Joint optimization of block-replacement and periodic-review spare-provisioning policy

Brezavšček, Alenka; Hudoklin, Alenka

doi:10.1109/tr.2002.805790

Cited by 77 publications

(31 citation statements)

References 10 publications

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Order By: Relevance

“…Working period (1) T is always greater than the order delivery time L , and T is a fixed value. The downtime loss is generated by equipment maintenance or (2) lack of spare parts, and it is proportional to the downtime.…”

Section: General Assumptionsmentioning

confidence: 99%

“…Sheu et al [15] studied the age replacement policy like document [14] for single equipment system, which includes impact deterioration system. Alenka [1] studied the joint optimization problem of periodic batch replacement and periodic spare procurement. These studies have only considered the spare parts ordering strategy under planned maintenance system and based on spare parts inventory levels.…”

Section: Introductionmentioning

confidence: 99%

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Integrated decision on spare parts ordering and equipment maintenance under condition based maintenance strategy

Wang¹,

Zhao²,

Cheng³

et al. 2015

EiN

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Integrated decIsIon on spare parts orderIng and equIpment maIntenance under condItIon based maIntenance strategy ZIntegrowany system decyZyjny dotycZący ZamawIanIa cZęścI ZamIennych I utrZymanIa ruchu urZądZeń w ramach strategII utrZymanIa Zależnej od bIeżącego stanu technIcZnego WAng Y, ZhAo J, Cheng Z, YAng Z. Integrated decision on spare parts ordering and equipment maintenance under condition based maintenance strategy. eksploatacja i niezawodnosc -Maintenance and Reliability 2015; 17 (4): 591-599, http://dx

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Section: General Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated decision on spare parts ordering and equipment maintenance under condition based maintenance strategy

Wang¹,

Zhao²,

Cheng³

et al. 2015

EiN

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show abstract

“…As in the paper Brezavšček and Hudoklin (2003), the arcing chamber which is an important locomotive component is studied. There are n = 120 components under observation.…”

Section: Numerical Examplementioning

confidence: 99%

“…Such mathematical models are usually quite complex, containing a great number of parameters (see e.g. Brezavšček and Hudoklin, 2003;Diallo et al, 2008;Hu et al, 2008;Huang et al, 2008;de SmidtDestombes et al, 2009;Wang et al, 2009), and are often difficult to implement. From a practical point of view, simpler methods for defining an efficient spare provisioning policy would be most desirable.…”

Section: Introductionmentioning

confidence: 99%

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Simple Stochastic Model for Planning the Inventory of Spare Components Subject to Wear-out

Brezavšček

2011

Organizacija

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Simple Stochastic Model for Planning the Inventory of Spare Components Subject to Wear-out We treat an industrial system which comprises of a number of identical components subject to wear-out. To support the system maintenance an appropriate inventory of spare components is needed. In order to plan the sufficient inventory of spare components, two variants of a simple stochastic model are developed. In both variants, the aim is to determine how many spare components are needed at the beginning of a planning interval to meet demand for corrective replacements during this interval. Under the first variant the acceptable probability of spare shortage during the planning interval is chosen as a decision variable. While in the second variant the adequate spare inventory level is assessed by taking into account the expected number of component failures within the planning interval. A comparison of both variants of the model shows that calculations involved in the second variant are simpler. However, it can only be used when the inventory of spare components can be planned for a relatively long period of time. The determination of an adequate number of spare components according to both variants of our model depends on the form of the probability density function of component failure times. Since the components are subject to wear-out, this function exhibits a peak-shaped form that can be described by different statistical density functions. Advantages and disadvantages of using the normal, lognormal, Weibull, and Gamma density function in our model are discussed. Among the probability density functions studied, the normal density function is found to be the most appropriate for calculations in our model. The applicability of both variants of the model is given through numerical examples using field data on electric locomotives of Slovenian Railways.

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Replacement Strategies

Popova¹,

Popova²

2007

Encyclopedia of Statistics in Quality and Reliability

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Joint optimization of block-replacement and periodic-review spare-provisioning policy

Cited by 77 publications

References 10 publications

Integrated decision on spare parts ordering and equipment maintenance under condition based maintenance strategy

Integrated decision on spare parts ordering and equipment maintenance under condition based maintenance strategy

Simple Stochastic Model for Planning the Inventory of Spare Components Subject to Wear-out

Replacement Strategies

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