Integrated Optimization of Biofuel Supply Chain: A Fuzzy Logic-Based Approach

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Industrial Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

2 Associate Professor, Department of Industrial Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

Abstract

Introduction and Objectives: This study proposes a multi-objective planning model for optimizing the design of a sustainable renewable energy supply chain network based on multi-period biomass. Given the existing challenges in this field, multi-objective modeling has been employed as an innovative approach to improve sustainability and reduce environmental impacts. The main goal of this study is to simultaneously optimize the economic, environmental, and social aspects of the biofuel supply chain to reduce operational costs and carbon emissions while fully meeting consumer demand. This study aims to develop an efficient model for the advancement of the renewable energy supply chain, taking into account multiple complexities and uncertainties.
Methods: To manage uncertainties in key parameters, fuzzy logic has been used, allowing the integration of expert opinions with more realistic data. The proposed multi-objective model was solved using the epsilon constraint method to find Pareto-optimal solutions and the GAMS software. Multiple criteria were considered simultaneously to achieve optimal results across various dimensions. The proposed model can optimize supply chain performance in complex and uncertain environments with greater accuracy and provide different scenarios to improve efficiency and reduce related risks. Sensitivity analysis was also performed to identify key factors affecting system efficiency.
Findings: The results indicate that the proposed model leads to reduced operational costs, decreased carbon emissions, and improved sustainability and efficiency of the supply chain network. Sensitivity analysis revealed that parameters such as transportation costs and CO2 emissions have a significant impact on overall system performance, with small changes in these parameters potentially causing large variations in the final outcomes. Additionally, adopting sustainable approaches and using fuzzy logic helped decision-makers make better decisions under uncertainty to optimize the network. The findings show that using sustainable methods can enhance various aspects of the supply chain. Furthermore, the analysis demonstrated that the fuzzy model provides more accurate parameter estimations, resulting in better decisions in response to environmental changes. The results highlight significant improvements in economic and environmental metrics compared to traditional methods that do not account for uncertainties.
Conclusion: This study demonstrated that using multi-objective fuzzy modeling can improve the sustainability and efficiency of the biofuel supply chain. Given the specific characteristics of the biofuel supply chain in Iran, recommendations for future development and enhancing the efficiency of this supply chain were presented, which can contribute to more optimal decision-making and sustainable development. This research indicates that using fuzzy optimization models can play a key role in improving managerial decisions in the face of uncertainties. Finally, applying this model to other energy supply chains can pave the way for developing similar methods to enhance efficiency and reduce environmental and economic risks on a larger scale.

