A hybrid machine learning solution for redesigning sustainable circular energy supply chains
•A sustainable circular decision-making model is developed.•A hybrid machine learning solution is presented.•A multi-objective optimization model is proposed.•Empirical data includes the renewable energy supply chain of Oregon state.•Data and source code of algorithms are available on GitHub. Sustai...
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| Vydané v: | Computers & industrial engineering Ročník 197; s. 110541 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.11.2024
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| ISSN: | 0360-8352 |
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| Abstract | •A sustainable circular decision-making model is developed.•A hybrid machine learning solution is presented.•A multi-objective optimization model is proposed.•Empirical data includes the renewable energy supply chain of Oregon state.•Data and source code of algorithms are available on GitHub.
Sustainability development goals require decision-makers to incorporate social and environmental indicators in their economic models using innovative solutions, such as a sustainable circular economy. This paper presents an innovative integrated production and logistic model for a circular economy using multi-objective optimization. Empirical data includes a renewable energy supply chain. We assess the sustainability performance of the proposed decision-making model by simultaneously considering production and logistics costs, carbon emissions, and the number of jobs created. The case study is optimized with an exact method, and a hybrid machine-learning algorithm solves large-scale numerical examples. The paper’s main contributions include movable manufacturers, uncertain parameters, a hybrid machine learning algorithm, and empirical data in the proposed decision-making model. The findings show that a moveable facility can substantially decrease total cost and carbon emissions. Sensitivity analysis shows that changes in moveable capacity and percent yield considerably impact the objectives. Findings show that decision-makers can achieve cost parity with fossil-based sources when employing circular supply chain management. |
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| AbstractList | •A sustainable circular decision-making model is developed.•A hybrid machine learning solution is presented.•A multi-objective optimization model is proposed.•Empirical data includes the renewable energy supply chain of Oregon state.•Data and source code of algorithms are available on GitHub.
Sustainability development goals require decision-makers to incorporate social and environmental indicators in their economic models using innovative solutions, such as a sustainable circular economy. This paper presents an innovative integrated production and logistic model for a circular economy using multi-objective optimization. Empirical data includes a renewable energy supply chain. We assess the sustainability performance of the proposed decision-making model by simultaneously considering production and logistics costs, carbon emissions, and the number of jobs created. The case study is optimized with an exact method, and a hybrid machine-learning algorithm solves large-scale numerical examples. The paper’s main contributions include movable manufacturers, uncertain parameters, a hybrid machine learning algorithm, and empirical data in the proposed decision-making model. The findings show that a moveable facility can substantially decrease total cost and carbon emissions. Sensitivity analysis shows that changes in moveable capacity and percent yield considerably impact the objectives. Findings show that decision-makers can achieve cost parity with fossil-based sources when employing circular supply chain management. |
| ArticleNumber | 110541 |
| Author | Sadeghi R., Kiarash Abadi, Moein Qaisari Hasan Huscroft, Joseph R. Haapala, Karl R. |
| Author_xml | – sequence: 1 givenname: Kiarash surname: Sadeghi R. fullname: Sadeghi R., Kiarash email: jsadeghi@ncat.edu organization: Department of Marketing and Supply Chain Management, Willie A. Deese College of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA – sequence: 2 givenname: Moein Qaisari Hasan surname: Abadi fullname: Abadi, Moein Qaisari Hasan organization: Department of Operations Management & Operations Research, ESSEC Business School, Cergy, France – sequence: 3 givenname: Karl R. surname: Haapala fullname: Haapala, Karl R. organization: School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA – sequence: 4 givenname: Joseph R. surname: Huscroft fullname: Huscroft, Joseph R. organization: Department of Marketing and Supply Chain Management, Willie A. Deese College of Business and Economics, North Carolina A&T State University, Greensboro, NC 27411, USA |
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| SubjectTerms | Circular economy Circular supply chain Hybrid machine-learning algorithm Sustainability |
| Title | A hybrid machine learning solution for redesigning sustainable circular energy supply chains |
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