Maintenance and quality control integrated strategy for manufacturing systems

Bahria, Nadia; Chelbi, Anis; Dridi, Imen Harbaoui; Bouchriha, Hanen

doi:10.1504/ejie.2018.092006

Cited by 16 publications

(7 citation statements)

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“…On the other hand, Zhong and Ma (2017) integrated the "x-bar" control chart and maintenance for two-stage-dependent processes. More recently, Bahria et al (2018) suggested an integrated maintenance and quality model where both PM and CM actions are triggered by the control chart. A mathematical model is developed to find the optimal parameters of the control chart minimizing the average total cost per time unit.…”

Section: Control Chart Production and Maintenancementioning

confidence: 99%

“…The decision variables considered are the production lot size, the safety stock, the sampling plan and overhaul scheduling with an outgoing quality constraint. Bahria et al (2019) extended the work of Bahria et al (2018) by integrating the buffer stock with the control chart and PM and CM both based on quality control results. Salmasnia et al (2019) proposed an integrated model for joint determination of production run length, adaptive control chart parameters and the maintenance policy.…”

Section: Control Chart Production and Maintenancementioning

confidence: 99%

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Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Bahria

Dridi

Chelbi

et al. 2020

JQME

Self Cite

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PurposeThe purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.Design/methodology/approachThe proposed integrated policy is defined and modeled mathematically.FindingsThe paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.Practical implicationsThe paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.Originality/valueThe existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

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Section: Control Chart Production and Maintenancementioning

confidence: 99%

Section: Control Chart Production and Maintenancementioning

confidence: 99%

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Bahria

Dridi

Chelbi

et al. 2020

JQME

Self Cite

View full text Add to dashboard Cite

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“…Replacing a system either too frequently or too less is not advisable as it increases unnecessary maintenance costs or reduces system availability. Therefore, scheduling the optimal replacement cycle and numbers according to some criteria, such as long-run average cost rate (ACR), expected long-run profit rate, or system availability is attracting more and more attention in maintenance activity [3].…”

Section: Introductionmentioning

confidence: 99%

Generalized periodic replacement policies for repairable systems subject to two types of failures

Dong

Liu

et al. 2022

RAIRO-Oper. Res.

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The purpose of this current research is to schedule generalized periodic replacement policies for a single unit system executing random working jobs. The system is subject to two types of failures when it has failed, including a minor failure (Type I failure), which can be thoroughly removed by the minimal repair and a catastrophic failure (Type II failure), which should be rectified by the corrective replacement. To be specific, four distinct periodic replacement models including a periodic replacement first model (Model A1), a modified periodic replacement first model (Model A2), a periodic replacement last model (Model B1), and a modified periodic replacement last model (Model B2) are investigated. The long run average cost rate (ACR) over an infinite time span under different situations is obtained theoretically and optimal periodic replacement interval for each condition is derived analytically. Numerical examples are exhibited to verify the derived results.

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“…The results were satisfactory and decreased the ATS and EATS criteria reported under the VSSI sampling scheme. On the other hand, the combination of the control charts with various maintenance and repair policies and reliability has been considered in previous studies such as He et al, 28 Zhong and Ma, 29 Bahria et al, 30 and Farahani et al 31 in the optimization of the production processes. Therefore, an improvement in the performance of the control chart can significantly enhance the efficiency of a process and should be considered by policymakers.…”

Section: Introductionmentioning

confidence: 99%

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

Shojaie

Imani

2021

Engineering Reports

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It has been proved that the quality control charts with variable sampling schemes are more effective than the classical ones in improving statistical measures. The Average Number of False Alarms (ANF), Average Number of Samples (ANS), Average Number of Inspected Items (ANI), and Adjusted Average Time to Signal (AATS) have been the most important statistical measures always being attending in the evaluation of control charts. Thus, this study was conducted aiming at illustrating a comprehensive analytical review on the U control chart via the statistical measures. To this end, different levels of the possible factors were determined, and the results of calculating the statistical measures and the obtained parameters on the sampling schemes of the U control chart were presented. The results indicated that the variable U control charts were capable of improving the effectiveness of statistical measures, especially for detecting shifts and number of false alarms.

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Maintenance and quality control integrated strategy for manufacturing systems

Cited by 16 publications

References 0 publications

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Generalized periodic replacement policies for repairable systems subject to two types of failures

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

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