Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

Kenné, Jean‐Pierre; Nkeungoué, L. J.

doi:10.1016/j.apnum.2006.11.010

Cited by 70 publications

(51 citation statements)

References 13 publications

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“…The control policy obtained is an extension to the so-called hedging point policy described in Akella and Kumar (1986). The preventive and corrective maintenance policies divide the computational domain into two regions where the preventive maintenance rate is set to zero and to its maximal value for backlog situation (or for uncomfortable stock levels) and for large stock levels respectively (Kenné and Nkeungoue (2008)). The corrective maintenance rate is set to its maximal and minimal values for backlog situation and for large stock levels respectively (Boukas (1998)).…”

Section: Expected Resultsmentioning

confidence: 99%

Production, preventive and corrective maintenance planning in manufacturing systems under imperfect repairs

Kouedeu

Kenné

Songmené

2011

2011 3rd International Workshop on Dependable Control of Discrete Systems

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This paper deals with the joint analysis of the optimal production and maintenance planning problems for a manufacturing system subject to random failures and repairs. When a machine fails down, an imperfect repair is undertaken. The objective of this study is to minimize a discounted overall cost consisting of preventive and corrective maintenance costs, inventory holding cost and backlog cost. A two-level hierarchical decision making approach, based on the determination of the mean time between failures (first level) and the statement of a joint optimization of production, preventive and corrective maintenance policies (second level) is proposed. Hence the production, preventive and corrective maintenance rates are determined in level 2 given the failure rates obtained from level 1. In the proposed model, the failure rate of the machine depends on the number of failures; hence, the control policies of the considered planning problem depend on the number of failures. A numerical example and a sensitivity analysis will illustrate the structure of the optimal control policies and the usefulness on the proposed approach. Keywords: Imperfect repairs; Manufacturing Systems; Preventive and Corrective Maintenance. IntroductionThe reliability of a manufacturing system depends on the quality of its conception and the actions of the maintenance which are undertaken during its exploitation (production activities). This paper deals with the control problem of a stochastic manufacturing system consisting of one machine producing one part type. The stochastic nature of the system is due to the fact that the machine is subject to random breakdowns and repairs. Upon a failure of a component of the machine, an imperfect repair is undertaken. The machine dynamics is assumed herein to be described by a finite state semi-Markov chain. The decision variables are the production, the preventive and the corrective maintenance rates, which influence the availability of the system and the stock level. Many authors have contributed to the production planning problem of manufacturing systems (Boukas (1998)), without considering the failure rates depending on the number of failures and the production control in the same model. The aim of this paper is to propose a production control and maintenance (preventive and corrective) planning for a manufacturing system subject to imperfect repairs, when the failure rate increases with the number of failures. The proposed hierarchical approach consists in developing a model where at the first level; the parameters of the machine failure stochastic process are derived for each number of failures. At the second level, we determine the optimal production, preventive and corrective maintenance policies for a system that deteriorates with the number of failures. We obtain a

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Section: Expected Resultsmentioning

confidence: 99%

Production, preventive and corrective maintenance planning in manufacturing systems under imperfect repairs

Kouedeu

Kenné

Songmené

2011

2011 3rd International Workshop on Dependable Control of Discrete Systems

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“…In these models, the working life of the critical resource is often modeled as a random variable. Examples of this type of work are found in Boukas & Haurie (1990), Chelbi & Ait-Kadi (2004), Iravani & Duenyas (2002), Kenné & Nkeungoue (2008), Liberopoulos & Caramanis (1994) and Yao et al (2005), to name but a few.…”

Section: Review Of the Literaturementioning

confidence: 99%

Simultaneous production and maintenance planning for a single capacitated resource facing both a dynamic demand and intensive wear and tear

