A Markovian approach to determining optimum process target levels for a multi-stage serial production system

Bowling, Shannon R.; Khasawneh, Mohammad T.; Kaewkuekool, Sittichai; Cho, Byung Rae

doi:10.1016/s0377-2217(03)00429-6

Cited by 73 publications

(32 citation statements)

References 31 publications

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“…Selim and Al-Zu'bi [24] further refined the Markovian model presented by Bowling et al [22] and corrected an error in the model for multiple features manufactured in series. Peng and Khasawneh [25] modified the dual feature approach proposed by Khasawneh et al [23] and applied it to a production system where a sampling plan was used to inspect feature quality, rather than a 100% inspection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Al-Sultan and Pulak [20] were the first to consider multiple feature in series, which was an extension of [21] but for two manufacturing stages. Bowling et al [22] and Khasawneh et al [23] introduced Markovian modelling to the optimal mean setting problem for serial and parallel process, respectively. Prior to this, rework was considered as a static one-off cost, however, rework is dynamic and several rework operations maybe required before features either conform or are scrapped.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, the Markovian method (outlined by Bowling et al [22] and Selim and Al-Zubi [24]), which implicitly uses the same mean for every iteration, is justified.…”

Section: Optimal Mean Setting -Single Featurementioning

confidence: 99%

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Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

Dodd

Scanlan

Marsh

et al. 2015

Int J Adv Manuf Technol

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The setting of a process mean for a manufacturing process which frequently produces scrap and rework, can significantly affect profitability. Optimal mean setting is a methodology by which the process mean is adjusted to maximise profit. This paper studies the dynamics of the problem and investigates the possibility of applying different process means to each rework iteration, to further maximise profit. A proof is given confirming there is only one optimal mean that applies over all rework iterations in the single feature case. However, applying similar reasoning to a dual feature case led to the development of a new optimal mean setting methodology which outperformed the existing approach in terms of the maximum expected profit.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

Dodd

Scanlan

Marsh

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…al. [4] proposed an absorbing Markov chain model for determining the optimal process means. According to their method, matrix M that is the fundamental matrix containing the expected number of transitions from a non-absorbing state to another non-absorbing state before absorption occurs can be obtained by the following equation,…”

Section: Practical Concepts Of Quality Controlmentioning

confidence: 99%

New Models of Acceptance Sampling Plans

Nezhad¹

2012

Practical Concepts of Quality Control

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“…The first stage probability of rejecting the batch is     The long run absorption probability matrix F can then be calculated as follows [14] , , , and c c c c together with their probability of rejecting or accepting the batch, of which the ones in bold are feasible. Subsequently, Equation (16) is numerically solved for all feasible sets of Table 3.…”

Section:  mentioning

confidence: 99%

Analysing Acceptance Sampling Plans by Markov Chains

Mirabi

Nezhad

2011

The South African Journal of Industrial Engineering

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In this research, a Markov analysis of acceptance sampling plans in a single stage and in two stages is proposed, based on the quality of the items inspected. In a stage of this policy, if the number of defective items in a sample of inspected items is more than the upper threshold, the batch is rejected. However, the batch is accepted if the number of defective items is less than the lower threshold. Nonetheless, when the number of defective items falls between the upper and lower thresholds, the decision-making process continues to inspect the items and collect further samples. The primary objective is to determine the optimal values of the upper and lower thresholds using a Markov process to minimise the total cost associated with a batch acceptance policy. A solution method is presented, along with a numerical demonstration of the application of the proposed methodology. OPSOMMINGIn hierdie navorsing word 'n Markov-ontleding gedoen van aannamemonsternemingsplanne wat plaasvind in 'n enkele stap of in twee stappe na gelang van die kwaliteit van die items wat geïnspekteer word. Indien die eerste monster toon dat die aantal defektiewe items 'n boonste grens oorskry, word die lot afgekeur. Indien die eerste monster toon dat die aantal defektiewe items minder is as 'n onderste grens, word die lot aanvaar. Indien die eerste monster toon dat die aantal defektiewe items in die gebied tussen die boonste en onderste grense lê, word die besluitnemingsproses voortgesit en verdere monsters word geneem. Die primêre doel is om die optimale waardes van die booonste en onderste grense te bepaal deur gebruik te maak van 'n Markov-proses sodat die totale koste verbonde aan die proses geminimiseer kan word. 'n Oplossing word daarna voorgehou tesame met 'n numeriese voorbeeld van die toepassing van die voorgestelde oplossing.

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A Markovian approach to determining optimum process target levels for a multi-stage serial production system

Cited by 73 publications

References 31 publications

Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

New Models of Acceptance Sampling Plans

Analysing Acceptance Sampling Plans by Markov Chains

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