Scenario-Based Mathematical Modeling for Biofuel Supply Chain Design

Document Type : Research Paper

Authors

1 . Ph.D. Candidate in Industrial Management, Faculty of Administrative and Economics, University of Isfahan, Isfahan, Iran.

2 Associate Prof., Faculty of Administrative and Economics, University of Isfahan, Isfahan, Iran.

3 Assistance Prof., Faculty of Engineering, University of Isfahan, Isfahan, Iran.

10.22059/imj.2025.388398.1008221

Abstract

Objective: This study aims to design and optimize a sustainable biofuel supply chain focusing on water resource management, uncertainty reduction, and enhancing economic, environmental, and social performance. Sustainable biomass, such as Paulownia trees, and recycled water are considered key inputs, providing an integrated solution to the challenges posed by fossil fuels and the urgent need for renewable energy development.
Methods: A multi-objective mathematical model is proposed to minimize costs, satisfy demand, and mitigate environmental impacts. The model incorporates uncertainties in supply and demand using the LP-metric method and applies the Fuzzy Analytic Hierarchy Process (FAHP) to weight objectives, ensuring balance among conflicting goals. Sensitivity analysis examines variations in biomass supply, prices, and demand, while Pareto frontier analysis evaluates trade-offs across objectives. 
Results: Results show that scenario-based modeling enables a comprehensive assessment of supply and demand impacts on supply chain performance. Incorporating wastewater and sewage sludge reduces pressure on natural resources and improves economic and environmental efficiency. The ε-constraint method generates Pareto-optimal solutions, offering decision-makers alternatives consistent with their priorities. Sensitivity analysis highlights that using Paulownia biomass and recycled water enhances flexibility, reduces risks, and promotes balance among economic, environmental, and social objectives, while lowering costs and unmet demand.
Conclusion: This study provides a practical framework for designing and managing a sustainable biofuel supply chain by presenting a comprehensive and practical model. The findings can serve as a roadmap for developing renewable energy and resource efficiency in the energy sector. Additionally, the proposed model offers a robust decision-making tool under conditions of uncertainty and environmental and economic fluctuations. Its application can significantly support sustainable development policies and reduce dependence on fossil fuel resources.

