Journal of Quality in Maintenance Engineering Model for economic replacement time of mining production rigs including redundant rig costs Hussan Saed Al-Chalabi Jan Lundberg Majid Al-Gburi Alireza Ahmadi Behzad Ghodrati Article information: To cite this document: Hussan Saed Al-Chalabi Jan Lundberg Majid Al-Gburi Alireza Ahmadi Behzad Ghodrati , (2015),"Model for economic replacement time of mining production rigs including redundant rig costs", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 207 - 226 Permanent link to this document: http://dx.doi.org/10.1108/JQME-07-2014-0041 Downloaded on: 07 May 2015, At: 03:06 (PT) References: this document contains references to 35 other documents. To copy this document: [email protected]The fulltext of this document has been downloaded 20 times since 2015* Users who downloaded this article also downloaded: Abdelhakim Abdelhadi, Layth C. Alwan, Xiaohang Yue, (2015),"Managing storeroom operations using cluster-based preventative maintenance", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 154-170 http://dx.doi.org/10.1108/JQME-10-2013-0066 Samir Khan, (2015),"Research study from industry-university collaboration on “No Fault Found” events", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 186-206 http:// dx.doi.org/10.1108/JQME-01-2014-0004 Kanwarpreet Singh, Inderpreet Singh Ahuja, (2015),"An evaluation of transfusion of TQM-TPM implementation initiative in an Indian manufacturing industry", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 134-153 http://dx.doi.org/10.1108/JQME-04-2013-0017 Access to this document was granted through an Emerald subscription provided by 172900 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Downloaded by LULEA UNIVERSITY OF TECHNOLOGY At 03:06 07 May 2015 (PT)
Transcript
Journal of Quality in Maintenance EngineeringModel for economic replacement time of mining production rigs includingredundant rig costsHussan Saed Al-Chalabi Jan Lundberg Majid Al-Gburi Alireza Ahmadi Behzad Ghodrati
Article information:To cite this document:Hussan Saed Al-Chalabi Jan Lundberg Majid Al-Gburi Alireza Ahmadi Behzad Ghodrati ,(2015),"Model for economic replacement time of mining production rigs including redundant rig costs",Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 207 - 226Permanent link to this document:http://dx.doi.org/10.1108/JQME-07-2014-0041
Downloaded on: 07 May 2015, At: 03:06 (PT)References: this document contains references to 35 other documents.To copy this document: [email protected] fulltext of this document has been downloaded 20 times since 2015*
Users who downloaded this article also downloaded:Abdelhakim Abdelhadi, Layth C. Alwan, Xiaohang Yue, (2015),"Managing storeroom operations usingcluster-based preventative maintenance", Journal of Quality in Maintenance Engineering, Vol. 21 Iss2 pp. 154-170 http://dx.doi.org/10.1108/JQME-10-2013-0066Samir Khan, (2015),"Research study from industry-university collaboration on “No Fault Found”events", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 2 pp. 186-206 http://dx.doi.org/10.1108/JQME-01-2014-0004Kanwarpreet Singh, Inderpreet Singh Ahuja, (2015),"An evaluation of transfusion of TQM-TPMimplementation initiative in an Indian manufacturing industry", Journal of Quality in MaintenanceEngineering, Vol. 21 Iss 2 pp. 134-153 http://dx.doi.org/10.1108/JQME-04-2013-0017
Access to this document was granted through an Emerald subscription provided by 172900 []
For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emeraldfor Authors service information about how to choose which publication to write for and submissionguidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The companymanages a portfolio of more than 290 journals and over 2,350 books and book series volumes, aswell as providing an extensive range of online products and additional customer resources andservices.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of theCommittee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative fordigital archive preservation.
*Related content and download information correct at time ofdownload.
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
Model for economic replacementtime of mining production rigsincluding redundant rig costsHussan Saed Al-Chalabi, Jan Lundberg, Majid Al-Gburi,
Alireza Ahmadi and Behzad GhodratiDivision of Operation, Maintenance and Acoustics,Luleå University of Technology, Luleå, Sweden
AbstractPurpose – The purpose of this paper is to present a practical model to determine the economicreplacement time (ERT) of production machines. The objective is to minimise the total cost of capitalequipment, where total cost includes acquisition, operating, maintenance costs and costs related to themachine’s downtime. The costs related to the machine’s downtime are represented by the costs of usinga redundant machine.Design/methodology/approach – In total, four years of cost data are collected. Data are analysed,practical optimisation model is developed and regression analysis is done to estimate the drilling rigsERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencingthe ERT of the drilling rigs.Findings – The results show that the redundant rig cost has the largest impact on ERT, followed byacquisition, maintenance and operating costs. The study also finds that increasing redundant costsper hour have a negative effect on ERT, while decreases in other costs have a positive effect.Regression analysis shows a linear relationship between the cost factors and ERT.Practical implications – The proposed approach can be used by the decision maker in determiningthe ERT of production machines which used in mining industry.Originality/value – The research proposed in this paper provides and develops an optimisationmodel for ERT of mining machines. This research also identifies and explains the factors that havethe largest impact on the production machine’s ERT. This model for estimating the ERT has neverbeen studied on mining drilling rigs.Keywords Decision support model, Life-cycle cost, Optimisation, Replacement timePaper type Research paper
NomenclatureERT Economical replacement time
(month)LCCi Labour cost for corrective
maintenance (cu)ANN Artificial neural network
SPVi Spare part value (cu)IAC Increasing acquisition cost (%)SPLi Spare part logistic cost (cu)DOC Decreasing operating cost (%)rt Repair time (h)
Received 5 July 2014Revised 9 November 2014Accepted 2 March 2015
The current issue and full text archive of this journal is available on Emerald Insight at:www.emeraldinsight.com/1355-2511.htm
The authors would like to thank Atlas Copco and Boliden mineral AB, for supporting thisresearch. Special appreciation is extended to the experts at Boliden mineral AB and Atlas Copcofor sharing their valuable knowledge and experience. The authors would like also to thank ArneVesterberg at Boliden mineral AB and Andreas Nordbrandt at Atlas Copco for them supports.The authors would like also to thank for the support of CAMM (Centre of Advanced Mining &Metallurgy) project in this research work. The authors’ sincerest gratitude is extended to thereviewers and the editor of this journal for the valuable comments that the authors’ received fromthem, which helped to improve this paper.
