Preventive Maintenance and Replacement Scheduling: Models and Algorithms


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1 Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Submtted to the Faculty of the Graduate School of the Unversty of Lousvlle n Partal Fulfllment of the Requrements for the Doctor of Phlosophy Canddacy Department of Industral Engneerng Unversty of Lousvlle Lousvlle Kentucky USA November 2008
2 Copyrght 2008 by Kamran S. Moghaddam All Rghts Reserved
3 Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Approved on November 2008 By the followng Dssertaton Commttee Professor John S. Usher Commttee Char Professor Gerald W. Evans Professor Gal W. DePuy Professor Sunderesh S. Heragu Professor Al M. Shahhossen
4 TABELE OF CONTENTS LIST OF TABLES... v LIST OF FIGURES... v. Introducton.. Preventve Mantenance and Replacement Schedulng Research Contrbutons Outlne Lterature Revew Introducton Optmzaton Models Exact Algorthms Heurstcs and MetaHeurstcs Algorthms Hybrd Algorthms MultObectve Algorthms Smulaton Models Monte Carlo Smulaton DscreteEvent and Contnuous Smulaton Age Reducton and Improvement Factor Models Applcatons Manufacturng and Producton Systems v
5 Servce Systems Power Systems Chapter Summary Optmzaton Models  Exact Algorthms Introducton Formulaton Mantenance Replacement Do Nothng Cost of Preventve Mantenance and Replacements Falure Cost Mantenance Cost Replacement Cost Fxed Cost Total Cost Optmzaton Models Model  Mnmzng total cost subect to a relablty constrant Model 2  Maxmzng relablty subect to a budget constrant Soluton Procedure Computatonal Results Chapter Summary Optmzaton Models  Heurstc Algorthms Introducton Formulaton v
6 4.3. Optmzaton Model MultObectve Genetc Algorthms Representaton of Solutons Ftness Functons Crossover Procedure Mutaton Procedure Generatonal GA Steady State GA Computatonal Results Computatonal Results of Ftness Functon Computatonal Results of Ftness Functon Computatonal Results of Ftness Functon Chapter Summary Research Plan 75 Bblography 76 v
7 LIST OF TABLES 3.. Parameters for the Numercal Example Model  Mantenance and Replacement Schedule Mnmzes Total Cost Model 2  Mantenance and Replacement Schedule Maxmzes Relablty Effectve age of components n Model Effectve age of components n Model Parameters of Genetc Algorthms Nonnferor solutons resulted from Ftness Functon Mantenance and Replacement Schedule Ftness Functon GGA Mantenance and Replacement Schedule Ftness Functon SSGA Nonnferor solutons resulted from Ftness Functon Mantenance and Replacement Schedule Ftness Functon 2 GGA Mantenance and Replacement Schedule Ftness Functon 2 SSGA Nonnferor solutons resulted from Ftness Functon Mantenance and Replacement Schedule Ftness Functon 3 GGA Mantenance and Replacement Schedule Ftness Functon 3 SSGA v
8 LIST OF FIGURES 3.. Effect of perod mantenance on component ROCOF Effect of perod replacement on system ROCOF Effectve age of components n Model Effectve age of components n Model Pareto Optmal Solutons for Ftness Functon Cost Improvement for Ftness Functon Relablty Improvement for Ftness Functon Pareto Optmal Solutons for Ftness Functon Cost Improvement for Ftness Functon Relablty Improvement for Ftness Functon Pareto Optmal Solutons for Ftness Functon Cost Improvement for Ftness Functon Relablty Improvement for Ftness Functon Pareto Optmal Solutons for all Ftness Functons v
9 Chapter Introducton.. Preventve Mantenance and Replacement Schedulng Preventve mantenance s a broad term that encompasses a set of actvtes amed at mprovng the overall relablty and avalablty of a system. All types of systems from conveyors to cars to overhead cranes have prescrbed mantenance schedules set forth by the manufacturer that am to reduce the rsk of system falure. Preventve mantenance actvtes generally consst of nspecton cleanng lubrcaton adustment algnment and/or replacement of subcomponents that wearout. Regardless of the specfc system n queston preventve mantenance actvtes can be categorzed n one of two ways component mantenance or component replacement. An example of component mantenance would be mantanng proper ar pressure n the tres of an automoble. Note that ths actvty changes the agng characterstcs of the tres and f done correctly ultmately decreases ther rate of occurrence of falure. An example of component replacement would be smply replacng one or more of the tres wth new ones. Obvously preventve mantenance nvolves a basc tradeoff between the costs of conductng mantenance/replacement actvtes and the cost savngs acheved by reducng the overall rate of occurrence of system falures. Desgners of preventve mantenance schedules must wegh these ndvdual costs n an attempt to mnmze
