2020.123694

    Li, Y., & Mathiyazhagan, K. (2018). Application of DEMATEL approach to identify the influential indicators towards sustainable supply chain adoption in the auto components manufacturing sector. Journal of cleaner production, 172, 2931-2941.‏ https://doi.org/10.1016/j.jclepro.2017.11.120

    Liu, P., & Zhang, F. J. (2022). Pricing strategies of dual-channel green supply chain considering Big Data information inputs. Soft Computing, 1-19.‏ https://doi.org/10.1007/s00500-021-06611-6

    Luthra, S., Garg, D., & Haleem, A. (2016). The impacts of critical success factors for implementing green supply chain management towards sustainability: An empirical investigation of Indian automobile industry. Journal of Cleaner Production, 121, 142–158. https://doi.org/10.1016/j.jclepro.2016.01.095

    Luthra, S., Garg, D., & Haleem, A. (2016). The impacts of critical success factors for implementing green supply chain management towards sustainability: An empirical investigation of Indian automobile industry. Journal of Cleaner Production, 121, 142–158. https://doi.org/10.1016/j.jclepro.2016.01.095

    Mahmoudi, A., Shishebori, D., & Sadegheih, A. (2020). Pricing for a multi-channel supply chain with the participation of a third-party logistics service: A game theory approach. Supply Chain Management, 22(67), 23-34. (In Persian ) DOR:20.1001.1.20089198.1399.22.67.2.1

    Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design, 31(4), 1785-1789.‏ https://doi.org/10.1016/j.matdes.2009.11.020

    Masrzadeh Oghaz, Pegah and Dehghanian, Farzad, (2023), Presenting a two-level model to determine the carbon tax in a green supply chain by considering price-sensitive demand and carbon emissions, 9th International Conference on Industrial and Systems Engineering, Mashhad. (In Persian )

    Mathivathanan, D., Kannan, D., & Haq, A. N. (2018). Sustainable supply chain management practices in Indian automotive industry: A multi-stakeholder view. Resources, Conservation and Recycling, 128, 284-305.‏ https://doi.org/10.1016/j.resconrec.2017.01.003

    mini, A., & Alinezhad, A. (2019). Developing Network DEA Model with Undesirable Outputs for Evaluation of Green Supply Chain Management. Supply Chain Management, 21(63), 51-63. 20.1001.1.20089198.1398.21.63.4.8

    Mousavi, P., Yousefizenouz, R., Hasanpoor, A. (2015). Identifying Organizational Information Security Risks Using Fuzzy Delphi. Journal of Information Technology Management, 7(1), 163-184. 10.22059/jitm.2015.53555

    Obeidat, S. M., Al Bakri, A. A., & Elbanna, S. (2020). Leveraging “green” human resource practices to enable environmental and organizational performance: Evidence from the Qatari oil and gas industry. Journal of Business Ethics, 164(2), 371-388. https://doi.org/10.1007/s10551-018-4075-z

    Palevich, R. (2011). Lean sustainable supply chain the: How to create a green infrastructure with lean technologies. Ft press.‏

    Rahmani, K., & Yavari, M. (2019). Pricing policies for a dual-channel green supply chain under demand disruptions. Computers & Industrial Engineering, 127, 493-510.‏ https://doi.org/10.1016/j.cie.2018.10.039

    Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394

    Saffie, N. A. M., & Rasmani, K. A. (2016, July). Fuzzy delphi method: Issues and challenges. In 2016 International Conference on Logistics, Informatics and Service Sciences (LISS) (pp. 1-7). IEEE.‏  10.1109/LISS.2016.7854490

    Sawik, T. (2016). On the risk-averse optimization of service level in a supply chain under disruption risks. Int. J. Prod. Res. 54, 98–113. https://doi.org/10.1080/00207543.2015.1016192

    Seuring &M. Müller, (2008)`.`From a literature review to a conceptual framework for sustainable supply chain management,'' J. Cleaner Prod., vol. 16, no. 15, pp. 16991710, Oct. https://doi.org/10.1016/j.jclepro.2008.04.020

    Sharma, V. K., Chandna, P., & Bhardwaj, A. (2017). Green supply chain management related performance indicators in agro industry: A review. Journal of Cleaner Production, 141, 1194-1208. https://doi.org/10.1016/j.jclepro.2016.09.103

    Shoaeinaeini, M., Govindan, K., & Rahmani, D. (2022). Pricing policy in green supply chain design: the impact of consumer environmental awareness and green subsidies. Operational Research, 1-40.‏ https://doi.org/10.1007/s12351-021-00680-z