Keywords

Main Subjects

References

  1. Abbasi, E., Abu Noori, A., & Mohammadzadeh, M. (2011). Economic evaluation of bioethanol production from sugarcane waste. Financial Economics, 161-194. (in Persion)
  2. 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.
  3. Ahn, Y.-C., Lee, I.-B., Lee, K.-H., & Han, J.-H. (2015). Strategic planning design of microalgae biomass-to-biodiesel supply chain network: multi-period deterministic model. Applied Energy, 154, 528-542.
  4. 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.
  5. Azar, A., & Raoufian, A. (2010). Optimization of quality function deployment using linear physical programming. Ministry of Science, Research, and Technology - Tarbiat Modares University. (in Persian).
  6. Babazadeh, R. (2015). Design of liquid biofuel supply chain network from Jatropha plant in Iran under uncertainty. Ministry of Industrial and Systems Engineering - Tehran (in Persian).
  7. Bairamzadeh, & Saidi-Mehrabad M. (2019). An Integrated Hydrocarbon Biofuel and Petroleum Supply Chain: Designing and Planning a Dynamic Supply Chain Network. Quarterly Journal of Energy Policy and Planning Research. 5(2), 97-143 (in Persian).
  8. Cambero, C., Sowlati, T., Marinescu, M., & Röser, D. (2015). Strategic optimization of forest residues to bioenergy and biofuel supply chain. International Journal of Energy Research, 39(4), 439-452.
  9. Das, S. K., Vincent, F. Y., Roy, S. K., & Weber, G. W. (2024). Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach. Expert Systems with Applications, 238,
  10. Datta, A., Hossain, A., & Roy, S. (2019). An overview on biofuels and their advantages and disadvantages. Asian Journal of Chemistry Asian Journal of Chemistry, 31(8).
  11. Delkhosh, F., & Sadjadi, S. J. (2020). A robust optimization model for a biofuel supply chain under demand uncertainty. International Journal of Energy and Environmental Engineering, 11(2), 229-245.
  12. Duc, D. N., Meejaroen, P., & Nananukul, N. (2021). Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration. Energy Reports, 7, 6833-6843
  13. 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.
  14. Haghighi, A., & Babapour, A. (2018). Using renewable energy as an effective way to reduce environmental pollution. Renewable and New Energy. (in Persion).
  15. Jakubowski, M. (2022). Cultivation potential and uses of Paulownia wood: A review. Forests, 13(5),
  16. 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, 10004.
  17. Jiménez, M. (1996). Ranking fuzzy numbers through the comparison of its expected intervals. International journal of uncertainty, fuzziness and knowledge-based systems, 4(04), 379-388.
  18. Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European journal of operational research, 177(3), 1599-1609.
  19. Kandpal, V., Jaswal, A., Santibanez Gonzalez, E. D., & Agarwal, N. (2024). Environmental Impact Assessment and Sustainable Energy Transition. In Sustainable Energy Transition: Circular Economy and Sustainable Financing for Environmental, Social and Governance (ESG) Practices, 273-288. Springer.
  20. Karakosta, C., & Askounis, D. (2010). Developing countries' energy needs and priorities under a sustainable development perspective: A linguistic decision support approach. Energy for Sustainable Development, 14(4), 330-338.
  21. Kim, J., Realff, M. J., Lee, J. H., Whittaker, C., & Furtner, L. (2011). Design of biomass processing network for biofuel production using an MILP model. Biomass and Bioenergy, 35(2), 853-871.
  22. Kiwjaroun, C., Tubtimdee, C., & Piumsomboon, P. (2009). LCA studies comparing biodiesel synthesized by conventional and supercritical methanol methods. Journal of Cleaner Production, 17(2), 143-153.
  23. 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.
  24. 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.
  25. 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.
  26. Ren, J., An, D., Liang, H., Dong, L., Gao, Z., Geng, Y., Zhu, Q., Song, S., & Zhao, W. (2016). Life cycle energy and CO2 emission optimization for biofuel supply chain planning under uncertainties. Energy, 94, 670-682.
  27. Rezaei, M., Chaharsooghi, S., Husseinzadeh Kashan, A., & Babazadeh, R. (2020). Optimal design and planning of biodiesel supply chain network: a scenario-based robust optimization approach. International Journal of Energy and Environmental Engineering, 11, 111-128.
  28. Salamanca, H., Chang, M., & Tian, X. (2012). Costs and prices for renewable energy development in industrialized countries and applications to China. Frontiers of Environmental Science & Engineering, 6, 403-411.
  29. Salmannejad, M., Mirghafouri, S. H. A., Andalib Ardakani, D., & Mirfakhraldini, S. H. (2022). Optimization of hospital supply chain under uncertainty: Application of fuzzy goal programming. Journal of Industrial Management Perspective, 12(1), 161-191. (in Persion)
  30. Santibañez-Aguilar, J. E., Morales-Rodriguez, R., González-Campos, J. B., & Ponce-Ortega, J. M. (2016). Stochastic design of biorefinery supply chains considering economic and environmental objectives. Journal of cleaner production, 136, 224-245.
  31. Shavvalpour , Asadi M., & Ghaderi H.  (2018). An Optimization Model for Biofuel Supply Chain. Iranian Journal of Energy , 21(1): 123-142. (in Persion).
  32. Soleimani, A., & Abrumand Azar, P. (2015). A review of renewable energies and their environmental impacts in Iran. In International Conference on Research in Science and Technology. (in Persion)
  33. Tan, Y., Wang, X., & Zheng, Y. (2018). Modeling and daily operation optimization of a distributed energy system considering economic and energy aspects. International Journal of Energy Research, 42(11), 3477-3495.
  34. Tang, D. Y. Y., Yew, G. Y., Koyande, A. K., Chew, K. W., Vo, D.-V. N., & Show, P. L. (2020). Green technology for the industrial production of biofuels and bioproducts from microalgae: a review. Environmental Chemistry Letters, 18, 1967-1985.
  35. Wassie, S. B. (2020). Natural resource degradation tendencies in Ethiopia: a review. Environmental systems research, 9(1), 1-29.
  36. Zarei, M., Cherif, A., Yoon, H.-J., Liu, J. J., & Lee, C.-J. (2023). Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method. Green Chemistry, 25(10), 4067-4075. (in Persion)
  37. 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 Persion).
  38. Zarrinpour, N., & Zahra, A. (2020). A robust optimization model for strategic and operational design of the oil supply chain. Journal of Industrial Management Perspective, 10(4), 155-191. (in Persion)
  39. Zhang, Y., & Jiang, (2017). Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil
  40. 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.

Files

Share

How to cite

Statistics