Application of Genetic Algorithm to Minimize Total Cost of Double Acceptance Sampling Plan

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Published: Jan 30, 2020
Keywords:
Genetic Algorithm Double Acceptance Sampling Plan Optimization Acceptance Number of Defectives

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Wimonmas Bamrungsetthapong
Division of Applied Statistics, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, Pathum Thani
Pramote Charongrattanasakul
Division of Mathematics, Faculty of Science and Technology, Rajamangala University of Technology Krungthep, Bangkok

Abstract

Double Acceptance Sampling Plan based on the producer's risk and consumer's risk is wildly used tools to establish the optimal product inspection plans. In actually, producer's risk and consumer's risk are unknown values which only the proportion of defective in the batch production is known. This research aims to increase efficiency of selecting optimal double acceptance sampling plan under the minimum total cost for inspection (TC). The application of Genetic Algorithm is used to calculate the optimal sample size (n1, n2) and the optimal acceptance number of defectives (c1, c2). The results show that the optimal constraint n1 = n2 and c1 ≤ c2 – 1 gives the minimum total cost while the optimal constraint n1 = 2n2 and c1 ≤ c2 – 1 gives maximum number of defective item detected (Nd) respectively. In addition, when the sample size increases, the results of the analysis show that total cost for inspection (TC), defective items detected and the Average Total Inspection (ATI) are increasing while the probability of accepting the lot (Pa) and the Average Outgoing Quality (AOQ) are decreasing.

Article Details

Issue
Vol. 30 No. 1 (2020): January-March, 2020
Section
Applied Science Research Articles

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