    Shoja,Mehdi,Hosseinzadeh Lotfi,Farhad,Gholam Abri,Amir,Rashidi Komijan,Alireza. (2020) .Efficiency of 4 stage supply chain in presence of non discretionary ,  undesirable and negative factors Using SBM model in DEA .Economic  Modeling ,51,73-98. 10.30495/eco.2020.1899015.2357

    Tanhaeean, M., Tavakkoli-Moghaddam, R., & Akbari, A. H. (2022). Boxing match algorithm: A new meta-heuristic algorithm. Soft Computing, 26(24), 13277-13299. https://doi.org/10.1007/s00500-022-07518-6

    Tariq, A., Badir, Y. F., Tariq, W., & Bhutta, U. S. (2017). Drivers and consequences of green product and process innovation: A systematic review, conceptual framework, and future outlook. Technology in Society, 51, 8-23.‏ https://doi.org/10.1016/j.techsoc.2017.06.002

    Tomislav, K. (2018). The concept of sustainable development: From its beginning to the contemporary issues. Zagreb International Review of Economics & Business, 21(1), 67-94.‏ https://doi.org/10.2478/zireb-2018-0005

    Tseng, M.-L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources, Conservation and Recycling, 141, 145–162. https://doi.org/10.1016/j.resconrec.2018.10.009

    Tseng, M.L.; Chiu, A.S.F. (2013). Evaluating firm’s green supply chain management in linguistic preferences. J. Clean Prod. 2013, 40, 22–31. https://doi.org/10.1016/j.jclepro.2010.08.007

    Wang, S., Liu, L., & Wen, J. (2024). Product pricing, green effort decisions and coordination in a dynamic three-echelon green supply chain. Journal of Industrial and Management Optimization, 0-0.‏  Doi: 10.3934/jimo.2024033

    Xu, J., & Zhu, Y. (2011). Dynamic pricing model for the operation of closed-loop supply chain system. Intelligent control and automation, 2(4), 418-423.‏ DOI: 10.4236/ica.2011.24048 

    Yavari, M., Marvi, M., & Akbari, A. H. (2020). Semi-permutation-based genetic algorithm for order acceptance and scheduling in two-stage assembly problem. Neural Computing and Applications, 32, 2989-3003. https://doi.org/10.1007/s00521-019-04027-w

    Yavari, M., & Akbari, A. H. (2023). Service level and profit maximisation in order acceptance and scheduling problem with weighted tardiness. International Journal of Industrial and Systems Engineering, 43(3), 331-362. https://doi.org/10.1504/IJISE.2023.129138

    Yeh, W.C.; Chuang, M.C. (2011). Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst. Appl. 38, 4244–4253. https://doi.org/10.1016/j.eswa.2010.09.091

    Yi, S., & Wen, G. (2023). Game model of transnational green supply chain management considering government subsidies. Annals of Operations Research, 1-22.‏ https://doi.org/10.1007/s10479-023-05420-4

    Yu, C., Wenxin, L., Khan, S. U., Yu, C., Jun, Z., Yue, D., & Zhao, M. (2020). Regional differential decomposition and convergence of rural green development efficiency: evidence from China. Environmental Science and Pollution Research, 27, 22364-22379.‏  https://doi.org/10.1007/s11356-020-08805-1

    Zaid, A. A., Jaaron, A. A., & Bon, A. T. (2018). The impact of green human resource management and green supply chain management practices on sustainable performance: An empirical study. Journal of Cleaner Production, 204, 965–979. https://doi.org/10.1016/j.jclepro.2018.09.062

    Zhang, C. T., & Wang, Z. (2021). Production mode and pricing coordination strategy of sustainable products considering consumers’ preference. Journal of Cleaner Production, 296, 126476.‏ https://doi.org/10.1016/j.jclepro.2021.126476

    Zhang, R., Liu, J., & Qian, Y. (2023). Wholesale-price vs cost-sharing contracts in a green supply chain with reference price effect under different power structures. Kybernetes, 52(5), 1879-1902.‏ https://doi.org/10.1108/K-11-2021-1096

    Zhu, Q., & Rass, S. (2018, October). Game theory meets network security: A tutorial. In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security (pp. 2163-2165).‏ https://doi.org/10.1145/3243734.3264421

    Zhu, Q., Feng, Y., & Choi, S. B. (2017). The role of customer relational governance in environmental and economic performance improvement through green supply chain management. Journal of Cleaner Production, 155, 46-53. https://doi.org/10.1016/j.jclepro.2016.02.124

    Zhu, Q., Feng, Y., & Choi, S. B. (2017). The role of customer relational governance in environmental and economic performance improvement through green supply chain management. Journal of Cleaner Production, 155, 46-53. https://doi.org/10.1016/j.jclepro.2016.02.124

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