10 the overall cost of system operaton. They may also be nterested n maxmzng the system relablty subect to some sort of budget constrant. Other crtera such as avalablty and demand satsfacton mght be consdered as the obectve functons but they wll not be studed n ths dssertaton. The problem s to fnd the best sequence of mantenance actons for each component n the system n each perod over a plannng horzon such that overall costs are mnmzed subect to a constrant on relablty or the relablty of the system s maxmzed subect to a constrant on budget..2. Research Contrbutons In ths dssertaton proposal optmzaton models are developed and solved va exact heurstc and metaheurstc algorthms. Analytcal and statstcal agereducton and mprovement factor models are developed and can be consdered as the man research contrbuton. In partcular the followng contrbutons are made:. Two optmzaton models wll be constructed based on extensons of prevous work n partcular by Usher et al (998). The optmzaton models are solved by usng a dynamc programmng approach. These models also provde a general framework to acheve optmal preventve mantenance and replacement polces and wth modfcatons can be used as a basc closedform model for any type of system. 2. A multobectve optmzaton model s developed based on a set of basc assumptons and engneerng economy consderatons. Ths model s optmzed va multobectve generatonal and steady state genetc algorthms as well as 2
11 by a multobectve smulated annealng algorthm whch allows for the comparson of these optmzaton approaches. 3. In order to estmate the parameters of optmzaton models an analytcal model for estmatng age reducton and mprovement factor parameters wll be developed. In addton a procedure wll be developed to estmate the mprovement factor of any general component due to mperfect mantenance actvtes. 4. Fnally a real case study wll be consdered as the applcaton of developed models and preventve mantenance and replacement schedule resulted from optmzaton models wll be compared wth the current mantenance polcy n that case study..3. Outlne The remander of ths dssertaton proposal s organzed as follows: In Chapter 2 a comprehensve lterature revew of models and applcatons of preventve mantenance and replacement schedulng s presented. In Chapter 3 a formulaton of the optmzaton models s presented and ther computatonal results are analyzed. Chapter 4 ncludes the extenson of Chapter 3 optmzaton models by consderng engneerng economy features. These models have been optmzed by multobectve generatonal and steady state genetc algorthms and the computatonal results obtaned by mplementaton of these algorthms are demonstrated. Fnally n Chapter 5 the plan and ts schedule for the research s presented. 3
12 Chapter 2 Lterature Revew 2.. Introducton Ths chapter has four man sectons. The frst secton presents a complete revew on varous optmzaton models and algorthms related to preventve mantenance and replacement schedulng. Secton 2.3 presents a revew of key works that utlze smulaton models. In Secton 2.4 models that ntroduce and develop age reducton and mprovement factor models are presented. Fnally applcatons of preventve mantenance and replacement schedulng n manufacturng and producton systems servce systems and power systems are revewed Optmzaton Models Exact Algorthms Determnstc optmzaton algorthms have been proposed by varous authors. Yao et al (200) present a twolayer herarchcal model that optmzes the preventve mantenance schedulng n semconductor manufacturng operatons. They develop a Markov decson process and optmze ths model va a mxed nteger lnear programmng model. They defne proft of cluster tools producton as the obectve functon to be maxmzed and consder tme wndow for preventve mantenance
13 actvtes and lmtaton of resources as the constrant whch were nonlnear functons. In order to acheve a global optmum they transfer the nonlnear functons nto lnear ones and use EasyModeler and OSL as the optmzaton software. In addton they utlze AutoSched AP as the smulaton software n order to construct a smulaton model to evaluate the performance of the optmzaton model n a real case study wth preventve mantenance tasks n a oneweek plannng horzon and compare the obtaned results wth the actual preventve mantenance plan. Later Yao et al (2004) extend ther prevous model to be more general apply ths extended model n a producton lne of a semconductor manufacturng system and show the applcaton of t va numercal examples. Jayakumar and Asgarpoor (2004) present a lnear programmng model n order to optmze the mantenance polcy for a