Keywords


Abbasi, E., Abu Noori, A., & Mohammadzadeh, M. (2011). Economic evaluation of bioethanol production from sugarcane waste. Financial Economics, 161–194. (in Persian).
Abbasi, M., & Pishvaee, M. S. (2019). A mathematical model for optimization in the biofuel supply chain network based on microalgae: A case study of Iran. 1st National Conference on Optimization and Novel Solution Methods. (in Persian).
Abbasi, M., Pishvaee, M. S., & Mohseni, S. (2021). Third-generation biofuel supply chain: A comprehensive review and future research directions. Journal of Cleaner Production, 323, 129100. (in Persian). https://doi.org/10.22075/jme.2022.23463.2096
Abdali, H., Sahebi, H., & Pishvaee, M. (2021). The water-energy-food-land nexus at the sugarcane-to-bioenergy supply chain: A sustainable network design model. Computers & Chemical Engineering, 145, 107199. https://doi.org/10.1016/j.cherd.2022.02.028
Ali, I., & Govindan, K. (2023). Extenuating operational risks through digital transformation of agri-food supply chains. Production Planning & Control, 34(12), 1165–1177. https://doi.org/10.1080/09537287.2021.1988177
Arabi, M., & Yaghoubi, S. (2024). A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue. Environmental Science and Pollution Research, 1–21. https://doi.org/10.1007/s11356-024-35428-7
Awino, F. B., & Apitz, S. E. (2024). Solid waste management in the context of the waste hierarchy and circular economy frameworks: An international critical review. Integrated Environmental Assessment and Management, 20(1), 9–35. https://doi.org/10.1002/ieam.4866.
Bahmani, P., Sadrabadi, M. H. D., Makui, A., & Jafari-Nodoushan, A. (2024). An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study. Renewable Energy, 120626. https://doi.org/10.1016/j.renene.2024.120626
Bayramzadeh, S., & Saeedi, M. (2019). Design and planning of a dynamic integrated supply chain network for advanced hydrocarbon biofuels and oil refineries considering financial flows. Energy Policy and Planning Research, 2(5), 97–143. (in Persian).
Datta, A., Hossain, A., & Roy, S. (2019). An overview on biofuels and their advantages and disadvantages. Renewable and Sustainable Energy Reviews, 50, 1234–1246. https://doi.org/10.14233/ajchem.2019.22098
Duc, D. N., Meejaroen, P., & Nananukul, N. (2021). Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration.EnergyReports,7,6833-6843. https://doi.org/10.1016/j.egyr.2021.12.139
Eslampanah, A., Jafarnezhad Chaghooshi, A., Heidary Dehu’i, J., & Taghizadeh Yazdi, M. R. (2023). Designing an industrial waste reverse supply chain network using an intelligent vehicular ad hoc network (VANET): A Iranian automotive industry case study. Industrial Management, 15(3), 447–477. (in Persian). https://doi.org/10.22059/imj.2023.363361.1008069
Fathi, M., Pahlevanzadeh, M. J., Safariniya, A., & Raeisi Nafchi, S. (2024). Designing a sustainable closed-loop supply chain network for automobile tires using a multi-objective mathematical programming approach: A case study. Journal of Green Development Management Studies, 3(1), 223–244. (in Persian). https://doi.org/10.22077/jgdms.2024.7174.1068
Flores-Sigüenza, P., López-Sánchez, V., Mosquera-Gutiérrez, J., Llivisaca-Villazhaya, J., Moscoso-Martínez, M., & Guamán, R. (2025). Fuzzy Optimization and Life Cycle Assessment for Sustainable Supply Chain Design: Applications in the Dairy Industry. Sustainability, 17(12), 5634.https://doi.org/10.3390/su17125634.
Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669–686. https://doi.org/10.1108/JMTM-10-2019-0368
Ghassemi, A., & Scott, M. J. (2021). A mathematical approach to improve energy-water nexus reliability using a novel multi-stage adjustable fuzzy robust approach. In Progress in Intelligent Decision Science: Proceedings of IDS 2020 (pp. 115–123). Springer Cham. https://doi.org/10.1007/978-3-030-66501-2_9
Ghorbani, N., Aghahosseini, A., & Breyer, C. (2020). Assessment of a cost-optimal power system entirely based on renewable energy for Iran by 2050 – Achieving zero greenhouse gas emissions and overcoming the water crisis. Renewable Energy, 146, 125–148. (in Persian).  https://doi.org/10.1016/j.renene.2019.06.079
