Simulation-Based Optimization of Maintenance Crew Configuration in Mining Sites

Abstract

The mining sector is a production industry that requires the efficient and interactive use of limited resources such as human resources, proven reserves, and equipment fleets. Mining companies must establish a delicate balance among these resources with the aim of achieving maximum productivity and efficiency. One of the most critical departments of a mining operation is the maintenance and repair department responsible for performing and planning maintenance and repair activities to ensure the operational reliability and reliability of the equipment fleet. These departments require human resources with different technical competencies and specialties. The number of personnel with different qualifications required for a maintenance and repair department is influenced by factors such as the equipment used in the relevant mining operation, the failure modes they are exposed to, the frequencies at which these failures occur, and the subsequent repair times. Uncertainties such as failure frequencies, repair times, and the number of employees assigned to failure modes often exist at different levels, and in situations where uncertainty increases, cases of maintenance and repair not being carried out due to a shortage of personnel are frequently observed. Employing an excessive number of personnel in the field allows maintenance and repair activities to be carried out on time but also leads to high physical expenses. This situation creates a trade-off point between production losses and physical expenses that need to be optimized when it comes to the shortage or excess of maintenance and repair personnel. The current research aims to create a simulation model that takes into account different equipment and failure mode behaviors in a working mining area and attempts to optimize the required maintenance and repair personnel numbers in different specialties accordingly. Thus, the model aims to allocate the necessary personnel to different equipment failure modes to achieve maximum efficiency and performance.

Full text

SIMULATION-BASEDOPTIMIZATIONOFMAINTENANCECREWCONFIGURATIONIN
MININGSITES
MADENSAHALARINDABAKIM-ONARIMEKİBİYAPILANDIRMASININSİMÜLASYONTABANLI
OPTİMİZASYONU
S.F.Sahiner
,*
,O.Golbasi
1
1
MiddleEastTechnicalUniversity,MiningEngineeringDepartment
(*CorrespondingAuthor:fsahiner@metu.edu.tr)

ÖZ
Madencilik sektörü, insan kaynağı, kanıtlanmış rezerv ve ekipman filosu gibi kısıtlı kaynakların
verimliveetkileşimliolarakkullanılmasınıgerektirenbirüretimsektörüdür.Madencilikşirketleri,maksimum
üretkenlikveverimliliğisağlamakamacıyla,bukaynaklararasındahassasbirdengekurmalıdırlar.Birmaden
işletmesininenkritikbölümlerindenbirisi,ekipmanfilosununoperasyonelkalımlılığıvegüvenilirliğinidevam
ettirmeyeyönelikbakımveonarımfaaliyetlerinigerçekleştirenveplanlayanbakımveonarımbölümüdür.Bu
bölümler,farklıteknik yeterlilik ve branşlardainsankaynağınaihtiyaç duymaktadırlar.Bir bakımve onarım
bölümüiçingereklifarklıniteliklerdekipersonelsayısı,ilgilimadencilik operasyonundaçalışanekipmanlar,
bunların maruz kaldıklarıarıza modları,bu arızaların ortaya çıkış frekansları ve sonrasında gerçekleştirilen
onarım sürelerigibifaktörlerdenetkilenmektedir.Arızafrekansları,bakımveonarımsürelerive arızamodu
için görevlendirilen çalışan sayısı gibi belirsizlikler farklı seviyelerdemevcut olup, belirsizliğin arttığı
durumlarda çalışan noksanlığına bağlı olarak bakım ve onarım yapılamama durumları da sıklıkla
gözlenmektedir. Bir sahada, gereğinden fazla personelin çalıştırılması bakım ve onarım faaliyetlerinin
zamanında yapılmasına olanak sağlarken, yüksek fiziki giderlere de neden olmaktadır. Bu durum, bakım-
onarımpersoneliazlığıveçokluğudurumlarındaoluşabileceküretimkayıplarıvefizikimasraflararasındabir
değiş-tokuşnoktasıyanioptimizeedilmesi gerekli birproblemiyaratmaktadır. Mevcut araştırma çalışması,
işleyen bir maden alanında farklı ekipman ve arıza modu davranışlarını dikkate alan ve buna göre farklı
branşlarda gerekli bakım ve onarım personel sayılarını optimize etmeye çalışan bir simülasyonmodelinin
oluşturulmasını amaçlamaktadır. Böylelikle; model, maksimum verimlilik ve performansı sağlamakiçin
gereklipersonellerifarklıekipmanarızamodlarınatahsisetmeyihedeflemektedir.

