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A New Fuzzy TOPSIS-Based Machine Learning Framework for Minimizing Completion Time in Supply Chains

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Abstract

This research focuses on the role of internal factors in small and medium-scale supply chains in developing countries, enhancing the product completion time in fuzzy conditions. In this research, a 3-phase fuzzy-based framework is proposed to address the product completion time problem. In the first phase, using quantitative research, human resource, machinery, material, technology, and social environment are identified as the group factors that can have the most impact on product completion time. Then, in the next phase, a supervised machine-learning algorithm is proposed to classify the production alternatives according to their actual status of the effective internal factor. Then, in the third phase, a new fuzzy-based heuristic is developed to generate production alternatives and evaluate and select the best one. The outcomes show that the layout (0.636), material (0.602), maintenance (0.584), human resource (0.56), and technology (0.553) affect the product completion time, respectively. The findings also indicated that the proposed Fuzzy-TOPSIS heuristic is capable of reducing the product completion time in a range between 0 and 10.3%.

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Notes

  1. https://www.statista.com/statistics/318732/share-of-economic-sectors-in-the-gdp-in-malaysia/. Retrieved in 01/08/2020.

  2. https://www.ceicdata.com/en/malaysia/number-of-bankruptcies/number-of-bankruptcies. Retrieved in 01/08/2020.

  3. https://tradingeconomics.com/malaysia/new-businesses-registered-number-wb-data.html#:~:text=New%20businesses%20registered%20(number)%20in,compiled%20from%20officially%20recognized%20sources. Retrieved in 01/08/2020.

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Acknowledgements

Authors would like to thanks anonymous reviewers and editors for their positive comments.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

    Fahad Kh A O H Alazemi & Mohd Khairol Anuar Bin Mohd Ariffin

  2. Department of Aerospace Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

    Faizal Bin Mustapha & Eris Elianddy Bin Supeni

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  1. Fahad Kh A O H Alazemi
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  2. Mohd Khairol Anuar Bin Mohd Ariffin
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  3. Faizal Bin Mustapha
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Correspondence to Mohd Khairol Anuar Bin Mohd Ariffin.

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Alazemi, F.K.A.O.H., Ariffin, M.K.A.B.M., Mustapha, F.B. et al. A New Fuzzy TOPSIS-Based Machine Learning Framework for Minimizing Completion Time in Supply Chains. Int. J. Fuzzy Syst. 24, 1669–1695 (2022). https://doi.org/10.1007/s40815-021-01226-3

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