Wolter

Helber

2015

Cent Eur J Oper Res

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Suggested Citation: Wolter, Anja; Helber, Stefan (2013) : Simultaneous production and maintenance planning for a single capacitated resource facing both a dynamic demand and intensive wear and tear, Discussion AbstractThis paper presents a generic modeling framework to simultaneously decide about production quantities and maintenance operations for a capacitated resource facing a dynamic demand for different types of products. As the resource needs to be setup for each specific type of product, a lot-sizing problem occurs. In addition it is assumed that production causes intensive wear and tear. For this reason frequent maintenance activities need to be coordinated with the production operations in order to efficiently use the capacitated resource. A single generic model is presented to capture alternative forms of maintenance and different modes of interaction between maintenance and setups. As the model is numerically intractable for standard branch & bound algorithms, we solve it heuristically via a decomposition using a Fix-and-Optimize approach. Numerical results show that the proposed solution method produces high-quality results quickly. We further study the impact of simultaneous vs. sequential decisions about production and maintenance in the case of intensive wear and tear.

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“…The expression given by equation (5) describes the impact of a machine age on its dynamics, as in Kenné and Nkeungoue. 19 The inverse of the transition rate 12 ( ) represents the mean time to failure as a function of the age denoted by ( ). We use the notation Ind{Θ(. )}…”

Section: Problem Formulationmentioning

confidence: 99%

“…The value function (. ) defined in equation (18) is the unique viscosity solution to the HJB equations (19). Proof.…”

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Age-dependent production and replacement strategies in failure-prone manufacturing systems

Ouaret

Kenné

Gharbi

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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IntroductionIn the manufacturing environment, the availability of the machine often decreases due to its age and also to imperfect maintenance activities. In general, corrective or preventive maintenance brings the state of the machine to a level which is not new, and it may not be able to meet the demand rate for the commodity produced. For this reason, the machine has to be replaced. We consider a machine that is subject to random breakdowns and repairs. It undergoes deterioration while in operation, and the failure rate increases with its age. The aging of the machine is an increasing function of its production rate. The corrective maintenance activities performed are imperfect and restore the machine to as-bad-as-old conditions. Replacement activities for their part renew the machine, which is similar to restoring the machine to as-good-as-new conditions (resetting its age to zero).The first objective of this paper is to simultaneously determine production and replacement policies in a manufacturing environment under deterioration and imperfect repairs. We enhance existing mathematical models by including the production cost in the objective function. Given that the dynamics of the machine aging process depends on the production rate, penalizing the latter helps to amplify the impact of aging and push the system (optimal control policies) towards an appropriate solution. The proposed model appears to be better at addressing industrial reality, and is yet to be used in the literature in analyzing age-dependent production and replacement strategies. The obtained solution provides the simultaneous optimal control of production and replacement of the machine (assuming that one replacement is performed). The decision variables are the production rate and the replacement policy. The dynamics of the machine is described by a semi-Markov decision model due to the machine's deterioration and imperfect repairs (as-bad-as-old). The optimal control policies are determined in order to satisfy a deterministic customer demand and minimize inventory, backlog, production, repair and replacement costs over an infinite planning horizon. AbstractA failure-prone manufacturing system that consists of one machine producing one type of product is studied. The random phenomena examined are machine breakdowns and repairs. We assume that the machine undergoes a progressive deterioration while in operation and that the machine failure rate is a function of its age. The aging of the machine (the dynamics of the machine age) is assumed to be an increasing function of its production rate. Corrective maintenance activities are imperfect and restore the age of the machine to as-bad-as-old conditions (ABAO). When a failure occurs, the machine can be repaired, and during production, the machine can be replaced, depending on its age. When the replacement action is selected, the machine is replaced by a new and identical one. The decision variables are the production rate and the replacement policy. The objective of this paper is to address th...

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Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

Cited by 70 publications

References 13 publications

Production, preventive and corrective maintenance planning in manufacturing systems under imperfect repairs

Production, preventive and corrective maintenance planning in manufacturing systems under imperfect repairs

Simultaneous production and maintenance planning for a single capacitated resource facing both a dynamic demand and intensive wear and tear

Age-dependent production and replacement strategies in failure-prone manufacturing systems

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