Anahtar Sözcükler: Sürekli-OlaySimülasyonu, Optimizasyon,Bakım ve Onarım,Madencilik,Makineve
Ekipman,İşgücü


IMCET2023/ANTALYA/TÜRKİYE/November28-December1
541
Cite as: Sahiner, S. F. and Golbasi, O. (2023). Simulation-Based Optimization of Maintenance Crew Configuration in Mining Sites.
Proceedings of the 28th International Mining Congress and Exhibition of Türkiye (pp. 541-549). Antalya: UCTEA Chamber of Mining Engineers.


ABSTRACT
The minin g sector is a produc tion industry that re quires the effic ient and interacti ve use of limited 
resourcessuchashumanresources,provenreserves,andequipmentfleets.Miningcompaniesmustestablisha
delicatebalanceamong theseresourceswiththeaimofachievingmaximumproductivityandefficiency.One
ofthemostcriticaldepartmentsofaminingoperationisthemaintenanceandrepairdepartmentresponsiblefor
performingandplanningmaintenanceandrepairactivitiestoensuretheoperationalreliabilityandreliability
oftheequipmentfleet.Thesedepartmentsrequirehumanresourceswithdifferenttechnicalcompetenciesand
specialties. The number of personnel with different qualifications required for a maintenance and repair
departmentisinfluencedbyfactors suchasthe equipmentused inthe relevant miningoperation,thefailure
modes t hey are exposed t o, the frequencie s at which these  failures occur, a nd the subsequ ent repair times. 
Uncertaintiessuchasfailurefrequencies,repairtimes,andthenumberofemployeesassignedtofailuremodes
often exist at differentlevels, andinsituations whereuncertaintyincreases,casesofmaintenanceandrepair
notbeingcarriedoutduetoashortageofpersonnelarefrequentlyobserved.Employinganexcessivenumber
ofpersonnelinthefieldallowsmaintenanceandrepairactivitiestobecarriedoutontimebutalsoleadstohigh
physicalexpenses.Thissituationcreatesatrade-offpointbetweenproductionlossesandphysicalexpensesthat
needtobeoptimizedwhenitcomestotheshortageorexcessofmaintenanceandrepairpersonnel.Thecurrent
research aims to create a simulation model that takes into account different equipment and failure mode
behaviorsinaworking miningarea and attemptsto optimizetherequiredmaintenanceandrepairpersonnel
numbers in different specialties accordingly. Thus, the model aims to allocate  the necessary personnel to
differentequipmentfailuremodestoachievemaximumefficiencyandperformance.

Keywords: ContinuousEventSimulation,Optimization,MaintenanceCrew,Mining,Workforce
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
542