Ghozatfar, A., & Yaghoubi, S. (2023). A cooperation approach for nexus among biofuel, compost, and water in waste supply chain under risk aversion: A case study. Computers & Chemical Engineering, 177, 108334. https://doi.org/10.1016/j.compchemeng.2023.108334
Gilani, H., & Sahebi, H. (2024). Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning. Energy Conversion and Management: X, 24, 100727. (in Persian). https://doi.org/10.1016/j.ecmx.2024.100727
Gilani, H., Sahebi, H., & Oliveira, F. (2020). Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model. Applied Energy, 278, 115653. (in Persian). https://doi.org/10.1016/j.apenergy.2020.115653
Gital, Y., & Bilgen, B. (2024). Biomass supply chain network design under uncertainty, risk and resilience: A systematic literature review. Computers & Industrial Engineering, 189, 110270. https://doi.org/10.1016/j.cie.2024.110270
Gomez, A., Rodrigues, M., Montanes, C., Dopazo, C., & Fueyo, N. (2011). The technical potential of first-generation biofuels obtained from energy crops in Spain. Biomass and Bioenergy, 35(5), 2143–2155. https://doi.org/10.1016/j.biombioe.2011.02.009
Habib, M. S., & Hwang, S.-J. (2024). Developing sustainable, resilient, and responsive biofuel production and distribution management system: A neutrosophic fuzzy optimization approach based on artificial intelligence and geographic information systems. Applied Energy, 372, 123683.  https://doi.org/10.1016/j.apenergy.2024.123683
Habibi, F., Chakrabortty, R. K., & Abbasi, A. (2023). Towards facing uncertainties in biofuel supply chain networks: A systematic literature review. Environmental Science and Pollution Research, 30(45), 100360–100390. (in Persian). https://doi.org/10.1007/s11356-023-25845-7
Huang, X., Ji, L., Xie, Y., & Luo, Z. (2024). Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties. Food and Bioproducts Processing.  https://doi.org/10.1016/j.fbp.2024.11.021
Huang, X., Ji, L., Yin, J., & Huang, G. (2024). Optimal design, robust regional bioethanol supply chain operational management, and various technological choices and uncertainty fusions. Computers & Chemical Engineering, 182,108565. https://doi.org/10.1016/j.compchemeng.2023.108565
International Energy Agency. (2021). CO₂ emissions from fuel combustion: Highlights 2021. IEA. https://www.iea.org
Iranian Ministry of Energy. (2018). Iran’s energy industry and electricity sector report (in Persian). Ministry of Energy of Iran. http://www.moe.gov.ir
Jakubowski, M. (2022). Cultivation potential and uses of Paulownia wood: A review. Forests, 13(5), 668. https://doi.org/10.3390/f13050668
Jana, D. K., Bhattacharjee, S., Dostál, P., Janková, Z., & Bej, B. (2022). Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique. Cleaner Logistics and Supply Chain, 4, 100044. https://doi.org/10.1016/j.clscn.2022.100044
Jiménez, M., Arenas, M., Bilbao, A., & Rodríguez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599–1609. https://doi.org/10.1016/j.ejor.2005.10.032
Kazemi Miyangaskari, M., Mehrregan, M. R., Safari, H., Keyvanpour, S., & Dehghan Nayeri, M. (2023). Designing a fuzzy multi-objective optimization model of a closed-loop supply chain aimed at supplier selection and order allocation (case study: food retail company in Iran). Industrial Management Studies, 21(69), 1–42. (in Persian). https://doi.org/10.22054/jims.2023.70278.2815
Kiani Mavi, R., Semiari, M., Hosseini Shekarabi, S. A., Kiani Mavi, N., Moshkdanian, F., Nikravesh, A., & Golsorkhi, S. (2025). Multi-Objective Optimization of a Three-Level Sustainable Food Supply Chain: Modeling the Impact of Government Subsidies. Global Journal of Flexible Systems Management, 26(3), 571–600. (in Persian). https://doi.org/10.1007/s40171-025-00454-y
Koçar, G., & Civaş, N. (2013). An overview of biofuels from energy crops: Current status and future prospects. Renewable and Sustainable Energy Reviews, 28, 900–916. https://doi.org/10.1016/j.rser.2013.08.022
Langholtz, M., Webb, E., Preston, B. L., Turhollow, A., Breuer, N., Eaton, L., King, A. W., Sokhansanj, S., Nair, S. S., & Downing, M. (2014). Climate risk management for the US cellulosic biofuels supply chain. Climate Risk Management, 3, 96–115. https://doi.org/10.1016/j.crm.2014.05.001
Maharana, D., Kommadath, R., & Kotecha, P. (2023). An innovative approach to the supply-chain network optimization of biorefineries using metaheuristic techniques. Engineering Optimization, 55(8), 1278–1295. https://doi.org/10.1080/0305215X.2022.2080204