207
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
DMC Decreasing maintenance cost (%)nl Number of laboursDRC Decreasing redundant rig cost (%)cl Man hour cost (cu/h)RT Replacement time (month)SPPi Spare part cost for preventive
maintenance (cu)ERTs Scaled economical replacement timeLCPi Labour cost for preventive
maintenance (cu)TC Total cost (cu)RCi Redundant rig cost (cu)cu Currency unitPTi Using time of redundant rig (h)AC Acquisition cost (cu)CRi Redundant rig cost per hour (cu/h)i Time period (month)TRi Logistic time for redundant rig (h)MCi Maintenance cost (cu)TFi Restoring time of faulty rig
to operation (h)OCi Operating cost (cu)T1i Moving time of redundant rig from
its location to production point (h)
COi Compensation cost (cu)T2i Moving time of redundant rig from
production point to its originallocation (h)
Si Resale value (cu)TMi Moving time of faulty rig from
production point to workshop (h)r Discount rate (%)TWi Time in workshop of faulty rig (h)N Number of replacement cyclesTLi Moving time of repaired rig from
workshop to production point (h)CMi Corrective maintenance cost (cu)tdi Delay time in workshop of faulty rig
before repair (h)PMi Preventive maintenance cost (cu)tri Actual repair time of faulty rig (h)SPCi Spare part cost for corrective
maintenance (cu)tIi Idle time in workshop of faulty rig
after repair (h)
1. IntroductionIndustrial companies, or more specifically, mining companies put huge funds, oftenmillions of dollars into their annual budgets to purchase heavy mobile equipment(HME) such as drilling rigs, scaling rigs, wheel dozers, wheel loaders, dump trucks, etc.Given the enormous costs of acquiring, operating and maintaining their HME, it isimportant for companies to optimise their replacement and procurement strategies(Richardson et al., 2013). As the HME operating hour’s rise, so too do the maintenanceand operating costs. At some point in the equipment’s life span, these costs will be toohigh; it will no longer be economically viable to continue using the old equipment, so itshould be replaced (Verheyen, 1979). An essential economic consideration in industrialcompanies is to find a model that can discriminate this point (i.e. the point at whichthe equipment replacement time is expected to yield minimal life-cycle cost (LCC)).Obviously, for mining companies, one of the most important decisions is determiningthe economic replacement time (ERT) of capital equipment; this can be done with thehelp of LCC analysis. The main reason for the increasing use of the life cycle costingconcept for HME is that at some point the operating and maintenance (O&M) costs willexceed their acquisition costs.
In general, LCC is determined by summing up all potential costs associated withequipment over its life time (i.e. the total of ownership and acquisition costs). It is wellknown that the value of expenditure today costs more than the same expenditure nextyear because of the decreasing “time value of money”. In this study we use a discountrate to account for the time value of money. To compare costs incurred at different
208
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
times, we must shift expenditure to a reference point in time. Thus, we calculate thepresent equivalent value of the costs by considering the discount rate factor.
1.1 Literature reviewStandard models for ERT decisions contain an estimation of the discounted costs byminimising the total cost of the equipment. The assumption of these models is thatequipment will be replaced at the end of its economic lifetime by a continuous sequenceof identical equipment (Hartman and Tan, 2014). Bellman (1955) developed the firstoptimal asset replacement model for variable lifetime of assets. Wagner (1975) offereddynamic programming formulation for the equipment replacement problem in whichthe state of the system is the time period and the decision at each period is to keep theequipment for N periods. His formulation has been extended by researchers to dealwith realities of technological changes (e.g. see (Oakford et al., 1984; Bean et al., 1985;Hartman and Rogers, 2006; Hritonenko and Yatsenko, 2008)). These authors assumeda finite horizon in their approaches to the problem of equipment replacement undernon-stationary costs. Elton and Gruber (1976) showed that an equal life policy wasoptimal on an infinite horizon under technological changes. In contrast, Hartmanand Murphy (2006) studied an asset replacement problem for a stationary finitehorizon; they illustrated how a bound on the number of times an asset is retained at itseconomic life can be obtained, thus suggesting it is optimal to replace the asset atits economic lifetime.
Dynamic programming models have been utilised in real cases of calculatingequipment replacement time because of the important uncertainties associated withLCC (Richardson et al., 2013). The net present value of all LCC associated with aninfinite sequence of equipment life cycles has also been used to make equipmentreplacement decisions (Bethuyne, 1998; Scarf and Bouamra, 1999; Hartman, 2005;Yatsenko and Hritonenko, 2005). Other researchers have used different equipmentreplacement models to analyse a variety of equipment, such as forklifts, buses andaircraft (Eilon et al., 1966; Keles and Hartman, 2004; Bazargan and Hartman, 2012).Although Tanchoco and Leung (1987) found replacement decisions could be influencedby capacity considerations, others have noted that technological changes can encouragedecision makers to utilise equipment beyond its economic life (Cheevaprawatdomrongand Smith, 2003). Still other researchers have considered reliability, maintainabilityand optimum replacement decisions; readers are referred to, e.g., Wijaya et al. (2012);Dandotiya (2012), Golmakani and Pouresmaeeli (2014) and Al-Chalabi et al. (2014a,b) forfurther discussion of the recent literature.
