| Title |
Development of a non-dominated sorting genetic algorithm for implementing circular economy strategies in the concrete industry |
| ID_Doc |
21352 |
| Authors |
NoParast, M; Hematian, M; Ashrafian, A; Amiri, MJT; AzariJafari, H |
| Title |
Development of a non-dominated sorting genetic algorithm for implementing circular economy strategies in the concrete industry |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.spc.2021.02.009 |
| Abstract |
The concrete manufacturing supply chain is one of the most carbon-intensive systems in the construction industry. Decision-making based on a multi-objective approach that accommodates the environmental impacts of concrete manufacturing has been rarely investigated. To fill this gap, a sustainable closedloop supply chain (SCLSC) model was conceived to capture the effect of different circular systems. The proposed model consists of different sub-systems including customers, suppliers, and manufacturing and recycling stations. An evolutionary-based algorithm named non-dominated sorting genetic algorithm was incorporated to solve the solutions. The SCLSC model was implemented on two scenarios based on inhouse or outside recycling plants to minimize the quarry of natural resources, the transportation cost, and the greenhouse gas (GHG) emissions. General Algebraic Modeling System (GAMS) programming software was used to validate the model and to verify the obtained results from the model. The Pareto solution results show that larger incorporation of recycled aggregates in concrete production can lower the excavation of quarries. Furthermore, although a decision for incorporating a green (net-zero GHG) cement contributes to reducing the GHG emissions of the supply chain, the transportation distance determines whether the demand would be supplied from the green cement source. The provision of an in-house concrete recycling unit can reduce the GHG emissions of the supply chain by 14% while the cost and virgin aggregate demand increase by 24% and 16%, respectively compared to a system that has an outside recycling plant. The validation process shows that the GAMS software needs additional steps to conduct the multi-objective Pareto solutions. Similar values were obtained from the proposed algorithm compared to the outcome of GAMS. The computational results prove the capability of the proposed SCLSC model and the applicability of the implemented solution approach. This approach can facilitate the decisionmaking process due to its effectiveness in improving customers' economic motivations and in reducing the environmental impacts. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. |
| Author Keywords |
Multi-objective optimization; Natural resource consumption; Production cost; Greenhouse gas emissions |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000674195700006 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
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