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      CoCoSo method-based optimization of cryogenic drilling on multi-walled carbon nanotubes reinforced composites

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          Most cited references35

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          A mathematical theory of communication

          C. Shannon (2001)
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            Determining objective weights in multiple criteria problems: The critic method

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              A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems

              Purpose The purpose of this paper is to discuss the advantage of a combinatory methodology presented in this study. The paper suggests that the comparison with results of previously developed methods is in high agreement. Design/methodology/approach This paper introduces a combined compromise decision-making algorithm with the aid of some aggregation strategies. The authors have considered a distance measure, which originates from grey relational coefficient and targets to enhance the flexibility of the results. Hence, the weight of the alternatives is placed in the decision-making process with three equations. In the final stage, an aggregated multiplication rule is employed to release the ranking of the alternatives and end the decision process. Findings The authors described a real case of choosing logistics and transportation companies in France from a supply chain project. Some comparisons such as sensitivity analysis approach and comparing to other studies and methods provided to validate the performance of the proposed algorithm. Originality/value The algorithm has a unique structure among MCDM methods which is presented for the first time in this paper.
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                Author and article information

                Journal
                International Journal on Interactive Design and Manufacturing (IJIDeM)
                Int J Interact Des Manuf
                Springer Science and Business Media LLC
                1955-2513
                1955-2505
                February 2023
                May 11 2022
                February 2023
                : 17
                : 1
                : 279-297
                Article
                10.1007/s12008-022-00894-1
                77cb5dc7-2ed6-420b-8c0a-8bf91462e33c
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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