1.2 Aim of the studyBlanchard et al. (1995) mentioned that the costs associated with equipment support,operation and maintenance can account for more than 75 per cent of the equipmentLCC. Thus, careful consideration must be given to estimating the capital equipmentownership costs. Given the importance of O&M costs and in order to assure a specificlevel of availability, industries must consider using redundant equipment to overcomeproduction loss when a failure occurs. Another way to ensure production performanceis to make a pooling agreement with other companies, renting commonly ownedequipment to ensure that failed equipment will be replaced by serviceable machines.But any of these compensation strategies cost money for the operator. In theassessment of equipment replacement time, the compensation cost (i.e. redundant rig
209
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
cost in this case study) associated with a machine should be taken into account.Thus, the aim of this paper is to develop a model to determine the ERT of productionequipment, in this case, a drilling rig, considering redundant rig cost. In this paper, wealso consider the relative importance of other cost factors on the rig’s ERT; thesefactors are the equipment acquisition, operating, maintenance and redundant rig costs.Finally, in the model, we consider the time value of money by using a discount rate.
2. Case studyIn this study, our model of ERT for production machines is implemented in a casestudy of a drilling rig used in mining industry. Our case study was selected from themining sector, as maintenance costs in this sector account for 20-30 per cent of the totalcost of production (Kumar, 1994). Kumar (1994) also maintained that to achieve optimalperformance from the capital intensive mining equipment and systems, mine operatorsmust ensure world-class maintenance in line with other advanced industries. Thedrilling rig is selected as a case study for several reasons: drilling is the first step in atypical mining cycle and thus is extremely important (see Figure 1); the drilling rigs areheavily loaded; the rigs’ acquisition and maintenance costs are high; finally, drillingrepresents a critical bottleneck for production.
3. Model formulationThe ERT of capital equipment is the age that minimises its total cost. In this study, thetotal cost is represented by acquisition (initial or investment) cost and ownership cost.The ownership cost includes O&M costs and compensation cost. All repairable systemswear over time; consequently, the ownership cost increases and the resale valuedecreases.
In this study, the ERT is defined as the value of the replacement time (RT ) whichminimises the total discounted cost, calculated on a monthly basis as follows:
MinTC ¼ Min ACþXRTi¼1
MCiþOCiþCOið Þ" #
�Si
!� 1
1þrð Þ i12
( )� N
" #(1)
The objective of the proposed model (i.e. Equation (1)) is to determine the ERT whichminimises the total discounted cost over the rig’s planned lifetime. We assume the
1. Drilling
2. Charging
3. Blasting
4. Loading
5. Scaling
6. Bolting
Figure 1.Typical undergroundmining cycle
210
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
replacement rig (i.e. the new rig) has the same performance and cost as the existing rig(i.e. identical rigs). The number of replacement cycles during the planned lifetime isrepresented as:
N ¼ TRT
(2)
where T and RT represent the planned lifetime and the replacement time (in months),respectively.
3.1 Maintenance costMaintenance can be defined as any work used to keep something in an appropriatecondition. Maintenance can corrective or preventive. Corrective maintenance refersto actions which take place after an unscheduled breakdown to return an item toa specified condition. Preventive maintenance refers to regularly scheduled actionsplanned to keep an item in the desired condition. The maintenance cost can be labelledas a summation of the materials and labour expense required to keep an item in suitableworking condition. In this study, due to the company’s regulations, all costs data areencoded and expressed as currency unit (cu). The maintenance cost is represented asfollows:
MCi ¼ CMiþPMi (3)
where CMi and PMi represent corrective and preventive maintenance cost (cu),respectively:
CMi ¼ SPCiþLCCi (4)
where SPCi and LCCi represent spare part and labour costs for corrective maintenance(cu), respectively:
SPCi ¼ SPViþSPLi (5)
where SPVi and SPLi are spare part value and spare part logistic costs (cu), respectively:
LCCi ¼ rt � nl � cl (6)
where rt represents repair time (h), ni is number of repairs and cl is man hour cost(cu /h):
PMi ¼ SPPiþLCPi (7)
where SPPi and LCPi represent spare part and labour costs for preventive maintenance(cu), respectively:
SPPi ¼ SPViþSPLi (8)
LCPi ¼ rt � nl � cl (9)
3.2 Operating costData on O&M costs were collected over four years and stored in the MAXIMOcomputerised maintenance management system (CMMS). Operating cost can be
211
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
defined as recurring costs for efficiently operating the equipment, in our case study,a drilling rig. The operating costs include administration, energy, fuel, indirectoverhead costs, consumables like steel rods, operator’s salary, a figure given to usby experts at the collaborating company. In CMMS, the cost data are recorded based oncalendar time. Since drilling is not a continuous process, the operating cost is estimatedby considering the utilisation of the drilling rig. The company plans to use the machinefor 120 months. Therefore, extrapolation for the O&M cost data was done. Figures 2and 3 illustrate the maintenance and operating costs determined by the dataextrapolation.
In Figures 2 and 3, the dots represent the real historical data for maintenance andoperating costs. Curve fitting is done by using Table curve 2D software to show thebehaviour of these costs before and after the time when data were collected. Note thatthe fitting would be better if more data were available for a time period of more thanfour years. This software uses the least squares method to find a robust (maximumlikelihood) optimisation for nonlinear fitting. It is worth mentioning that the drillingmachine in this case study has no multi-level preventive maintenance programme.In addition, it was new at the start of utilisation. This is the main reason why themaintenance cost is quite low in earlier months. The history shows that whenthe maintenance costs started growing, the user company began to keep track of cost
data by using CMMS. The equation “Lorentzian Cumulative” of extrapolation forexpected maintenance cost obtained by the software is expressed as:
Y ¼ ap
arctanx�bc
� �þp2
� �(10)
where Y represents the expected maintenance cost, a¼ 217.42, b¼ 112.37, c¼ 13.63,r2 (adj.)¼ 0.97 and X represents the time (1, 2, 3, 4, […], nmonths). Similarly, the equation“Lorentzian Cumulative” of extrapolation for expected operating cost is expressed as:
Y ¼ ap
arctanx�bc
� �þp2
� �(11)
where Y represents the expected operating cost, a¼ 79.89, b¼ 109.2, c¼ 13.85,r2 (adj.)¼ 0.91 and X represents the time (1, 2, 3, 4, […], n months).