INTRODUCTION
With mass production and gl obalization improvem ents, machines hav e become crucial in various
manufacturingsectors.Consequently,astrongcorrelationbetweenproductionoperationsandmaintenancehas
become e ssential for the overall success of com panies. Equipment m alfunctions can dire ctly or indirectly
disruptproduction,resultinginsignificantfinanciallosses.Therefore,ithasbecomeimperativeforproduction
companies to establish a competent and well-organized maintenance department with a skilled workforce
possessingdiversecompetenciestoensureuninterruptedproduction.Intheminingsector,whichheavilyrelies
onmachinery, miningcompanies mustalso establish efficient maintenance departmentsin their operational
areas.Mining equipmentvariesincomplexityandis used insurface mines,underground mines,andmineral
processingfacilities, facing multiple failuremodes thatdemanddifferent maintenanceactions. Maintenance
workshops comprise units and employees with diverse qualifications in mining areas. Additionally, the
maintena nce policies in mining are diver se and complex, with main tenance work repres enting a substantial
shareoftheoperatingcost.Therelationshipbetweenmaintenanceandoperatingcostsintheminingindustry
has been extensively discussed in various studies and literature. The findings reveal several important
observatio ns; for instance , equipment m aintenance cos t in mining con stitutes a signifi cant portion, ran ging
from20% to 35%,ofthetotal operating cost(Unger and Conway,1994).Inspecific regionslike Chileand
Indonesia, m aintenance cos ts for surface min es surpass 60% of the  operating cost (Won g et al., 2000). In 
addition,maintenancecostsdominateasubstantialportionoftheequipmentoperatingcostacrossthemining
industry,ranging from40%to50% (KumarandForsman,1992). Moreover, a Finnishcompany sharedthat
maintenance costsin theirmines account forapproximately 30% of theproduction cost (Harjunpaa,1992).
Lastly, un planned mainten ance activities ha ve been shown to  lead to a 10%  production lo ss in Australian 
undergroundcoalmines(Clark, 1990).Theseobservationsunderscorethesignificantimpactofmaintenance
costs on the overall operational expenses in the mining sector and highlight the importance of effective
maintenancestrategiestoensureefficientanduninterruptedproduction.
Theliteraturehighlightsthatmaintenanceisunavoidableinmachinery-basedproductionindustriesand
can significantly impact operating costs, leading to both direct expenses and indirect consequences like
productionloss.Therefore,whendevisingmaintenancepolicies,itiscrucialtodevelopabalancebetweenthe
physical cost of maintenancework and the value of production loss per unit. The composition of the
maintena nce crew, referring to the number of ind ividuals with different qua lifications, is a critical factor in
makingmaintenance-relateddecisions.Findingtherightbalanceinthemaintenancecrewisessentialasboth
over-employmentandunder-employmentcanaffectthecostdynamics,includingdirectandindirectexpenses.
Thisstudyaims to developa continuoussimulationalgorithmtodetermine the optimalmaintenance
crew in t erms of quantity  and qualification.  The objective is  to minimize the  overall cost, con sidering the
stochasticnatureofequipmentfailuresexperiencedataminingsite.

Problem St atement
Maintenance plays a crucialrole in production areas,as itenhancesand sustains the reliability and
functionality of systems. However, maintenance activities can lead to production loss if the maintenance
departmentlacksthenecessaryworkforce.Additionally,maintenanceactionscanbeexpensiveandlimitedby
available resources. Hence, balancing system breakdown costs and maintenance expenses is essential.
Neglecting  maintenance can re sult in excessiv e failures, leadi ng to downtime a nd system dete rioration. To
ensure sustained production and improved operational profitability, it is crucial to establish an optimal
maintenance policy that minimizes both direct and indirect cost items related to employee expenses. In
machine-intensive sectors suchasmining,where production relieson multiple well-coordinatedheavy-duty
machines , maintenance cost sc an be substantial and sig nificantly contri bute to the total operatin g cost. The
maintenance cost included in the operating cost budget can differ significantly across industries. For the
manufacturingindustry,ittypicallyrangesfrom3%to15%,whileformetallurgicalprocesses,itfallsbetween
15%and20%.However,inthehighlymechanizedminingindustry,theaccountedmaintenancecostcanbeas
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
543


highas50%(Ben-Dayaetal.,2016).Furthermore,innumerousindustries,thechallengeofallocatinglimited
resourcestoaspecificsetoftasksisacommonoccurrence.Atthispoint,optimizinglaborresourceallocation
andconfigurationisessentialtoenhancesystems'servicelevelswhileminimizingdirectandindirectcosts.In
the case of a highly machine-intensive industry like mining, determining the optimal human resource
configurationformaintenanceactivitiesbecomesverycrucial.