Mahjoub, N., Sahebi, H., Mazdeh, M., & Teymouri, A. (2020). Optimal design of the second and third generation biofuel supply network by a multi-objective model. Journal of Cleaner Production, 256, 120355. (in Persian). https://doi.org/10.1016/j.jclepro.2020.120355
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Mohammadi, A. S., Alem Tabriz, A., & Pishvaee, M. S. (2018). Designing a green closed-loop supply chain network with financial decisions under uncertainty. Industrial Management Journal, 10(1), 61–84. (in Persian). https://doi.org/10.22059/imj.2018.240867.1007303
Mohammadi, T., Sajadi, S. M., Najafi, S. E., & Taqizadeh Yazdi, M. R. (2022). Optimization of a smart supply chain under vendor-managed inventory policy with IoT-related technology selection. Industrial Management, 14(3), 458-483. (in Persian). https://doi.org/10.22059/IMJ.2022.343552.1007948
Mohseni, E., & Mohamadi, D. (2025). Integrated Optimization of Biofuel Supply Chain: A Fuzzy Logic-Based Approach. Journal of Industrial Management Perspective, 15(2), 177–201. (in Persian). https://doi.org/10.48308/jimp.15.2.177
Mohseni, S., & Pishvaee, M. S. (2016). A robust programming approach towards design and optimization of microalgae-based biofuel supply chain. Computers & Industrial Engineering, 100, 58–71. (in Persian). https://doi.org/10.1016/j.cie.2016.08.003
Mollahosseini, A., Hosseini, S. A., Jabbari, M., Figoli, A., & Rahimpour, A. (2017). Renewable energy management and market in Iran: A holistic review on current state and future demands. Renewable and Sustainable Energy Reviews, 80, 774–788. (in Persian). https://doi.org/10.1016/j.rser.2017.05.236
Mondal, A., Giri, B. K., & Roy, S. K. (2023). An integrated sustainable bio-fuel and bio-energy supply chain: A novel approach based on DEMATEL and fuzzy-random robust flexible programming with Me measure. Applied Energy, 343, 121225. https://doi.org/10.1016/j.apenergy.2023.121225
Murillo-Alvarado, P. E., & Ponce-Ortega, J. M. (2024). Optimal planning of biofuel supply chains incorporating temporality of unconventional bioresources. Environment, Development and Sustainability, 26(3), 7715–7733. https://doi.org/10.1007/s10668-023-03028-z
Murillo-Alvarado, P. E., Guillén-Gosálbez, G., Ponce-Ortega, J. M., Castro-Montoya, A. J., Serna-González, M., & Jiménez, L. (2015). Multi-objective optimization of the supply chain of biofuels from residues of the tequila industry in Mexico. Journal of Cleaner Production, 108, 422–441.  https://doi.org/10.1016/j.jclepro.2015.08.052
Nozari, H., Nassar, S., & Szmelter-Jarosz, A. (2025). Fuzzy multi-objective optimization model for resilient supply chain financing based on blockchain and IoT. Digital, 5(3), 32. (in Persian). https://doi.org/10.3390/digital5030032
Pan, A., Xu, S., & Zaidi, S. A. H. (2024). Environmental impact of energy imports: Natural resources income and natural gas production profitability in the Asia-Pacific Economic Cooperation Countries. Geoscience Frontiers, 15(2), 101756. https://doi.org/10.1016/j.gsf.2023.101756
Pandey, V. C., Bajpai, O., & Singh, N. (2016). Energy crops in sustainable phytoremediation. Renewable and Sustainable Energy Reviews, 54, 58–73. https://doi.org/10.1016/j.rser.2015.09.078
Paul, S., Mazumder, C., & Mukherjee, S. (2024). Challenges faced in commercialization of biofuel from biomass energy resources. Biocatalysis and Agricultural Biotechnology, 103312https://doi.org/10.1016/j.bcab.2024.103312
Petridis, K., Grigoroudis, E., & Arabatzis, G. (2018). A goal programming model for a sustainable biomass supply chain network. International Journal of Energy Sector Management, 12(1), 79–102.
Qadir, S. A., Al-Motairi, H., Tahir, F., & Al-Fagih, L. (2021). Incentives and strategies for financing the renewable energy transition: A review. Energy Reports, 7, 3590–3606. https://doi.org/10.1108/IJESM-09-2017-0002
Rahbari, M., Khamseh, A. A., & Mohammadi, M. (2023). A novel multi-objective robust fuzzy stochastic programming model for sustainable agri-food supply chain: case study from an emerging economy. Environmental Science and Pollution Research, 30(25), 67398-67442. (in Persian). https://doi.org/10.1007/s11356-023-26305-w1