As the figures show, the O&M costs increase over time. In fact, the number offailures increases with time and/or the machine consumes more energy due to machinedegradation.
3.3 Compensation costIn the present study, we focus on the compensation cost by using a redundant rig costas one of the critical factors affecting the ERT. Mining companies, for example, losea large amount of money each year from lost production which, in turn, is due to theproduction equipment’s downtime. In fact, this may be the most important factoraffecting the ERT of production machines. In this study, we assume when a drilling rigfails and is sent to the workshop for maintenance, to continue production without stops,the company uses a redundant rig which has the same performance as the existingfaulty rig. Since in the mining industry, downtime in production is almost zero, thecompensation cost in this case represents the cost of using a redundant rig. In thisstudy, the experts at the collaborating mine classified the rig’s failures in threecategories as[1]:
(1) failures fixed by maintenance team at the workshop;
(2) failures fixed by maintenance team at the production point (mining room); and
(3) failures fixed by operators at the production point (mining room).
Detailed information, such as experience in years and work position of the experts,appears in Table I.
The compensation cost based on a Category 1 failure of the rig is modelled asfollows:
COi ¼ RCi (12)
where RCi represents redundant rig cost (cu):
RCi ¼ PTi � CRi (13)
where PTi and CRi represent the used time of the redundant rig (h) and redundant rigcost per hour (cu/h), respectively:
PTi ¼ TRiþTFi (14)
213
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
where TRi and TFi represent the logistic time of the redundant rig (h) and the time torestore the faulty rig to operation (h), respectively:
TRi ¼ T1iþT2i (15)
where T1i and T2i represent the time to move the redundant rig from its location to theproduction point and the return time from the production point to its original location(h), respectively:
TFi ¼ TMiþTWiþTLi (16)
where TMi, TWi and TLi represent for the time to move the faulty rig from theproduction point to the workshop (h), time in workshop (h) and return time after repair(from workshop to production point) (h), respectively:
TWi ¼ tdiþ triþ tI i (17)
where tdi, tri and tIi represent delay time in workshop before repair (h), actual repair time(h) and idle time in workshop after repair (h), respectively.
Figure 4 illustrates the time the redundant rig is used due to a category 1 failure inthe existing rig.
Table II represents the clarifications of symbols A, B, C, D, E and G of Figure 2.Since the moving speed inside the underground mine is limited to low speed, we
assume the moving time of the maintenance team from the workshop to the productionpoint is almost equal to the moving time of the faulty rig from a production point to thesame workshop. Thus, the using time a redundant rig is used after a category 2 rigfailure is modelled as[2]:
PTi ¼ TRiþTMiþ tri (18)
Table III illustrates the minimum and maximum time values given by the maintenanceexpert in the collaborating mine; these are used in the model.
Current position at companies (U) and (M) Expert field and experience (no. of years)
Maintenance engineer for open pit andunderground mines (U)
Maintenance of mobile and fixed equipment’s (23)
Mine production foreman (U) Underground drill machines (30)Mine production manager (U) Mine drilling and production (15)Mine production planner (U) Mine production planning (22)Maintenance supervisor (U) Maintenance of mobile equipment’s (30)Maintenance manager (U) Maintenance of mobile equipment’s (26)Mine production manager (U) Mine drilling and production (32)Maintenance foreman (U) Maintenance of mobile equipment’s (25)Maintenance engineer for fixed equipment (U) Maintenance of fixed equipment’s (10)Global service operations manager (M) Maintenance of equipment (20)Design engineer–underground drill rigs (M) Designing underground equipment (10)Global fleet manager Marketing and business management (8)Vice president service operations (M) Parts and Service Business management and
Maintenance of mobile equipment (18)Regional business-Europe and product linemanager-rental (M)
Project management and business management (10)
Table I.Description ofexpertise of theexperts used in thepresent study
214
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
We assume the moving time of the redundant rig T1i is equal to the moving time of thefaulty rig TMi. It is worth mentioning that the time values tdi, tIi, TMi, TLi, T1i and T2iare randomly generated by using MATLAB code, since this type of data is notavailable from the collaborating mine. We use a discount rate of 10 per cent toconsider the “time value of money” following the suggestions of the collaboratingmining company.
3.4 Resale valueA Matheson formula (declining balance depreciation model) was used to estimate theresale value of the rig after each month of operation. In this method, a fixed percentageof the book value at the beginning of the month represents the monthly depreciation ofthe rig. The rig resale value is its value if/when the firm wants to sell it at any time
Symbol Clarification
A Production stops and a redundant rig starts moving from its locationB Production starts with a redundant rig and a faulty rig starts moving to the workshopC Faulty rig enters the workshopD Faulty rig exits the workshopE Faulty rig starts work after repair and redundant rig starts moving to its original locationG Redundant rig arrives at the original location
Table II.Clarifications of
symbols A, B, C, D,E and G of Figure 2
C
T1i TM i tdi tri tIi
D
TLi
TW i
TFi
PTi
A B
T2i
E
G
Time
Figure 4.Time the redundant
rig is used due toa category
one rig failure
Time Minimum Maximum
Moving time of faulty rig from production point to workshop (TMi) 30 60Delay time in workshop before repair (tdi) 30 90Idle time in workshop after repair (tIi) 30 60
Table III.Minimum andmaximum time
values (minute) usedin the model
215
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
during its lifetime. The resale value denoted by Si is calculated by using the followingmodel (Luderer et al., 2010; Eschenbach, 2010; Dhillon, 2010):
Si ¼ BV 1 � 1�Drð Þi (19)
where “i” represents a time (number of months), i¼ 1, 2, 3, […]120 planed lifetime, BV1and Dr represent the rig value on the first day of operation and depreciation rate,respectively. In addition:
BV 1 ¼ AC � A (20)
where A represents the percentage of decrease multiplied by the rig acquisition cost torepresent the rig value on the first day of use. During discussions with us, companyexperts agreed that the rig acquisition cost decreases by 10 per cent on the first day ofuse (i.e. A¼ 0.9). In this study, the rig acquisition cost is 6,000 (cu). Hence, the rig valueon the first day of use is 5,400 (cu).