Objective & Scope of the Study
Thecurrentstudyaimstodevelopasimulationalgorithmcapableofoptimizingthemaintenancecrew
configurationinanoperationinvolvingmultipletypesofequipmentwithrandomfailuremodes.Theprimary
goalistominimizethecumulativecostassociatedwiththe maintenancecrew,encompassingbothdirectand
indirectcostitems.Directcrew-inducedcostsincludevariouselementssuchassalary,insurance,foodservice,
shuttle servic e, rent help, and fa mily help. Indire ct costs, on the other  hand, encompas s production losses
resulting from scheduled maintenance downtime and potential unavailability of the maintenance crew. In
addition to themainobjective,the study aimstoachieve severalsub-objectives. First,anindustrialresearch
componentisconductedtoinvestigatethefactorsdeterminingmaintenancecrewconfiguration,specificallyin
theminingindustry.Understandingthesefactorsiscrucialforoptimizingthecrewstructureeffectively.
Besides, the studyseekstoestablishthedependenciesbetweenproduction lossandthemaintenance
workforce.Byunderstandinghowthesetwofactorsarerelated,theresearcherscanbetterdesignamaintenance
crew config uration that minimi zes production los ses. To facilitate the ob jectives, the resea rch endeavors to
develop a m aintenance cre w simulation algor ithm within a cont inuous event sim ulation environme nt. This
algorithm will enable the evaluation and optimization of the crew configuration under various scenarios.
Moreover, the study involves implementing the developed model using an operational dataset after pre-
processingdata groups.This practicalimplementationusing real-world datawillenhancethe reliability and
relevance of t he results. Lastly , the researchers fo cus on verifying a nd validating the  developed simu lation
algorithm toensure itsaccuracyandeffectiveness in optimizing themaintenance crew configuration.In the
applicationpart, thestudy utilizesthe historical maintenance dataset of a five-excavatorfleet operating in a
surfacecoalmine.Thefailuresarecategorizedintotwocommontypes:mechanicalandelectrical.

LITERATURE REVIEW
Maintenancecanbedefinedastheauxiliaryactivitiestoensurethatasystem,whichmayhavevarying
complexity and functions, remains in a satisfactory state by conducting regular checks, replaceme nts and
repairs on its components. As aresult, amaintenance policyinvolves a combination of actionswith distinct
objectivestoenableacomponenttofunctioneffectivelythroughoutthesystem'sentireservicelife.Theprimary
purpose of ma intenance action si s to enhance the func tionality and depend ability of systems .N evertheless,
improving reliability can be expensive in certain situations and is constrained by technical and financial
limitations. Consequently, there exists a delicate balance between the economic impact of maintenance
activities andthepotentialdeteriorationofthesystem.Toensure optimalresults,amaintenancepolicymust
bedesignedtomaintainthesystem'sreliabilityabovethedesiredlevel,takingintoaccountitsroleandvalue
inproduction.However,itisessentialtoavoidimplementingexcessivelyhigh-ratedpreventiveworkpackages,
as they may lead to additional investment costs and increased system unavailability due to preventive
downtimes.
Foraminingcompany,threecriticalassetsarecrucial:humanresources,orereservesforexploitation,
and an equipment fleet. Among these, human resources employed in operational areas hold particular
significance.Thenumberandqualificationsofpersonnelmustbedeterminedbasedonthedivisionalcapacity
requireme nts in mining areas. N otably, the mainte nance facility is obse rved to be the most lab or-intensive
aspect, asit requiresa considerablenumber ofindividuals withdiverse qualifications to ensure theoptimal
performan ce of the equipment fleet. Several studies have focused on mining equipm ent maintenance and
managementdecision-makingprocessesfortheminingsector.
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
544

