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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Bibliographic Details
Published in:Computers & industrial engineering Vol. 197; p. 110541
Main Authors: Sadeghi R., Kiarash, Abadi, Moein Qaisari Hasan, Haapala, Karl R., Huscroft, Joseph R.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2024
Subjects:
Circular economy
Circular supply chain
Hybrid machine-learning algorithm
Sustainability
Circular supply chain
Circular economy
Hybrid machine-learning algorithm
Sustainability
ISSN:0360-8352
Online Access:Get full text
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Summary:•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.
ISSN:0360-8352
DOI:10.1016/j.cie.2024.110541