A Hybrid Decision Support System for the Resource Allocation Problem in Cloud Manufacturing Platforms.
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| Title: | A Hybrid Decision Support System for the Resource Allocation Problem in Cloud Manufacturing Platforms. |
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| Authors: | Kaynak, Sümeyye1 (AUTHOR) sumeyye@sakarya.edu.tr, Kaynak, Baran2 (AUTHOR) kaynak@sakarya.edu.tr, Uygun, Özer3 (AUTHOR) ouygun@sakarya.edu.tr |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Jul2025, Vol. 50 Issue 14, p11257-11268. 12p. |
| Subjects: | Decision support systems, Computational mathematics, Resource allocation, Manufacturing industries, Cloud computing |
| Abstract: | Cloud manufacturing (CMfg) makes it possible to share mass manufacturing resources and capabilities more widely through network and accessible to users as needed based on pay-as-you-go model. Increasing diversity of manufacturing resources and capabilities and the conflicting objectives in cloud manufacturing integration make it difficult to achieve optimum resource allocation. In this paper, a new manufacturing resource allocation model has been developed, with criteria TCQR (time, cost, quality and risk), LD (late delivery), ED (early delivery) and company priority (P) being considered using AHP and genetic algorithm to support the multi-objective decision-making optimization. The model offers good performance in time consumption. With a customers priority matrix, customer can flexibly prioritize the criteria and shape the results suggested by the resource allocation algorithm according to their own priorities. The developed model is evaluated with a numerical case and its accuracy is confirmed. In contrast to studies in the literature, time criterion is examined under 2 different criteria as ED and LD. The evaluation of time criteria in 2 different scales reduces service volatility. Volatility reduces service stability, trust, compliance and quality. Low service volatility will improve the optimal selection. [ABSTRACT FROM AUTHOR] |
| Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Cloud manufacturing (CMfg) makes it possible to share mass manufacturing resources and capabilities more widely through network and accessible to users as needed based on pay-as-you-go model. Increasing diversity of manufacturing resources and capabilities and the conflicting objectives in cloud manufacturing integration make it difficult to achieve optimum resource allocation. In this paper, a new manufacturing resource allocation model has been developed, with criteria TCQR (time, cost, quality and risk), LD (late delivery), ED (early delivery) and company priority (P) being considered using AHP and genetic algorithm to support the multi-objective decision-making optimization. The model offers good performance in time consumption. With a customers priority matrix, customer can flexibly prioritize the criteria and shape the results suggested by the resource allocation algorithm according to their own priorities. The developed model is evaluated with a numerical case and its accuracy is confirmed. In contrast to studies in the literature, time criterion is examined under 2 different criteria as ED and LD. The evaluation of time criteria in 2 different scales reduces service volatility. Volatility reduces service stability, trust, compliance and quality. Low service volatility will improve the optimal selection. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 2193567X |
| DOI: | 10.1007/s13369-024-09641-y |