Barberá etal.(2014) utilized theGAMMmethod toanalyze two slurry pumps in amining plantin
Chile,suggestingimprovementsforpumpmaintenance.AliandReza(2014)developedanewapproachusing
statistical modelstodetermineloading equipment'soverhaulandmaintenancecostinsurfacemining. Morad
etal.(2014)investigatedmaintenancepoliciesforoperatingtrucksinSungunCopperMinetominimizefailure
downtimes.Kovacevicetal.(2016)describedatwo-stepmethodtoanalyzefactorsinfluencinghumanerrors
duringminingmachines'maintenanceactivity.Nikulinetal.(2016)presentedacomputer-aidedapplicationto
evaluatetheoperationalandmaintenancestrategyforcomplexprocesseswithequipment.GölbaşıandDemirel
(2017)developedasimulationalgorithmtooptimizeinspectionintervalsandminimizemaintenancecostsfor
miningmachines.Jonssonetal.(2018)discussedanalyzingdigitalizedcondition-basedmaintenancedatainan
iron ore mine. Angeles and Kumral (2020) proposed a maintenance management approach to improve
equipmentavailability and reliability inaminingtruck fleet. These studies contribute valuable insights and
practical tools for effectively maintaining mining systems regarding cost minimization and availability
maximization.
In addition, various simulation studies in the mining industry have been conducted to evaluate
uncertaintiesattheoperationallevel,optimizeprocesses,andaddressvariousaspectsofminingoperations.For
instance, Hashemi and Sattarvand (2014) developed a model using discrete-event simulation to analyze
interaction s between loadi ng and hauling syst ems in mines, r esulting in improved  dispatching sy stems and
reducedtruckqueueingtime.UpadhyayandNasab(2018)presentedasimulationandoptimizationframework
toenhanceshort-termproductionplanninganddecision-makinginminingoperations,consideringuncertainties
and dependencies between various factors. Golbasi and Demirel (2017) introduced an inspection interval
optimize r using a stochast ic, continuous , and dynamic  simulation str ucture to determ ine the best co st-wise
decisionsinequipmentmaintenancepolicies.
Other studies explored optimiza tion in truck dispatching and allocation to shovels (Moradi et al.,
2019), truck allocation considering uncertainties in dispatching operations (Moradi et al., 2019), and the
effectof humanfactorsonminingequipmentreliability(Ozdemirand Kumral,2018).Additionally,Ozdemir
and Kumral (2019) proposed a two-stage dispatching system to maximize the utilization of truck-shovel
systems, le ading toi ncreased material quantity .Gol basia ndTu ran(2 020) introduced a maintenance po licy
optimizer todetermine optimal maintenance work packages based on equipment uptime and downtime
character istics. Bernardi et al. (2 020) compared mat erials handling sy stems in a mine to optim ize handling
systemsand minimizeoperatingcostsusingdiscreteeventsimulation.GolbasiandKina(2022)developeda
fuelconsumptionsimulatortoevaluatefuelusageinhaultrucksoperatingunderstochasticconditions.
These studieshaveprovidedvaluable insightsandpracticaltools for effectively maintaining mining
systemsregardingcostminimizationandavailabilitymaximization,improvingminingoperations,optimizing
maintenance policies, and enhancingdecision-making processes. However,optimization of human resource
configurationinminingactivitieshasnotbeenobservedintheliterature.

MODEL DEVELOPMENT
The algorithm's objectiveis todetermine the optimal maintenance crew configuration for a mining
area,ensuringthemostcost-effectiveoperationbystrikingabalancebetweenphysicalexpensesandproduction
losses re sulting from over or  under-employm ent of skilled wor kers. The mainten ance crew incurs v arious
physicalcostitems,includingwages,employmentinsurance,foodservice,transportation,andaccommodation.
Over-employment in different maintenance branches can lead to a significant increase in direct costs.
Conversely,under-employmentinamaintenancebranchcancausenotableproductionlossesasrelatedfailure
types may n ot receive timely at tention, impac ting machinery avai lability. It is cruc ial to avoid overla pping
maintenanceactivitiesforsimilarfailuremodesthatrequiresimilartechnicalcompetency,asfailuretoevaluate
failure mode characterization and crew member occupancy rates jointlycan disrupt machinery availability.
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
545

Giventhatminingareasutilizenumerousequipmentwithvaryingnumbersandoperationalrequirements,
any misjudgment in maintenance behavior can lead to catastrophic situations, reduce equipment
utilization, and result in additional unavailability periods that harm short-term production plans. The
algorithmlogicisillustratedinFigure1briefly.



Figure1.SimulationModelAlgorithm
The model was tested on a fleet of five excavators, each with distinct failure occurrence and
maintenance characteristics. Failures were categorized into mechanical and electrical types, which
determinedthe crewgroups.Through200simulations,theinteractionswithin the systemwereevaluated
by varyin g the total numb er of mechanical an d electrical crew  members in each  run. Each simulat ion
coveredanobservationperiodof4,383hours.Theresultsindicatedthattheoptimizedcrewconfiguration,
consisting of 4 members inthe electrical and 4 members in the mechanical divisions, minimized the
cumulativedirectandindirectcosts.Thefleet'sgeneralevaluation,includingfiveexcavators,canalsobe
seenandFigure2.


