Optimization of maintenance policy using the proportional hazard model

Samrout, Mohamad; Châtelet, Éric; Kouta, Raed; Chebbo, Nazir

doi:10.1016/j.ress.2007.12.006

Cited by 58 publications

(24 citation statements)

References 15 publications

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“…However, today survival analysis has become a major tool for other fields of study for instance, in engineering reliability, networking and software reliability and survivability, machine learning, and prognostics and health management as stated in (Ma andKrings, 2008a, 2008b;Samrout et al, 2009) There are many examples of application of survival analysis approach in the medical field for example, which was done by (Ghazali et al, 2010). The purpose of this study is to identify the five years of survival rate and prognostics factors for survival in patients with colorectal cancer.…”

Section: The Application Of Survival Analysismentioning

confidence: 99%

“…Besides that, according to (Ma andKrings, 2008b, 2011) stated that other field of computer science and engineering also has great potential benefits by adopting survival analysis approach for example in network reliability and survivability as well as in prognostics and health management which merits further research. Recent evidence has reported that the application of Cox PH model in the reliability engineering field has increased (Samrout et al, 2009). Moreover, according to (Samrout et al, 2009) studies show that the Cox PH model is used as a modeling tool to integrate the effect of the corrective maintenance on the component's reliability through its relation on the component's age.…”

Section: The Application Of Survival Analysismentioning

confidence: 99%

“…Recent evidence has reported that the application of Cox PH model in the reliability engineering field has increased (Samrout et al, 2009). Moreover, according to (Samrout et al, 2009) studies show that the Cox PH model is used as a modeling tool to integrate the effect of the corrective maintenance on the component's reliability through its relation on the component's age. Finally, the findings show that the corrective maintenance affects on the failure rate and has an effect on the adopted preventive maintenance policy.…”

Section: The Application Of Survival Analysismentioning

confidence: 99%

See 2 more Smart Citations

Adopting and Adapting Medical Approach in Risk Management Process for Analysing Information Security Risk

Samy¹,

Ahmad²,

Ismail³

2012

Risk Management for the Future - Theory and Cases

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Section: The Application Of Survival Analysismentioning

confidence: 99%

Section: The Application Of Survival Analysismentioning

confidence: 99%

Section: The Application Of Survival Analysismentioning

confidence: 99%

See 1 more Smart Citation

Adopting and Adapting Medical Approach in Risk Management Process for Analysing Information Security Risk

Samy¹,

Ahmad²,

Ismail³

2012

Risk Management for the Future - Theory and Cases

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“…This model was first introduced by Cox (1972) and later on reviewed by Kumar and Klefsjö (1994). In maintenance this model is often used to estimate the influence of different covariates on the time to failure of a system or component (Samrout et al 2009). …”

Section: Modeling Deteriorationmentioning

confidence: 99%

Maintenance optimization models and criteria

Horenbeek

Pintelon

Muchiri

2010

Int J Syst Assur Eng Manag

115

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Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.

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“…There is another category called risk mostly modeled based on mathematical techniques for determining the optimal maintenance policy. For example, De Almeida and Bohoris (1995), Lugtigheid et al (2004), Samrout et al (2009), Martorell et al (1999, Gurler and Kaya (2002), Amari et al (2006) used mathematical models to illustrate an approach to determine the optimal maintenance policy for decision making. Machine replacement problem has been studied in many researches and researchers presented different approaches to determine an optimal maintenance policy.…”

Section: Introductionmentioning

confidence: 99%

An optimal maintenance policy for machine replacement problem using dynamic programming

Amalnik¹,

Pourgharibshahi²

2017

10.5267/j.msl

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In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by considering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.

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Optimization of maintenance policy using the proportional hazard model

Cited by 58 publications

References 15 publications

Adopting and Adapting Medical Approach in Risk Management Process for Analysing Information Security Risk

Adopting and Adapting Medical Approach in Risk Management Process for Analysing Information Security Risk

Maintenance optimization models and criteria

An optimal maintenance policy for machine replacement problem using dynamic programming

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