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All Journal Data Science: Journal of Computing and Applied Informatics Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Ishola, Christie Y.
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Computer Science & IT Mathematics

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Fuzzy Approach For Determining Statistical Process Control (Spc) Tools Location On Production Floor Ishola, Christie Y.; Olabode, Adewoye S.
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v8.i1-17137

Abstract

Statistical Process Control (SPC) is a technical tool that is used to control and to improve almost any kind of process. However, because of cost consideration, management need to decide which process should apply SPC. In this paper, we propose the use of probability and fuzzy membership function to determine SPC allocation. Conditional probability is used to analyse process failure rate and process repair rate. Then, using Markov Matrix, we calculate the probability of out-of-control process (PO). Nevertheless, in a production line that consists of many parts, the probability value is not adequate to be used as a reference to determine SPC allocation. There are cases for instance, where the value of PO in one part does not mean the same as in other parts since each part may have different sensitivity degree to the final product. For example 0.25 of PO in part 1 may have higher influence to the final product compare to 0.25 of PO in part 2 or part 3. Furthermore, we cannot randomly choose one of those parts to apply SPC or even decide to apply SPC in all parts of the production line. To overcome this problem we propose fuzzy membership function that uses linguistic terms and degree of memberships to analyse PO instead of the probability values. By this mean, the SPC allocation could be determined without ambiguity. For this purpose, the membership function is classified into three categories, namely LOW, MEDIUM and HIGH. Any part with PO fall into the “HIGH” category and high degree of membership is prioritized to apply SPC.
Numerical Solution of Fractional Order Differential Equations by Chebyshev Least Squares Approximation Method Adebisi, Ajimot F.; Uwaheren, Ohigweren A.; Oseni, Wasiu A.; Ishola, Christie Y.; Peter, Olumuyiwa James
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.31034

Abstract

In this paper, fractional differential equations (FDEs) are solved numerically using the least squares method (LSM). Shifted Chebyshev polynomials are used as the basis functions, and the results are compared with the exact solutions. Several numerical examples are presented to illustrate the theoretical results and are also compared with the outcomes obtained from other numerical methods. It is found that the results of the proposed approximate method converge rapidly to the exact solutions.
Co-Authors Adebisi, Ajimot F. Olabode, Adewoye S. Oseni, Wasiu A. Peter, Olumuyiwa James Uwaheren, Ohigweren A.