Optimal just-in-time buffer inventory for regular preventive maintenance

Salameh, Moueen K.; Ghattas, R.E.

doi:10.1016/s0925-5273(01)00122-0

Cited by 60 publications

(33 citation statements)

References 5 publications

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“…An analytical model of BRP and safety stock strategy is formulated by Ki-Ling and Warren (1997), using also restrictive assumptions such as: the time to accomplish build-up and depletion of safety stock is small relative to the mean time to failures (MTTF). The model presented in Salameh and Ghattas (2001) combines ARP and safety stock to show that one need to built an inventory just before the preventive maintenance. It is assumed in Salameh and Ghattas (2001) that extra capacity is maintained to buffer against uncertainties of the production processes and that there is no possible breakdown of the machine before the preventive maintenance date.…”

Section: Introductionmentioning

confidence: 99%

“…The model presented in Salameh and Ghattas (2001) combines ARP and safety stock to show that one need to built an inventory just before the preventive maintenance. It is assumed in Salameh and Ghattas (2001) that extra capacity is maintained to buffer against uncertainties of the production processes and that there is no possible breakdown of the machine before the preventive maintenance date. Without the assumption made by Salameh and Ghattas (2001) on the machine dynamics, the stochastic optimal control theory is used in Boukas et al (1995), Kenne (2000, 2005), Kenne and Gharbi (1999) and in Kenne and Boukas (2003) to define an machine age dependent production and preventive maintenance policies.…”

Section: Introductionmentioning

confidence: 99%

“…It is assumed in Salameh and Ghattas (2001) that extra capacity is maintained to buffer against uncertainties of the production processes and that there is no possible breakdown of the machine before the preventive maintenance date. Without the assumption made by Salameh and Ghattas (2001) on the machine dynamics, the stochastic optimal control theory is used in Boukas et al (1995), Kenne (2000, 2005), Kenne and Gharbi (1999) and in Kenne and Boukas (2003) to define an machine age dependent production and preventive maintenance policies. Such policies are based on non homogeneous Markov models, and hence are restricted to exponential distributions describing operational and down times of the involved machines.…”

Section: Introductionmentioning

confidence: 99%

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Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems

Gharbi

Kenné

Beit

2007

International Journal of Production Economics

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We consider a manufacturing system with preventive maintenance that produces a single part type. An inventory is maintained according to a machine age dependent hedging point policy. We conjecture that, for such a system, the failure frequencies can be reduced through preventive maintenance resulting in possible increase in system performance. Traditional preventive maintenance policies, such as age replacement, periodic replacement, are usually studied without finished goods inventories. In the cases where the finished goods inventories are considered, restrictive assumptions are used, such as not allowing breakdown during the stock build up period and during backlog situations due to the complexity of the mathematical model. In order to solve this problem, we develop a more realistic mathematical model of the system, and derive expressions of the overall incurred cost used as the basis for optimal determination of the jointly production and preventive maintenance policies (i.e. production rates and preventive maintenance frequency, depending on inventory levels of the produced parts). Such a cost consists of inventory, backlog, corrective and preventive maintenance costs. The work reported here has a significant practical application (no restriction on failures occurrence and backlog situations) in the context of production planning of manufacturing systems. Numerical examples are included to illustrate the importance and the effectiveness of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems

Gharbi

Kenné

Beit

2007

International Journal of Production Economics

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“…Most of the manufacturing organizations try to implement some of the ideas adopted by Just in Time (JIT) philosophy, like on time delivery or minimum inventory (Salameh and Ghattas 2001). In this paper we introduce and formulate a JIT single machine scheduling problem with a periodic preventive maintenance on the machine.…”

Section: Introductionmentioning

confidence: 99%

JIT single machine scheduling problem with periodic preventive maintenance

et al. 2016

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This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.

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“…These mathematical models combine BRP or ARP with production/inventory control policies to respond to demand during maintenance activities (Ki Ling and Hausman (1997), Salameh and Ghattas (2001), Tadashi et al (2001), Chelbi and Ait-Kadi (2004), Kenné et al (2007), Gharbi et al (2007), , Rezg et al (2008), Berthaut et al (2011)). …”

Section: Introductionmentioning

confidence: 99%

Joint optimal production control/preventive maintenance policy for imperfect process manufacturing cell

Dhouib

Gharbi

Aziza

2012

International Journal of Production Economics

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Production, maintenance, and quality are often modeled as separate problems, despite the strong link that exists between these primary components of any manufacturing system. This paper deals with an integrated approach for the joint optimization of production-inventory control and preventive maintenance policy for a manufacturing cell comprising an imperfect process where the 'in-control' period have a general deterioration distribution. The production-inventory control policy based on the hedging point policy consists of building and maintaining a security stock of finished products in order to respond to demand and to avoid shortages during restoration actions. Restoration actions are planned when the system switches to the 'out-of-control' state and starts producing non-conforming items. The manufacturing cell is also subject to an age-based preventive maintenance policy in order to reduce the shift rate to the 'out-of-control' state. The main objective of this paper is to determine the joint optimal policy that minimizes the overall cost, which is comprised of setup, maintenance, inventory holding and shortage costs, as well as the cost incurred by producing non-conforming items. A mathematical model is proposed, and the expression of the overall incurred cost is derived and used as a basis for the optimal determination of the joint production-inventory control and preventive maintenance policy. These issues are illustrated using numerical examples. It is found that performing preventive maintenance will yield reductions in the overall incurred cost. Sensitivity analyses are also carried out in order to illustrate the robustness of the proposed methodology.

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Optimal just-in-time buffer inventory for regular preventive maintenance

Cited by 60 publications

References 5 publications

Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems

Optimal safety stocks and preventive maintenance periods in unreliable manufacturing systems

JIT single machine scheduling problem with periodic preventive maintenance

Joint optimal production control/preventive maintenance policy for imperfect process manufacturing cell

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