component wth deteroraton and random falure rate. They determne optmal mean tmes of mnor and maor preventve mantenance actons based on maxmzng the avalablty of the component. They utlze MAPLE and LINGO for solvng the lnear programmng model of Markov decson process. Duarte et al (2006) present a model and algorthm for mantenance optmzaton of a system wth seres components. In ths research they assume that all components have lnearly ncreasng falure rate wth a constant mprovement factor for mperfect mantenance. In addton they consder the total cost as the obectve functon and the total downtme as the man constrant. In terms of mantenance actvtes they defne preventve and correctve mantenance for each component. Fnally ther algorthm optmzes the nterval of tme between mantenance actons for each component over a plannng horzon. Canto (2006) presents an optmzaton model to schedule a preventve mantenance of a real power plant over a plannng horzon. He consders the total 5
14 cost of varous operatons as the obectve functon and uses Bender s decomposton to solve a mxednteger lnear programmng model. Buda et al (2006) present two mxednteger lnear programmng models for preventve mantenance schedulng problems. The authors assume the total cost ncludng possesson costs mantenance costs and the penalty costs of early consecutve mantenance actvtes as the obectve functon for both models. They present and prove a theorem about the NPhard structure of the preventve mantenance schedulng problem and use GAMS to mplement the optmzaton models. They use CPLEX as the optmzaton software to fnd the optmal preventve mantenance schedule. They apply ther model to a case study of ralway mantenance schedulng. In addton they develop four heurstc optmzaton algorthms two for each model and compare the computatonal results obtaned from exact algorthms n CPLEX wth the results acheved from heurstc algorthms and menton the advantages of each soluton methodology. Another excellent study n ths area s by Tam et al (2006) who develop three nonlnear optmzaton models: one that mnmzes total cost subect to satsfyng a requred relablty one that maxmzes relablty at a gven budget and one that mnmzes the expected total cost ncludng expected breakdown outages cost and mantenance cost. They utlze MSExcel Solver as the optmzaton software that uses a generalzed reduced gradent (GRG) algorthm to solve the nonlnear optmzaton models. Usng these models they determne the optmal mantenance ntervals for a multcomponent system but ther models consder only mantenance actons for components and do not consder replacement actons. Alardh et al (2007) present a bnary nteger lnear programmng model n order to fnd the best preventve mantenance schedule n separated and lnked cogeneraton plants. The 6
15 researchers defne the avalablty of the power and desaltng equpments as the obectve functon to be maxmzed and consder the mantenance tme wndow mantenance completon duraton logcal operatonal resource lmtaton mantenance crew avalablty effcency measures and demand as the set of constrants. They apply ther model n two cogeneraton plants wth seven unts and 42 peces of equpment n Kuwat over a 52week plannng horzon and utlze LINGO as the optmzaton software to optmze the model. In addton they perform a senstvty analyss on the model to assess the robustness and analyze the effect of expandng the plannng horzon reducng the resources and ncreasng the demand on the mantenance strateges. Panagotdou and Tagaras (2007) develop an optmzaton model that optmzes the preventve mantenance schedules n a manufacturng process. The authors consder two dfferent states for components ncontrol or outofcontrol before complete falure. They treat the tme to shft and the tme to falure as random varables and express them wth Webull and Gamma dstrbutons. In addton they combne agebased and condtonbased concepts nto the optmzaton model wth the mnmzaton of total cost and solve t by applyng KarushKahnTucker (KKT) condtons of optmalty to obtan the optmal preventve mantenance schedule. Fnally they present several numercal examples to demonstrate the effectveness of ther methodology. Shrmohammad et al (2007) develop an agebased nonlnear optmzaton model to determne the optmal preventve mantenance schedule for a sngle component system. They defne two types of decson varables the tme between preventve replacements and the cutoff age and assume an expected cost of falures mantenance replacement costs and total cycle cost n the cost functon and consder cost per unt tme as the obectve 7