The depreciation rate that allows for full depreciation by the end of the plannedlifetime of the rig is modelled by the following formula (Luderer et al., 2010; Dhillon,2010):
Dr ¼ 1� SVBV 1
� � 1T
(21)
where T and SV represent the planned lifetime of the rig, 120 months, and rig scrapvalue, respectively. The rig is assumed to reach scrap value after 10 years. The rigresale value is calculated by:
Si ¼ AC � A� 1�Drð Þi (22)
The declining balance depreciation model is suitable in our case study because thismodel writes off the cost of the rig early in its lifespan at an accelerated rate and atcorrespondingly lower monthly charges close to the end of its lifespan. It also considersthe rig to be more productive when it is new, and its productivity declines continuouslydue to rig aging. Therefore, in the early years of its lifespan, a rig will generate morerevenue than in later years. In accountancy, depreciation refers to two aspects of thesame concept. The first is the decrease in the rig value. The second is the systematicallocation of the capital cost of the rig over its lifespan. The scrap value is an estimateof the value of the equipment at the time it is sold or disposed of. In our case study, 50(cu) is assumed to be the scrap value of the rig at the end of its planned lifetime, a figuregiven to us by company experts.
4. Results and discussionWe tested the model for ERT on a case study of a drilling rig. This rig is manufacturedby Atlas Copco Company and used by Boliden mineral AB Company in Sweden.MATLABTM software is used to enable a variation of the replacement time (RT ) ofEquation (1) which minimises the total cost.
Figure 5 shows the optimisation curve and the ERT of our case study at a redundantrig cost per hour equal to 1 (cu/h).
The results show the lowest possible total cost can be achieved by replacing the rigat 104 months of its planned lifetime. A decision to replace the rig before or after its
216
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
ERT incurs greater costs for the user company. The use of a lower replacement age(i.e. less than 104 months) incurs higher costs due to the high investment cost.Meanwhile, if the lifetime of the rig exceeds its ERT (i.e. more than 104 months), losseswill increase for two reasons:
(1) the O&M and redundant rig costs increase when the operating hours increasedue to rig degradation; and
(2) the rig resale value will decrease for each month of operation until it reaches itsscrap value by the end of its planned lifetime.
As Figure 5 also shows, there is a range 97-109 (months) when the minimum total costcan be still achieved in practice. In this study, we call it the economic replacementrange. Finding the economic replacement range is an important result of our study, as itcan help decision makers in their planning. To show the effect of the redundant rig costper hour (CRi) in the ERT of our case study, we change the values of the redundant rigcost per hour from 1 to 6 (cu/h). Figure 6 shows the result.
It is clear from Figure 6 that increasing the CRi (cu/h) has a negative effect on theERT of the drilling rig. To determine the effect of other factors on the ERT, we performa sensitivity analysis on rig acquisition, operating, maintenance and redundant rigcosts (cu) using the ANN technique. Four MATLAB codes for six cases of CRi (1-6 cu/h)are used to identify the effect of increased acquisition cost (IAC), decreased operatingcost (DOC), decreased maintenance cost (DMC) and decreased redundant rig cost(DRC). The resulting ERT from these four codes is fed as input to the ANN and theresults translated into a relatively simple equation to estimate the ERT of the drillingrig. The method of partitioning weights, proposed by Garson (1991) and adoptedby Goh (1995), is used to determine the relative importance of the various input factors(see Figure 7).
As evident in Figure 7, the most important factor is the redundant rig cost, followedby the acquisition, maintenance and operating costs. Therefore, a design for reliabilityand maintainability should be adapted to reduce the downtime and maintenance costsof the drilling rig. As mentioned earlier, four MATLAB codes are used to identify theeffect of IAC, DOC, DMC and DRC on the ERT of drilling rig. We choose the case where
0 20 40 60 80 100 120 140 160 180 200 220 2400
5
10
15
18 x 104 Economic replacement time of a drilling rig
Replacement time RT (month)
Tot
al c
ost (
cu)
Redundant rig cost = 1 (cu/h)
ERT=104 monthsFigure 5.Economic
replacement time ofthe drilling rig
217
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
CRi¼ 1 (cu/h) to demonstrate the effect of these factors on the ERT. Figure 8 showsthe correlation ofDRC and IAC for a given 25 per cent DOC and DMC. Figure 9 shows thecorrelation of DRC and DMC for a given 25 per cent IAC and DOC. Figure 10shows the correlation of DRC and DOC for a given 25 per cent IAC and DMC.
As Figures 8-10 show, DRC, IAC, DMC and DOC have a positive effect on the ERTof the drilling rig, but it is also evident that DRC has a more positive effect, followedby IAC, DMC and ROC.
