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Optimized np Attribute Control Chart Using Triple Sampling

Jose Jorge Muñoz (josemunozjm@unimagdalena.edu.co), Manuel J. Campuzano (mcampuzano@unimagdalena.edu.co) and Jaime Mosquera
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Jose Jorge Muñoz: Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia
Manuel J. Campuzano: Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia
Jaime Mosquera: Escuela de Estadística, Universidad del Valle, Cali 760032, Colombia

Mathematics, 2022, vol. 10, issue 20, 1-21

Abstract: This paper studies an attribute control chart for monitoring the number of nonconforming items using a triple sampling (TS-np) which has not yet been applied to attribute control charts. The chart design and procedure for the decision about the state of the process are given. Mathematical expressions for the average run length ( ARL ) for in-control and out-of-control processes and the average sample number (ASN) are given. A bi-objective genetic algorithm that seeks to minimize the ASN and the probability of type 2 error is implemented in order to optimize the design of the TS-np control chart. A comparison between TS-np, single sampling np (SS-np), double sampling np (DS-np), and multiple dependent state repetitive sampling (MDSRS) control charts is carried out in terms of the out-of-control average run length ( A R L 1 ). Tables of A R L 1 values for TS-np are presented in comparison with MDSRS and DS-np for various scenarios. The operation of the proposed control chart is shown through simulated data. Finally, it is concluded that the proposed TS-np chart has a better performance in terms of A R L 1 detecting small and moderate shifts in the process nonconforming rate in-control ( p 0 ) compared with MDSRS and DS-np.

Keywords: control chart; attributes control chart; triple sampling; double sampling; average run length; average sample number; np control chart (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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