IMCET2023/ANTALYA/TÜRKİYE/November28-December1
546



Figure2.TotalDowntime(B)andTotalCost(A)for5ExcavatorsforEachCrewPolicy

CONCLUSIONS
In a machine-based production company, the maintenance department consists of various
divisionswithdifferentcrewconfigurations,dependingonthecompany'sproductionprofile,complexity,
andtypesofmachinesinvolvedinproductionphases.Inminingareas,operationscanbeeithersurfaceor
underground, depending on the mining method, and specific heavy-duty machinery with varying
production rates is requiredbased on the mining type and production capacity. A mining company
typically possesses a large fleet of machines, including loading, hauling, drilling, and auxiliary
equipment.Eachmachinemayexperiencedifferentfailuremodeswithvaryingoccurrencefrequency and
consequences,resultingindifferentdowntimeprofiles.

Given that the m aintenance cre w is a limited resource  with specific num bers of people in each
division,it is crucial to determine the optimal configuration of the maintenance crew, considering the
trade-of fbet weenthe  total financial co nsequences of diff erent crew config urations. To addr ess this, the
studydevelopsa multi-scenario continuous-eventsimulationmodeltoidentifytheoptimalcapacityand
qualification of the maintenance crew for a functional mining operation. The developed model is
implemented for the maintenancecrew requirement of a fleet covering five excavators experiencing
randomfailureswithvaryingmaintenancerequirementsanddurations.Theoverallcostis minimizedfor
acrewconfigurationwith4and4peopleintheelectricalandmechanicaldepartments.
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
547

References

Angeles, E., & Kumral, M. (2020). Optimal Inspe ction and Preventive Maintenance Scheduling of Mining
Equipment. Journal of Failure Analysis and Prevention, 20(4), 1408–1416.
https://doi.org/10.1007/s11668-020-00949-z
Barberá,L., Crespo,A., Viveros,P.,&Stegmaier,R.(2014). Acasestudyof GAMM(graphicalanalysis for
maintenancemanagement)intheminingindustry. ReliabilityEngineeringandSystemSafety, 121,113–
120.https://doi.org/10.1016/j.ress.2013.07.017
Ben-Daya , M., Kumar, U., & Mur thy, D. N. P. (2016). Int roduction to mai ntenance enginee ring: modelling,
optimizationandmanagement.
Bernardi,L.,Kumral,M.,&Renaud,M.(2020).Comparisonoffixedandmobilein-pitcrushingandconveying
and truck-shovel systems used in mineral industries through discrete-event simulation. Simulation
ModellingPracticeandTheory,103,102100.https://doi.org/10.1016/j.simpat.2020.102100
Clark, D.: T ribology – its appl ication to equipme nt reliability and m aintainability de sign in the underg round
coalminingindustry.In:ProceedingsoftheInstitutionofEngineersAustraliaTribologyConference,pp.
38–44(1990)
Demirel, N., Minerals, O. G.-, & 2016, undefined. (2016). Preventive replacement decisions for dragline
componentsusingreliabilityanalysis.Mdpi.Com.https://doi.org/10.3390/min6020051
Forsmann,B.,Kumar,U.:Surfaceminingequipmentandmaintenancetrends inthescandinaviancountries.J.
MinesMineralsFuelsAugust/September266–269(1992)
Golbasi, O., & Demirel, N. (2017). Simulation of an Active Maintenance Policy: A Preliminary Study in
Dragline Maintenance Optimization. Lecture Notes in Mechanical Engineering, 669–679.
https://doi.org/10.1007/978-3-319-23597-4_49
Golbasi, O., & Kina,E.(2022).Haultruck fuelconsumption modelingunderrandomoperating conditions:A
case study. Transportation Research Part D: Transport and Environment, 102, 103135.
https://doi.org/10.1016/j.trd.2021.103135
Golbasi, O., & Turan, M. O. (2020). A discrete-event simulation algorithm for the optimization of multi-
scenariom aintenance pol icies. Computers & Indus trial Engineering ,1 45, 106514.
Harjunpaa,H.:Howtodeterminetheeffectivenessofanindustrialmaintenanceorganizationanditsinfluence
on the profitability of a company. In : Proceedings of the Eu ro Maintenance Conf erence, pp. 101–104
(1992)
Hashemi,A.S.,&Sattarvand,J.(2014).LNPE9-ApplicationofARENASimulationSoftwareforEvaluation
of Open P it Mining Tran sportation Sy stems – A Case S tudy. https://d oi.org/10.1007/97 8-3-319-1230 1-
1_20
https://doi.org/10.1016/J.CIE.2020.106514
Jonsson, K.,Mathiassen, L., Holmströ, J.,& Jonsson,K. (2018). Representation andmediation in digitalized
work:evidencefrommaintenanceofminingmachinery.https://doi.org/10.1057/s41265
Kovacevic,S.,Papic, L.,Janackovic,G.L.,& Savic, S.(2016).The analysisof humanerroras causesinthe
maintenance of machines: A case study in mining companies. South African Journal of Industrial
Engineering,27(4),193–202.https://doi.org/10.7166/27-4-1493
Lashgari, A., & Sayadi, A. R. (2013).Statisticalapproachtodeterminationof overhaulandmaintenance cost
ofloadingequipmentinsurfacemining.InternationalJournal ofMiningScienceandTechnology,23(3),
441–446.https://doi.org/10.1016/j.ijmst.2013.05.002
Morad, A. M., Pourgol-Mohammad, M., & Sattarvand, J. (2014). Application of reliability-centered
maintenanceforproductivityimprovementof openpitminingequipment:CasestudyofSungun Copper
Mine.JournalofCentralSouthUniversity,21(6),2372–2382.https://doi.org/10.1007/s11771-014-2190-2
Moradi Afrapoli,A., & Askari-Nasab,H. (2019).Mining fleetmanagementsystems:areviewofmodels and
algorithms. International Journal of Mining, Reclamation and Environment, 33(1), 42–60.
https://doi.org/10.1080/17480930.2017.1336607
 