Figure 6.Effect of theredundant rig costper hour on the ERTof the drilling rig
0
10
20
30
40
50
60
1 2 3 4 5 6IAC (cu) 33.1 33.1 31.7 34.6 30.5 32.8
DOC (cu) 14.1 11.9 8.0 9.4 10.3 3.2
DMC (cu) 20.4 15.0 17.7 10.1 12.3 6.2
DRC (cu) 32.2 39.8 42.3 45.7 46.6 57.5
Rel
ativ
e 1m
port
ance
(%
)
Figure 7.Relative importanceof input factors onERT of drilling rig
218
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
0 5 10 15 20 25 30 35 40 45 50110
112
114
116
118
120
122
124
126
ER
T (
mon
th)
Decreasing redundant rig cost (%)
IAC=10%IAC=20%IAC=30%IAC=40%IAC=50%
DOC=25%DMC=25%
Figure 8.Correlation of DRCand IAC for a given
25 per cent DOCand DMC
0 5 10 15 20 25 30 35 40 45 50
112
114
116
118
120
122
124
126
ER
T (
mon
th)
Decreasing redundant rig cost (%)
DMC=10%DMC=20%DMC=30%DMC=40%DMC=50%
IAC=25%DOC=25%
Figure 9.Correlation of DRC
and DMC for a given25 per cent IAC
and DOC
0 5 10 15 20 25 30 35 40 45 50
114
116
118
120
122
124
ER
T (
mon
th)
Decreasing redundant rig cost (%)
DOC=10%DOC=20%DOC=30%DOC=40%DOC=50%
IAC=25%DMC=25%
Figure 10.Correlation of DRC
and DOC for a given25 per cent IAC
and DMC
219
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
4.1 Training and testing the proposed ANN modelANNs can perform nonlinear modelling without prior information and are able tolearn complex relationships between inputs and outputs; the process is also fast(Ahmadzadeh and Lundberg, 2013). Our ANN analyses are based on the resultsobtained from the four cases represented by four MATLAB codes, as explained above.The resulting ERTs from these codes are fed as inputs to ANN and the resultstranslated into a relatively simple equation which can be used to estimate the overallERT of the drilling rig. The equation is transformed to an Excel spread-sheet to makeERT estimation quick and easy for any engineer to apply. As mentioned earlier, theproposed model has four inputs: IAC, DOC, DMC and DRC. A hidden layer with threeneurons and a nonlinear transfer function allows the network to learn nonlinear andlinear relationships between input and output variables. The number of neurons in theoutput layer is constrained to one, as the output only requires one parameter, in thiscase, the ERT of the drilling rig. 90 per cent of the data are used in training and10 per cent in testing the neural network; see Figures 11 and 12. The model shownin Figures 11 and 12 have very high values of R¼ 99 per cent for ANN. However, asalso shown in the figures, the neural network model yields outputs very close to thedesired targets with a high level of accuracy.
The proposed ANNmodel is used to construct a formula to calculate the ERT of ourcase study. The formula is transformed to an Excel spread-sheet to make ERTestimation quick and easy for any engineer to apply. The structure of the optimal ANNmodel is shown in Figure 13; its connection weights and threshold levels aresummarised in Table IV.
Pre-processing data by scaling improves the training of the neural network. Toavoid a slow rate of learning, specifically near the end points of the output range (due tothe property of the sigmoid function, which is asymptotic to values 0 and 1), the input
105 110 115 120 125 130 135105
110
115
120
125
130
135
Targets T
Out
puts
Y, L
inea
r F
it: Y
= (
1)T
+ (
−0.4
4)
Outputs vs Targets, R = 0.99819
Data Points
Best Linear Fit
Y = T
Figure 11.Training capability
220
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
and output data are scaled in the interval between 0.1 and 0.9 (Oreta, 2004). It should benoted that any new input data should be scaled before being presented to the networkand the corresponding predicted values should be un-scaled before use (Yousif, 2007).The linear scaling equation is expressed by:
Xs ¼0:8D
� �Xþ 0:9�0:8Xmax
D
� �(23)
105 110 115 120 125 130 135105
110
115
120
125
130
135
Targets T
Out
puts
Y, L
inea
r F
it: Y
=(1
)T +
(−0
.81)
Outputs vs Targets, R = 0.99829
Data PointsBest Linear FitY = T
Figure 12.Testing capability
IAC (%)
DOC (%)
DRC (%)
DMC (%) Output (ERT)
Inpu
t fac
tors
1
2
3
4
5
6 8
7
Figure 13.Optimal structure
of artificialneural network(ANN) model
Hidden layer nodes
wij weights from node ith input layer to node jthhidden layer
where Xs represents the scaled value of input factors and X represents the un-scaledvalue of input factors. As it used in MATLAB code for neural networks, Equation (23)is used here for an IAC, DOC, DMC and DRC between a minimum increasing ordecreasing percentage of 1 per cent (Xmin) and a maximum increasing or decreasingpercentage of 50 per cent (Xmax). This results in:
D ¼ Xmax�Xmin (24)
The equation length depends on the number of nodes in the hidden layer. Adoptingthree nodes gives an accuracy of 99 per cent. The small number of connection weightsof the neural network enables the ANN model to be translated into a relatively simpleformula, in which the predicted ERT can be expressed as follows:
ERTs ¼1
1þexp� y8 þ w5:8
11þ e�x1
� �þ w6:8
11þ e�x2
� �þ w7:8
11þ e�x3
� �n o (25)
where ERTs represents the scaled ERT derived from the ANN model, θj represents theoutput threshold and wij represents the weight from node i in the hidden layer to node jin the output layer. Hence:
To obtain the actual value of ERT, the predicted ERTs must be re-un-scaled using thefollowing formula:
X ¼ XsD0:8
� ��0:9
D0:8
� �þXmax (29)
Equation (29) is obtained by solving Equation (23), considering the input variable (X) asunknown and the output variable (Xs) as known. An Excel spread-sheet can be used asa substitute for fast and accurate calculation of the ERT of the drilling rig. Equation(30) is applied to estimate the actual value of the ERT of the drilling rig as follows:
ERT ¼ ERTsERTmax�ERTmin
0:8
� ��0:9
ERTmax�ERTmin
0:8
� �þERTmax (30)
where ERTmax and ERTmin represent the maximum and minimum values of ERT,respectively, derived from the optimisation model.
In this paper, it is worth to mention that the ANN techniques were used for thefollowing two main reasons:
(1) One aim was to help the engineers and decision makers in the user company toestimate the ERT of new drilling rigs without needing to use complicatedsoftware.
(2) Another aim was to determine the relative importance of factors which wereused in the optimisation model and which would affect the ERT of new drillingrigs. The factors which have the highest impact on the ERT of new rigs shouldbe prioritised in the development process of new drilling rigs.
