| Title | Sustainability assessment of palm oil industry 4.0 technologies in a circular economy applications based on interval-valued Pythagorean fuzzy rough set-FWZIC and EDAS methods |
|---|---|
| ID_Doc | 28716 |
| Authors | Ibrahim, HA; Zaidan, AA; Qahtan, S; Zaidan, BB |
| Title | Sustainability assessment of palm oil industry 4.0 technologies in a circular economy applications based on interval-valued Pythagorean fuzzy rough set-FWZIC and EDAS methods |
| Year | 2023 |
| Published | |
| Abstract | The palm oil industry is one of the most competitive industries and must comply with industry standards for flexible supply chain operations, productivity, and sustainability. Many researchers have demonstrated that Industry 4.0 technologies in a circular economy and sustainability practices (I4.0-in-a-CE-and-SPs) present promising future research opportunities, particularly for an industry with sustainability challenges. Hence, determining the most sustainable I4.0-in-a-CE-and-SPs application is critical for the palm oil industry. However, this process is deemed as a multiple attributes decision-making (MADM) problem due to the presence of three issues, namely, multiple sustainability performance attributes, uncertainty of the attribute's importance level, and data variation. Therefore, an MADM solution is necessary to address these issues. This study extended the fuzzy weighted with zero inconsistency (FWZIC) method with an interval-valued Pythagorean fuzzy rough set (IVPFRS) and integrated it with the evaluation based on distance from average solution (EDAS) method to rank I4.0 -in-a-CE-and-SP applications. The research method starts with the creation of a decision matrix (DM) based on the intersection of 26 I4.0-in-a-CE-and-SPs applications and 14 sustainability performance attributes. The IVPFRS-FWZIC method is subsequently developed to determine the weights of the sustainability performance attributes. The EDAS method uses these weights and the constructed DM to rank I4.0-in-a-CE-and-SPs applications. The robustness of the proposed methods was evaluated using sensitivity analysis, the Spearman's correlation coefficient test, and comparison analysis.(c) 2023 Elsevier B.V. All rights reserved. |
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