Rahmandoust, A., Hafezalkotob, A., Rahmani Parchikolaei, B., & Azizi, A. (2023). Designing a multi-objective stable mathematical model for routing municipal waste collection vehicles. Industrial Management, 15(4), 680–709. (in Persian). https://doi.org/10.22059/imj.2023.350291.1007997
Ramírez-Arpide, F. R., Demirer, G. N., Gallegos-Vázquez, C., Hernández-Eugenio, G., Santoyo-Cortés, V. H., & Espinosa-Solares, T. (2018). Life cycle assessment of biogas production through anaerobic co-digestion of nopal cladodes and dairy cow manure. Journal of Cleaner Production, 172, 2313–2322. https://doi.org/10.1016/j.jclepro.2017.11.192
Ransikarbum, K., & Pitakaso, R. (2024). Multi-objective optimization design of sustainable biofuel network with integrated fuzzy analytic hierarchy process. Expert Systems with Applications, 240, 122586.  https://doi.org/10.1016/j.eswa.2023.122586
Rashid Khan, H. U., Awan, U., Zaman, K., Nassani, A. A., Haffar, M., & Abro, M. M. Q. (2021). Assessing hybrid solar-wind potential for industrial decarbonization strategies: Global shift to green development. Energies, 14(22), 7620. https://doi.org/10.3390/en14227620
Seidl, P. R., & Goulart, A. K. (2016). Pretreatment processes for lignocellulosic biomass conversion to biofuels and bioproducts. Current Opinion in Green and Sustainable Chemistry, 2, 48–53.  https://doi.org/10.1016/j.cogsc.2016.09.003
Suresh, S., Barboza, A. B., Ashwini, K., & Dinesha, P. (2024). Optimization of ANN Models Using Metaheuristic Algorithms for Prediction of Tailpipe Emissions in Biodiesel Engine. Heat Transfer.  https://doi.org/10.1002/htj.22972
Tong, K., Gleeson, M. J., Rong, G., & You, F. (2014). Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty. Biomass and Bioenergy, 60, 108–120. https://doi.org/10.1016/j.biombioe.2012.11.026
Wachyudi, T., Daryanto, A., Machfud, M., & Arkeman, Y. (2020). Biofuel supply chain risk mitigation strategy framework: Expert interview based approach. Journal of Industrial Engineering and Management, 13(1), 179–194. https://doi.org/10.3926/jiem.3127
Wang, M., Ji, L., Xie, Y., & Huang, G. (2024). Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis. Journal of Environmental Management, 351, 119883. https://doi.org/10.1016/j.jenvman.2023.119883
Wassie, S. B. (2020). Natural resource degradation tendencies in Ethiopia: A review. Environmental Systems Research, 9(1), 1–29. https://doi.org/10.1186/s40068-020-00180-0
Yousefloo, A., Babazadeh, R., Mohammadi, M., Pirayesh, A., & Dolgui, A. (2023). Design of a robust waste recycling network integrating social and environmental pillars of sustainability. Computers & Industrial Engineering, 176, 108970. https://doi.org/10.1016/j.cie.2023.108970
Zarei, M., Shams, M. H., Niaz, H., Won, W., Lee, C.-J., & Liu, J. J. (2022). Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties. Renewable Energy, 200, 694–705. (in Persian). https://doi.org/10.1016/j.renene.2022.09.012
Zarrinpour, N., & Khani, A. (2019). Design of second-generation green fuel supply chain from corn waste under uncertainty conditions. The 16th International Conference on Industrial Engineering. (in Persian).
Zema, D. A., Bombino, G., Andiloro, S., & Zimbone, S. M. (2012). Irrigation of energy crops with urban wastewater: Effects on biomass yields, soils and heating values. Agricultural Water Management, 115, 55–65. https://doi.org/10.1016/j.agwat.2012.08.003
Zhang, X., & Vesselinov, V. V. (2016). Energy-water nexus: Balancing the tradeoffs between two-level decision makers. Applied Energy, 183, 77–87. https://doi.org/10.1016/j.apenergy.2016.08.153
Zhang, Y., Jiang, Y., Zhong, M., Geng, N., & Chen, D. (2016). Robust optimization on regional WCO-for-biodiesel supply chain under supply and demand uncertainties. Scientific Programming, 2016, 1087845. https://doi.org/10.1155/2016/1087845
Zhao, X., Ke, Y., Zuo, J., Xiong, W., & Wu, P. (2020). Evaluation of sustainable transport research in 2000–2019. Journal of Cleaner Production, 256, 120404. https://doi.org/10.1016/j.jclepro.2020.120404
Zhou, T., Zhou, T., Li, Z., Aviso, K. B., Tan, R. R., Jia, X., & Wang, F. (2024). Multi-objective optimization of straw-based bio-natural gas supply chains considering cost, CO2 emission, and safety. Journal of Cleaner Production, 449, 141759. https://doi.org/10.1016/j.jclepro.2024.141759