222
JQME21,2
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
4.2 Regression analysisThe regression analysis of the results obtained from the above four MATLAB codesuses Minitab software and the least squares method. ERT is modelled as a linearfunction of IAC, DOC, DMC and DRC. The regression analysis results in the followingmathematical model:
It is evident from the regression analysis for this particular case (i.e. CRi¼ 1 cu/h) thatthe IAC has the greatest effect on the ERT of the drilling rig, followed by DRC, DMCand DOC, but at the same time, CRi (cu/h) increases from two to six for other cases.Ultimately, the DRC has the largest effect on ERT in our case study; see Figure 7. TheR2 value obtained from the regression analysis, R2 (adj)¼ 99 per cent, indicates that theERT of the drilling rig depends linearly on the factors of IAC, DOC, DMC and DRC,supporting the results obtained in the sensitivity analysis.
5. ConclusionsThis paper presents a model for the economical replacement time of productionmachines. Although the problem has been solved previously by other researchersusing different models, our model can more readily examine the relationship betweenthe factors affecting the ERT of production machines, especially the cost of usinga redundant rig. The model is found to be a good choice for estimating the ERT in a casestudy of the drilling rig used in underground mines in Sweden, and it can be extendedto other production capital assets in other industries. In our case study, the results ofthe sensitivity analysis show that the redundant equipment cost has the highest impacton the ERT followed by equipment acquisition, maintenance and operating costs. Theresults of the sensitivity analysis also indicate that decreasing the operating,maintenance and redundant rig costs have a positive effect on increasing the ERT. Theresults obtained from the optimisation curves show that increasing the redundant rig costper hour has a negative effect on the ERT. Therefore, improving the reliability andmaintainability of production equipment is essential to reduce their downtime andmaintenance costs.
The absolute ERT of the drilling rig when CRi¼ 1 cu/h is 104 months. However, theERT has a range of 97-109 months, during which period the total cost remains almostconstant. This means the user company has the flexibility of making replacementswithin the optimum replacement age range (12 months). The results of the regressionanalysis show that the ERT of the new equipment depends linearly on its acquisition,operating, maintenance and redundant rig costs. These results confirm the resultsof the sensitivity analysis. In summation, this study presents a comprehensive andvery practical approach which can determine the ERT of any mobile equipment withhigher levels of certainty by using ANN analysis.
Notes1. Note: we obtained information on the drilling process and maintenance of drilling rigs by
talking with experts at the user company (U) and manufacturing company (M).
2. Note: as the failures fixed by operators are classified as small failures and take only a shorttime, the mining company does not use a redundant rig in the third category of failures.
223
Model for ERTof mining
production rigs
Dow
nloa
ded
by L
UL
EA
UN
IVE
RSI
TY
OF
TE
CH
NO
LO
GY
At 0
3:06
07
May
201
5 (P
T)
ReferencesAhmadzadeh, F. and Lundberg, J. (2013), “Remaining useful life prediction of grinding mill liners
using an artificial neural network”, Minerals Engineering, Vol. 53, pp. 1-8, available at:http://dx.doi.org/10.1016/j.mineng.2013.05.026
Al-Chalabi, H., Ahmadzadeh, F., Lundberg, J. and Ghodrati, B. (2014a), “Economic lifetimeprediction of a mining drilling machine using artificial neural network”, InternationalJournal of Mining, Reclamation and Environment, Vol. 28 No. 5, pp. 311-322, available at:http://dx.doi.org/10.1080/0013791X.2014.952466
Al-Chalabi, H., Lundberg, J., Ahmadi, A. and Jonsson, A. (2014b), “Case study: model for economiclifetime of drilling machines in the Swedish mining industry”, The Engineering Economist,pp. 1-17, available at: http://dx.doi.org/10.1080/0013791X.2014.952466
Bazargan, M. and Hartman, J. (2012), “Aircraft replacement strategy: model and analysis”,Journal of Air Transport Management, Vol. 25, pp. 26-29, available at: www.sciencedirect.com/science/article/pii/S0969699712000865#
Bean, J.C., Lohmann, J.R. and Smith, R.L. (1985), “A dynamic infinite horizon replacementeconomy decision model”, The Engineering Economist, Vol. 30 No. 2, pp. 99-120.
Bellman, R. (1955), “Equipment replacement policy”, Journal of Society for Industrial and AppliedMathematics, Vol. 3 No. 3, pp. 133-136.
Bethuyne, G. (1998), “Optimal replacement under variable intensity of utilization and technologicalprogress”, The Engineering Economist, Vol. 43 No. 2, pp. 85-105.
Blanchard, B.S., Verma, D. and Peterson, E.L. (1995), Maintainability: a Key to EffectiveServiceability and Maintenance Management, John Wiley & Sons, New York, NY.
Cheevaprawatdomrong, T. and Smith, R.L. (2003), “A paradox in equipment replacement undertechnological improvement”, Operations Research Letters, Vol. 31 No. 1, pp. 77-82.
Dandotiya, R. (2012), “Decision support models for the maintenance and design of mill liners,PhD thesis, Lulea University of Technology, Lulea.
Dhillon, B.S. (2010), Life Cycle Costing for Engineers, Taylor & Francis Group, New York, NY.
Eilon, S., King, J.R. and Hutchinson, D.E. (1966), “A study in equipment replacement”, Journal ofthe Operational Research Society, Vol. 17 No. 1, pp. 59-71.
Elton, E.J. and Gruber, M.J. (1976), “On the optimality of an equal life policy for equipment subjectto technological improvement”, Operational Research Quarterly, Vol. 27 No. 1, pp. 93-99.
Eschenbach, T. (2010), Engineering Economy: Applying Theory to Practice, 3rd ed., OxfordUniversity Press, New York, NY.
Goh, A.T.C. (1995), “Back-propagation neural networks for modeling complex systems”, ArtificialIntelligence in Engineering, Vol. 9 No. 3, pp. 143-151.
