New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization

Samrout, Mohamad; Yalaoui, Farouk; Châtelet, Éric; Chebbo, Nazir

doi:10.1016/j.ress.2004.09.005

Cited by 104 publications

(58 citation statements)

References 20 publications

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“…Others have used genetic algorithms [17], evolutionary algorithms [15] and neural nets [6] for same purposes. Few researchers have also proposed reliability centred maintenance (RCM) using FMEA and proposing actions on the basis of RPN number [13].…”

Section: Auto Components Manufacturing and Maintenance Environmentmentioning

confidence: 99%

Prioritization of Tools in Joint Production–Maintenance Environment of Auto Component Manufacturer Using AHP–Fuzzy–TOPSIS

2016

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Maintenance of tools is one of the areas of maintenance which has been less explored. While maintenance of machinery has a number of research works to its cause, the same cannot be said for tools. In this paper we have identified the unique maintenance needs of the tools and the challenges it present in case of joint production-maintenance environment especially with regard to efficient allocation of resources. Various methods of maintenance planning are reviewed and their suitability is studied. Due to the resources constraint, prioritization of tools for maintenance becomes important. In this work, we have considered the environment of an auto component manufacturer, where there are a number of tools which needs to be serviced on the basis of time and counter. The approach proposed for prioritization is AHP-Fuzzy-TOPSIS as it provides the best combination of quantitative as well as qualitative parameters. Through the use of this methodology we have presented a final ranking which can be used by management for deciding on the optimum allocation of resources.

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Section: Auto Components Manufacturing and Maintenance Environmentmentioning

confidence: 99%

Prioritization of Tools in Joint Production–Maintenance Environment of Auto Component Manufacturer Using AHP–Fuzzy–TOPSIS

2016

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“…Lim and Chang (2000) studied a repairable system modeled by a Markov chain with two repair modes. Samrout et al (2005) describes the availability and reliability as good evaluations of a system's performance. Barabady and Kumar (2007) states that the most important performance measures for repairable system designers and operators are system reliability and availability.…”

Section: Introductionmentioning

confidence: 99%

Markov approach to evaluate the availability simulation model for power generation system in a thermal power plant

Kumar¹,

Sharma²,

Tewari³

2012

IJIEC

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In recent years, the availability of power plants has become increasingly important issue in most developed and developing countries. This paper aims to propose a methodology based on Markov approach to evaluate the availability simulation model for power generation system (Turbine) in a thermal power plant under realistic working environment. The effects of occurrence of failure/course of actions and availability of repair facilities on system performance have been investigated. Higher availability of the components/equipments is inherently associated with their higher reliability and maintainability. The power generation system consists of five subsystems with four possible states: full working, reduced capacity, reduced efficiency and failed state. So, its availability should be carefully evaluated in order to foresee the performance of the power plant. The availability simulation model (Av.) has been developed with the help of mathematical formulation based on Markov Birth-Death process using probabilistic approach. For this purpose, first differential equations have been generated. These equations are then solved using normalizing condition so as to determine the steady state availability of power generation system. In fact, availability analysis is very much effective in finding critical subsystems and deciding their preventive maintenance program for improving availability of the power plant as well as the power supply. From the graphs illustrated, the optimum values of failure/repair rates for maximum availability, of each subsystem is analyzed and then maintenance priorities are decided for all subsystems.The present paper highlights that in this system, Turbine governing subsystem is most sensitive demands more improvement in maintainability as compared to the other subsystems. While Turbine lubrication subsystem is least sensitive.

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“…In such situations, obtaining the exact global optimum using analytical approaches via mathematical inference is impractical. Therefore, the metaheuristic algorithms, such as a genetic algorithm (GA) (Bris et al 2003;Marseguerra and Zio 2000;Jia et al 2003;Zimin Yang et al 2008), colony algorithm (Samrout et al 2005(Samrout et al , 2007 and simulated annealing (Leou 2006), are widely employed to optimize these models and approach the global optimum. Of these heuristic algorithms, GAs are also widely used to efficiently solving problems associated with reliability structural design and redundancy allocations (Gen and Cheng 1997;Hsieh et al 1998;Tavakkoli-Moghaddam et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system

Lin

Wang

2010

J Intell Manuf

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This study presents a hybrid genetic algorithm (GA) to optimize the periodic preventive maintenance model in a series-parallel system. The intrinsic properties of a repairable system, including the structure of reliability block diagrams, maintenance priority of components, and their maintenance periods, are considered in developing the proposed hybrid GA. The importance measure of components is employed to account for these properties, identify important components, and determine their maintenance priorities. The optimal maintenance periods of these important components are then determined to minimize total maintenance cost given the allowable worst reliability of a repairable system using the GA search mechanism. An elitist conservation strategy is applied to retain superior chromosomes in the iterative breeding process to accelerate the approach toward the global optimum. Furthermore, the response surface methodology is utilized to systematically determine crossover probability and mutation probability in the GA instead of using the conventional trial-and-error process. A case study demonstrates the effectiveness and practicality of the proposed hybrid GA in optimizing the periodic preventive maintenance model in a series-parallel system.

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New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization

Cited by 104 publications

References 20 publications

Prioritization of Tools in Joint Production–Maintenance Environment of Auto Component Manufacturer Using AHP–Fuzzy–TOPSIS

Prioritization of Tools in Joint Production–Maintenance Environment of Auto Component Manufacturer Using AHP–Fuzzy–TOPSIS

Markov approach to evaluate the availability simulation model for power generation system in a thermal power plant

A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system

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