A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics
The escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an efficient...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Supply Chain Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S294986352500055X |
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| author | Mehdi Soltani Tehrani Siamak Noori Ehsan Dehghani |
| author_facet | Mehdi Soltani Tehrani Siamak Noori Ehsan Dehghani |
| author_sort | Mehdi Soltani Tehrani |
| collection | DOAJ |
| description | The escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an efficient supply chain. This study introduces a comprehensive two-stage optimization framework to advance the design and operation of biofuel supply chains. In the initial stage, a novel hybrid methodology integrates data envelopment analysis with artificial neural networks to identify optimal sites for agricultural waste collection facilities. This approach combines the performance assessment strengths of data envelopment analysis with the predictive capabilities of neural networks, enabling a data-informed site selection process. The second stage employs a mixed-integer linear programming model to optimize a closed-loop biofuel supply chain under uncertain conditions, targeting both cost reduction and minimized carbon emissions. A probabilistic scenario-based approach is utilized to address uncertainties, enhancing the model’s real-world applicability. Additionally, the Lagrangian relaxation technique is implemented to achieve precise solutions while preserving computational efficiency. For large-scale scenarios, the study leverages the non-dominated sorting genetic algorithm and multi-objective simulated annealing to generate near-optimal solutions. A practical case study validates the proposed framework and provides decision-makers with clear and actionable strategies to optimize site planning, reduce operational costs, and enhance environmental sustainability in biofuel supply chain management. |
| format | Article |
| id | doaj-art-5cd9cfc43bb84266a641eb2d35e82208 |
| institution | Kabale University |
| issn | 2949-8635 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Supply Chain Analytics |
| spelling | doaj-art-5cd9cfc43bb84266a641eb2d35e822082025-08-20T03:46:50ZengElsevierSupply Chain Analytics2949-86352025-09-011110015510.1016/j.sca.2025.100155A two-stage approach to enhancing biofuel supply chains through predictive and optimization analyticsMehdi Soltani Tehrani0Siamak Noori1Ehsan Dehghani2School of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranCorresponding author.; School of Industrial Engineering, Iran University of Science and Technology, Tehran, IranThe escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an efficient supply chain. This study introduces a comprehensive two-stage optimization framework to advance the design and operation of biofuel supply chains. In the initial stage, a novel hybrid methodology integrates data envelopment analysis with artificial neural networks to identify optimal sites for agricultural waste collection facilities. This approach combines the performance assessment strengths of data envelopment analysis with the predictive capabilities of neural networks, enabling a data-informed site selection process. The second stage employs a mixed-integer linear programming model to optimize a closed-loop biofuel supply chain under uncertain conditions, targeting both cost reduction and minimized carbon emissions. A probabilistic scenario-based approach is utilized to address uncertainties, enhancing the model’s real-world applicability. Additionally, the Lagrangian relaxation technique is implemented to achieve precise solutions while preserving computational efficiency. For large-scale scenarios, the study leverages the non-dominated sorting genetic algorithm and multi-objective simulated annealing to generate near-optimal solutions. A practical case study validates the proposed framework and provides decision-makers with clear and actionable strategies to optimize site planning, reduce operational costs, and enhance environmental sustainability in biofuel supply chain management.http://www.sciencedirect.com/science/article/pii/S294986352500055XSupply chain optimizationArtificial neural networkMulti-objective planningLagrangian approachLocation analysisData envelopment analysis |
| spellingShingle | Mehdi Soltani Tehrani Siamak Noori Ehsan Dehghani A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics Supply Chain Analytics Supply chain optimization Artificial neural network Multi-objective planning Lagrangian approach Location analysis Data envelopment analysis |
| title | A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| title_full | A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| title_fullStr | A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| title_full_unstemmed | A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| title_short | A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| title_sort | two stage approach to enhancing biofuel supply chains through predictive and optimization analytics |
| topic | Supply chain optimization Artificial neural network Multi-objective planning Lagrangian approach Location analysis Data envelopment analysis |
| url | http://www.sciencedirect.com/science/article/pii/S294986352500055X |
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