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OPTIMIZATION OF A MARKOVIAN BAYESIAN SINGLE SAMPLING PLAN IN COAL PRODUCTION.

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Title: OPTIMIZATION OF A MARKOVIAN BAYESIAN SINGLE SAMPLING PLAN IN COAL PRODUCTION.
Authors: V., KAVIYARASU1 kaviyarasu@buc.edu.in, E., KARTHICK2 karthick.statistics@buc.edu.in
Source: Reliability: Theory & Applications. Mar2026, Vol. 21 Issue 1, p224-234. 11p.
Subjects: Acceptance sampling, Statistical models, Optimization algorithms, Statistical sampling, Quality control, Energy consumption, Quality standards
Abstract: Coal serves as a critical energy source for power generation and industrial operations, the demand for coal is rising, yet production levels are declining. In statistical quality control a statistical procedure can play a crucial role in boosting production efficiency and output. Techniques such as proximate and ultimate analysis, calorific value assessments, and ash fusion testing are employed to evaluate coal's composition and performance. Effective quality control techniques not only maximizes energy output but also reduces environmental impact and operational complications. This article introduces a stochastic approach to designing acceptance sampling techniques, aimed at enhancing the optimization of the Markov method for classifying conforming and non-conforming items. A Bayesian single sampling plan includes a defined sample size, an acceptance number, and a decision rule based on lower and upper thresholds, ensuring compliance with both buyer and vendor specifications for lot acceptance. The design methodology is developed using a Markov model to create a single sampling plan under Gamma-Poisson conditions. The sampling plan's performance is evaluated using Acceptable Quality Level (AQL) and Limiting Quality Level (LQL), optimizing thresholds through the minimum angle method. The practical applicability of this sampling plan is demonstrated through numerical examples within a coal manufacturing environment. [ABSTRACT FROM AUTHOR]
Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: OPTIMIZATION OF A MARKOVIAN BAYESIAN SINGLE SAMPLING PLAN IN COAL PRODUCTION.
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  Data: <searchLink fieldCode="JN" term="%22Reliability%3A+Theory+%26+Applications%22">Reliability: Theory & Applications</searchLink>. Mar2026, Vol. 21 Issue 1, p224-234. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Acceptance+sampling%22">Acceptance sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+standards%22">Quality standards</searchLink>
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  Label: Abstract
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  Data: Coal serves as a critical energy source for power generation and industrial operations, the demand for coal is rising, yet production levels are declining. In statistical quality control a statistical procedure can play a crucial role in boosting production efficiency and output. Techniques such as proximate and ultimate analysis, calorific value assessments, and ash fusion testing are employed to evaluate coal's composition and performance. Effective quality control techniques not only maximizes energy output but also reduces environmental impact and operational complications. This article introduces a stochastic approach to designing acceptance sampling techniques, aimed at enhancing the optimization of the Markov method for classifying conforming and non-conforming items. A Bayesian single sampling plan includes a defined sample size, an acceptance number, and a decision rule based on lower and upper thresholds, ensuring compliance with both buyer and vendor specifications for lot acceptance. The design methodology is developed using a Markov model to create a single sampling plan under Gamma-Poisson conditions. The sampling plan's performance is evaluated using Acceptable Quality Level (AQL) and Limiting Quality Level (LQL), optimizing thresholds through the minimum angle method. The practical applicability of this sampling plan is demonstrated through numerical examples within a coal manufacturing environment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.24412/1932-2321-2026-190-224-234
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      – Code: eng
        Text: English
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      – SubjectFull: Statistical models
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      – SubjectFull: Optimization algorithms
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      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Quality control
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      – SubjectFull: Energy consumption
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      – SubjectFull: Quality standards
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      – TitleFull: OPTIMIZATION OF A MARKOVIAN BAYESIAN SINGLE SAMPLING PLAN IN COAL PRODUCTION.
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              Text: Mar2026
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              Y: 2026
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