Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets
Purpose: Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive...
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Ayandegan Institute of Higher Education, Tonekabon,
2024-08-01
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| Series: | تصمیم گیری و تحقیق در عملیات |
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| Online Access: | https://www.journal-dmor.ir/article_199979_50d6ea02d6e97890f465b830323094e0.pdf |
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| author | Farzaneh MansooriMooseloo Maghsoud Amiri Mohammad Taghi Taghavi Fard Mostafa Hajiaghaei-Keshteli |
| author_facet | Farzaneh MansooriMooseloo Maghsoud Amiri Mohammad Taghi Taghavi Fard Mostafa Hajiaghaei-Keshteli |
| author_sort | Farzaneh MansooriMooseloo |
| collection | DOAJ |
| description | Purpose: Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive conservatism, they increased supply chain costs, making them unattractive to investors. Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. Then, a data-driven optimization model was designed to optimize the biomass-to-bioethanol supply chain network, addressing the issues of previous classic approaches.Findings: The findings of this study are presented in two categories: strategic and operational decisions. The strategic section focuses on determining the optimal locations for biomass cultivation, preprocessing centers, and refineries. In the operational section, the optimal amounts of biomass sent to preprocessing centers and refineries were determined.Originality/Value: This study, by producing robust solutions without the conservatism of traditional robust optimization approaches, can significantly attract public and private sector investors. Additionally, using a three-objective model based on a sustainable development approach that simultaneously considers economic, social, and environmental components, enhances the comprehensiveness of this research, providing more realistic and detailed results. |
| format | Article |
| id | doaj-art-05f08060290e43cbbd53bfed7cbf3247 |
| institution | Kabale University |
| issn | 2538-5097 2676-6159 |
| language | fas |
| publishDate | 2024-08-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | تصمیم گیری و تحقیق در عملیات |
| spelling | doaj-art-05f08060290e43cbbd53bfed7cbf32472025-01-30T15:03:45ZfasAyandegan Institute of Higher Education, Tonekabon,تصمیم گیری و تحقیق در عملیات2538-50972676-61592024-08-019232735210.22105/dmor.2024.461901.1849199979Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty setsFarzaneh MansooriMooseloo0Maghsoud Amiri1Mohammad Taghi Taghavi Fard2Mostafa Hajiaghaei-Keshteli3Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.Department of Industrial Engineering, Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Mexico.Purpose: Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive conservatism, they increased supply chain costs, making them unattractive to investors. Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. Then, a data-driven optimization model was designed to optimize the biomass-to-bioethanol supply chain network, addressing the issues of previous classic approaches.Findings: The findings of this study are presented in two categories: strategic and operational decisions. The strategic section focuses on determining the optimal locations for biomass cultivation, preprocessing centers, and refineries. In the operational section, the optimal amounts of biomass sent to preprocessing centers and refineries were determined.Originality/Value: This study, by producing robust solutions without the conservatism of traditional robust optimization approaches, can significantly attract public and private sector investors. Additionally, using a three-objective model based on a sustainable development approach that simultaneously considers economic, social, and environmental components, enhances the comprehensiveness of this research, providing more realistic and detailed results.https://www.journal-dmor.ir/article_199979_50d6ea02d6e97890f465b830323094e0.pdfrenewable energybioethanol supply chaindata-driven robust optimizationdisjunctive uncertainty sets |
| spellingShingle | Farzaneh MansooriMooseloo Maghsoud Amiri Mohammad Taghi Taghavi Fard Mostafa Hajiaghaei-Keshteli Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets تصمیم گیری و تحقیق در عملیات renewable energy bioethanol supply chain data-driven robust optimization disjunctive uncertainty sets |
| title | Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets |
| title_full | Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets |
| title_fullStr | Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets |
| title_full_unstemmed | Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets |
| title_short | Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets |
| title_sort | designing and planning a bioethanol supply chain network under uncertainty using a data driven robust optimization model under disjunctive uncertainty sets |
| topic | renewable energy bioethanol supply chain data-driven robust optimization disjunctive uncertainty sets |
| url | https://www.journal-dmor.ir/article_199979_50d6ea02d6e97890f465b830323094e0.pdf |
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