Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate

Hajej, Zied; Dellagi, Sofiène; Rezg, Nidhal

doi:10.1080/00207543.2010.528063

Cited by 83 publications

(21 citation statements)

References 16 publications

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“…More then, in several studies the authors developed an analytical model representing an objective function related to economical or technical profit. The authors optimize analytically or by simulation methods theses developed functions in order to obtain an optimal variable decisions helpful for industrial (Schutz et al [16], Hajej et al [17]). …”

Section: Possible Extensionsmentioning

confidence: 99%

TPM implantation in manufacturing systems: A survey of the state-of-the-art and extensions

2017

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Abstract. Maintenance activities represent a significant pillar in industry. Its real goal consists at improving the production systems productivity. Specially, TPM represents one of the main evolutions of the maintenance methods in order to improve the machines efficiency by a proactive approach. This paper presents an overview of several TPM implantations and especially efficiency and effectiveness measurement of the TPM in several industries. In a second hand, a proposition of possible extension about analytical assessment of the TPM performance, based on both technical and economic criteria, is presented.

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Section: Possible Extensionsmentioning

confidence: 99%

TPM implantation in manufacturing systems: A survey of the state-of-the-art and extensions

2017

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“…For example, Najid, Alaoui-Selsouli, and Mohafid (2011) use mixed-integer linear programming to find the optimal maintenance and production policies when demand shortages are costly. Zied, Sofiene, and Nidhal (2011) first find the optimal production policy taking into account manufacturing and inventory costs; then, they derive an optimal maintenance policy based on the initial optimal production policy. Xiang et al (2014) incorporate random yields into the production and maintenance decisions.…”

Section: Introductionmentioning

confidence: 99%

Two types of learning effects on maintenance activities

Tarakci

2015

International Journal of Production Research

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This paper studies a manufacturer with a system prone to failure. The manufacturer performs two types of maintenance activities: preventive maintenance (PM), performed periodically, resets the system, and Minimal Repair (MR), performed after breakdowns, restores the system to working condition. It is assumed that two different types of learning take place: (i) repetition learning: due to the repetitive nature of PM, the manufacturer gains experience and learns to perform the PM activities faster and at a lower cost and (ii) failure learning: each failure gives the manufacturer the opportunity to find the root causes, to learn from mistakes and to improve the system. This paper, the first one to quantify failure learning in maintenance literature, assumes that such learning can then be applied during the next PM activity, which brings down the failure rate for the next PM cycle. For the increasing failure rate case, repetition learning increases the PM frequency, whereas failure learning causes the manufacturer to reduce the optimal number of PM activities. However, for the constant failure rate, repetition learning has no effect on the PM frequency, whereas failure learning may actually increase it.

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“…Gharbi et al (2009) presented an approach for simultaneously optimizing maintenance cost and availability of failure-prone manufacturing systems. Zied et al (2011) proposed a model to obtain combined maintenance and production policy for randomly failing production systems. A few researchers have evaluated optimal maintenance policies for manufacturing systems based on Markov and semi-Markov models.…”

Section: Introductionmentioning

confidence: 99%

Maintenance cost minimization of manufacturing systems using PSO under reliability constraint

Loganathan

Gandhi

2015

Int J Syst Assur Eng Manag

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Profit is a main objective of production units, and minimization of its maintenance cost will help to achieve it. But with no compromise with plant machinery reliability constraints. Manufacturing systems do require maintenance, but deferring it due to production constraints may increase its requirements, resulting in the increased cost. The experience shows that carrying out of the preventive maintenance (PM) helps in avoidance of catastrophic failures, but this needs to be optimized. An optimal PM interval will minimize maintenance cost, while maintaining their lower bound reliability. Most existing optimization models do not address this issue especially in the context of real life industries. Particle swarm optimization is an effective meta-heuristic technique and is extensively applied to find optimal solution for various engineering problems. In this paper, this is proposed for maintenance cost minimization of manufacturing systems under reliability constraint. An example is illustrated to demonstrate it.

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Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate

Cited by 83 publications

References 16 publications

TPM implantation in manufacturing systems: A survey of the state-of-the-art and extensions

TPM implantation in manufacturing systems: A survey of the state-of-the-art and extensions

Two types of learning effects on maintenance activities

Maintenance cost minimization of manufacturing systems using PSO under reliability constraint

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