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
548

Moradi Afrapoli, A .,T abesh, M., & Askari-Nas ab, H. (2019a). A mul tiple objective tra nsportation problem
approachto dynamic truck dispatching in surface mines. European Journal of Operational Research,
276(1),331–342.https://doi.org/10.1016/j.ejor.2019.01.008
Moradi Af rapoli, A., Tabesh , M., & Askari- Nasab, H. (2019 b). A stochastic h ybrid simulation -optimization
approach towards haul fleet sizing in surface mines. Mining Technology, 128(1), 9–20.
https://doi.org/10.1080/25726668.2018.1473314
Nikulin, C., Ulloa,A.,Carmona,C.,&Creixell, W. (2016).AComputer-aidedApplicationforModelingand
Monitoring  Operational and  Maintenance Inf ormation in Mini ng Trucks. Archi ves of Mining Scie nces,
61(3),695–708.https://doi.org/10.1515/amsc-2016-0048
Ozdemir,B., &Kumral,M.(2018).Appraisingproductiontargetsthroughagent-basedPetrinetsimulationof
material h andling system s in open pit m ines. Simula tion Modellin g Practice and  Theory, 87, 138– 154.
https://doi.org/10.1016/j.simpat.2018.06.008
Ozdemir,B.,&Kumral, M.(2019).Simulation-basedoptimizationoftruck-shovelmaterial handlingsystems
in multi-pit surface mines. Simulation Modelling Practice and Theory, 95, 36–48.
https://doi.org/10.1016/j.simpat.2019.04.006
Unger, R. L.andConway,K.(1994),Impact ofmaintainability designoninjury ratesand maintenance costs
forundergroundminingequipment,inImprovingSafetyatSmallUndergroundMines,compiledbyR.H.
Peters,ReportNo.SpecialPublication18–94,USBureauofMines,Washington,pp.140–167.
Upadhyay, S. P., & Askari-Nasab, H. (2018). Simulation and optimization approach for uncertainty-based
short- termplanninginopen pit mines. International Journal of Mining Scienceand Technology, 28(2),
153–166.https://doi.org/10.1016/j.ijmst.2017.12.003
Wong, J.,R.A. &Daneshmend, L.K.& Lipsett, M.& Hall. (2000).Reliability analysis as atool forsurface
miningequipmentevaluationandselection.CIMBulletin.93.78-82.
IMCET2023/ANTALYA/TÜRKİYE/November28-December1
549