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Inspection strategies for quality products with rewards in a multi-stage production.

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Title: Inspection strategies for quality products with rewards in a multi-stage production.
Authors: Satheesh Kumar, R.1 (AUTHOR) satheeshram74@gmail.com, Nagarajan, A.1 (AUTHOR)
Source: Journal of Control & Decision. Oct2023, Vol. 10 Issue 4, p596-609. 14p.
Subjects: Machine part failures, Product quality, Markov processes, Manufacturing processes, Dynamic programming
Abstract: In a multi-stage manufacturing system, defective components are generated due to deteriorating machine parts and failure to install the feed load. In these circumstances, the system requires inspection counters to distinguish imperfect items and takes a few discreet decisions to produce impeccable items. Whereas the prioritisation of employee appreciation and working on reward is one of the important policies to improve productivity. Here we look at the multi-stage manufacturing system as an M/PH/1 queue model and rewards are given for using certain inspection strategies to produce the quality items. A matrix analytical method is proposed to explain a continuous-time Markov process in which the reward points are given to the strategy of inspection in each state of the system. By constructing the value functions of this dynamic programming model, we derive the optimal policy and the optimal average reward of the entire system in the long run. In addition, we obtain the percentage of time spent on each system state for the probability of conformity and non-conformity of the product over the long term. The results of our computational experiments and case study suggest that the average reward increases due to the actions are taken at each decision epoch for rework and disposal of the non-conformity items. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Control & Decision is the property of Taylor & Francis Ltd 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: In a multi-stage manufacturing system, defective components are generated due to deteriorating machine parts and failure to install the feed load. In these circumstances, the system requires inspection counters to distinguish imperfect items and takes a few discreet decisions to produce impeccable items. Whereas the prioritisation of employee appreciation and working on reward is one of the important policies to improve productivity. Here we look at the multi-stage manufacturing system as an M/PH/1 queue model and rewards are given for using certain inspection strategies to produce the quality items. A matrix analytical method is proposed to explain a continuous-time Markov process in which the reward points are given to the strategy of inspection in each state of the system. By constructing the value functions of this dynamic programming model, we derive the optimal policy and the optimal average reward of the entire system in the long run. In addition, we obtain the percentage of time spent on each system state for the probability of conformity and non-conformity of the product over the long term. The results of our computational experiments and case study suggest that the average reward increases due to the actions are taken at each decision epoch for rework and disposal of the non-conformity items. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Control & Decision is the property of Taylor & Francis Ltd 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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        Text: English
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      – SubjectFull: Markov processes
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              Text: Oct2023
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