Golmakani, H.R. and Pouresmaeeli, M. (2014), “Optimal replacement policy for condition-basedmaintenance with non-decreasing failure cost and costly inspection”, Journal of Quality inMaintenance Engineering, Vol. 20 No. 1, pp. 51-64.
Hartman, J.C. (2005), “A note on a strategy for optimal equipment replacement”, ProductionPlanning & Control, Vol. 16 No. 7, pp. 733-739.
Hartman, J.C. and Murphy, A. (2006), “Finite-horizon equipment replacement analysis”, IIETransacations, Vol. 38 No. 5, pp. 409-419.
Hartman, J.C. and Rogers, J.L. (2006), “Dynamic programming approaches for equipmentreplacement problems with continuous and discontinuous technological change”, IMAJournal of Management Mathematics, Vol. 17 No. 2, pp. 143-158.
Hartman, J.C. and Tan, C.H. (2014), “Equipment replacement analysis: a literature review anddirections for future research”, The Engineering Economist, Vol. 59 No. 2, pp. 136-153.
Hritonenko, N. and Yatsenko, Y. (2008), “The dynamics of asset lifetime under technologicalchange”, Journal of the Operations Research Letters, Vol. 36 No. 5, pp. 565-568.
Keles, P. and Hartman, J.C. (2004), “Case study: bus fleet replacement”, Engineering Economist,Vol. 49 No. 3, pp. 253-278.
Kumar, U. (1994), “Recent trends in mine maintenance technology and management”, CorrosionReviews, Vol. 12 Nos 3-4, pp. 191-200.
Luderer, B., Nollau, V. and Vetters, K. (2010), Mathematical Formulas for Economists, 4th ed.,Springer, Heidelberg, Dordrecht, London and New York, NY.
Oakford, R.V., Lohmann, J.R. and Salazar, A. (1984), “Adynamic replacement economy decisionmodel”, IIE Transactions, Vol. 16 No. 1, pp. 65-72.
Oreta A.W.C. (2004), “Simulating size effect on shear strength of RC beams without stirrups usingneural networks”, Engineering Structures, Vol. 26 No. 5, pp. 681-691.
Richardson, S., Kefford, A. and Hodkiewicz, M. (2013), “Optimised asset replacement strategy inthe presence of lead time uncertainty”, International Journal of Production Economics,Vol. 141 No. 2, pp. 659-667.
Scarf, P.A. and Bouamra, O. (1999), “Capital equipment replacement model for a fleet withvariable size”, Journal of Quality in Maintenance Engineering, Vol. 5 No. 1, pp. 40-49.
Tanchoco, J.M.A. and Leung, L.C. (1987), “An input-output model for equipment replacementdecisions”, Engineering Costs and Production Economics, Vol. 11 No. 2, pp. 69-78.
Verheyen, P.A. (1979), “Economic interpretation of models for the replacement of machines”,European Journal of Operational Research, Vol. 3 No. 2, pp. 150-156.
Wijaya, A.R., Lundberg, J. and Kumar, U. (2012), “Robust-optimum multi-attribute age-basedreplacement policy”, Journal of Quality in Maintenance Engineering, Vol. 18 No. 3,pp. 325-343.
Yatsenko, Y. and Hritonenko, N. (2005), “Optimization of the lifetime of capital equipment usingintegral models”, Journal of Industrial and Management Optimization, Vol. 1 No. 4,pp. 415-432.
Yousif, S.T. (2007), “Artificial neural network modeling of elasto-plastic plates”, PhD thesis,College of Engineering, Mosul University, Mosul.
About the authorsHussan Saed Al-Chalabi received Bsc Eng Degree in Mechanical Engineering from the MosulUniversity, Iraq in 1994 and MSc Degree in Mechanical Engineering in Thermal Power from theMosul University, Iraq in 2008. Then he joined the Department of Mechanical Engineering atthe Mosul University as a Lecturer. Since 2011, he joined the Division of Operation, Maintenanceand Acoustics at LTU as a Doctoral Student. Hussan Saed Al-Chalabi is the correspondingauthor and can be contacted at: [email protected]
Jan Lundberg is a Professor of Machine Elements at the Luleå University of Technology andalso a Professor in Operation and Maintenance with focus on product development. During theyears 1983-2000, his research concerned mainly about engineering design in the field of machineelements in industrial environments. During the years 2000-2006, his research concerned mainlyabout industrial design, ergonomic and related problems as cultural aspects of design andmodern tools for effective industrial design in industrial environments. From 2006 and forward,his research is completely focused on maintenance issues like methods for measuring failuresources, how to do design out maintenance and how to design for easy maintenance.
Majid Al-Gburi received BSc Eng Degree in Civil Engineering from the Mosul University, Iraqin 1998 and MSc Degree in Structural Engineering in time dependent of concrete behaviour fromthe Mosul University, Iraq in 2001. Then he joined the Department of Dam and Water ResourcesEngineering at the Mosul University as a Lecturer. Since 2011, he joined the Division ofStructural Engineering and Production at the LTU as a Doctoral Student.
Alireza Ahmadi is an Assistant Professor at the Division of Operation and MaintenanceEngineering, Luleå University of Technology (LTU), Sweden. He has received his PhD Degree inOperation and Maintenance Engineering in 2010. Alireza has more than ten years of experiencein civil aviation maintenance as a Licensed Engineer, and Production Planning Manager.His research topic is related to the application of RAMS and Maintenance optimisation.
Behzad Ghodrati is an Associate Professor of Maintenance and Reliability Engineering at theLulea˚ University of Technology. He obtained his PhD Degree on “Reliability based spare partsplanning” from the Lulea˚ University of Technology and he was awarded the PostdoctoralResearch Fellowship from the University of Toronto in 2008.
For instructions on how to order reprints of this article, please visit our website:www.emeraldgrouppublishing.com/licensing/reprints.htmOr contact us for further details: [email protected]
