Quality Control Testing with Experimental Practical Illustrations under the Modified Lindley Distribution Using Single, Double, and Multiple Acceptance Sampling Plans
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Tashkandy, Yusra
[1
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Emam, Walid
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King Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi ArabiaKing Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia
Emam, Walid
[1
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Ali, M. Masoom
[2
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Yousof, Haitham M. M.
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Benha Univ, Dept Stat Math & Insurance, Banha 13511, EgyptKing Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia
Yousof, Haitham M. M.
[3
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Ahmed, Basma
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Higher Inst Specif Studies, Dept Informat Syst, Giza 12561, EgyptKing Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia
Ahmed, Basma
[4
]
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[1] King Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia
[2] Ball State Univ, Dept Math Sci, Muncie, IN 47306 USA
[3] Benha Univ, Dept Stat Math & Insurance, Banha 13511, Egypt
Quality control testing under acceptance sampling plans involves inspecting a representative sample of products or materials from a larger lot or batch to determine whether the lot meets predetermined quality standards. In this research, the modified Lindley distribution is used as a model for lifetime study. When a life test is amputated at a pre-appropriated time to decide on the admission or refusal of the submitted batches, the problems of the single, double, and multiple (three and four stages) acceptance sampling strategies are introduced. The optimal sample sizes are computed for single, double, and multiple acceptance sampling plans to ensure that the veritable mean life is greater than the prescribed mean life at the stipulated consumer's risk. The operating characteristic functions are investigated at diverse quality levels. For single, double, and multiple acceptance sampling plans, the minimal ratios of the veritable mean life to the prescribed mean life at the established percent of the producer's risk are obtained. To demonstrate the uses of single, double, and multiple, some numerical experiments are presented.
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[1]
Ahmed B., 2023, Statistics, Optimization & Information Computing, V11, P409
[2]
Ahmed B., 2022, ANN DATA SCI, DOI [10.1007/s40745-022-00451-3, DOI 10.1007/S40745-022-00451-3]
机构:
Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea
Park, H
Jun, CH
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Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea
Jun, CH
Kim, KJ
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Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea
机构:
Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea
Park, H
Jun, CH
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Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea
Jun, CH
Kim, KJ
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Pohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South KoreaPohang Univ Sci & Technol, Div Mech & Ind Engn, Pohang 790784, South Korea