16 functon. In order to solve the optmzaton model and show the effectveness of the proposed approach they utlze MAPLE and run the program for a numercal example by settng dfferent values for an mprovement factor whch s assumed as a constant n the model. Dynamc programmng has been broadly used as a standard optmzaton technque to acheve the optmal mantenance and replacement actons n engneerng problems. Canfeld (986) studes preventve mantenance optmzaton models va focusng on dfferent aspects of falure functon on systems relablty. He mentons that preventve mantenance actons do not change or affect deteroraton behavor of falure rate so the developed falure functon s constant wth mantenance actons. He consders ncreasng falure rate based on the Webull dstrbuton for hs study and determnes the optmal cost of mantenance polces by defnng the average costrate of system operaton and applyng dynamc programmng as the soluton approach. Robeln and Madanat (2006) develop a mantenance optmzaton model for brdge decks va a Markov chan process. In ths paper they classfy optmzaton models nto two categores () physcally based deteroraton models wth lmted number of decson varables and (2) smpler deteroraton models wth more and sophstcated decson varables. They apply Markov chan methodology wth states based on hstory of deteroraton and mantenance actons and utlze dynamc programmng as the soluton approach to solve Markov decson process. As a case study they apply ther approach to optmze the mantenance polcy of brdges. 8
17 Heurstcs and MetaHeurstcs Algorthms Genetc algorthm as a maor optmzaton approach has been presented n several research papers. Usher et al (998) present an optmzaton mantenance and replacement model for a snglecomponent system. They determned an optmal preventve mantenance schedule for a new system subect to deteroraton by consderng the tme value of money n all future costs ncreasng rate of occurrence of falure over tme and the use of the mprovement factor to provde for the case of mperfect mantenance actons. In addton they provde a comparson of computatonal results among random search genetc algorthm and branch and bound algorthms. One of the most notable studes n the area of relablty and mantenance optmzaton for multstate multcomponent systems s found n Levetn and Lsnansk (2000). They defne a multstate system as a system n whch all or some of components have dfferent performance levels from proper functonng to complete falure and the relablty of the system as ts ablty of satsfyng the demand levels. They formulate an optmzaton model to determne preventve mantenance actons that affect the effectve age of components. Ther model s based on mnmzaton of cost subect to requred level of relablty. They apply a unversal generatng functon technque and use a genetc algorthm to determne the best mantenance strategy. Levetn and Lsnansk (2000) present addtonal research n whch an optmzaton model was developed n order to determne the optmal replacement schedulng n multstate seresparallel systems. They consdered an ncreasng falure rate based on the expected number of falures durng tme ntervals and defned summaton of mantenance actvtes cost along 9
18 wth cost of unsuppled demand due to falures of components as the obectve functon. Fnally they utlzed unversal generatng functon approach and appled genetc algorthm to fnd the optmal mantenance polcy. Wang and Handschn (2000) develop a new genetc algorthm by modfyng the basc operators crossover and mutaton of a standard genetc algorthm based on the specfc characterstc of preventve mantenance schedulng problem for power systems. They mprove the tme computatonal complexty of genetc algorthm by consderng a codespecfc and constranttransparent ntegrated codng method to acheve faster convergence and to prevent producton of nfeasble solutons. As the mplementaton methodology an obect orented programmng approach s appled and the effectveness of the new genetc algorthm shown va theoretcal analyss and smulaton results to compare wth a tradtonal genetc algorthm. Tsa et al (200) consder two actvtes mperfect mantenance and replacement n ther preventve mantenance optmzaton model. They model mperfect mantenance actvtes based on the concept of an mprovement factor whch s determned by a quanttatve assessment procedure. They use a genetc algorthm to fnd the optmal preventve mantenance actvtes whle the system untcost lfe s consdered as the obectve functon. As a case study they test a mechatronc system to show the effectveness of ther model and algorthm. Cavory et al (200) present an optmzaton model to schedule the best preventve mantenance tasks of all machnes n a sngle product manufacturng producton lne. They assume that each machne should be assgned to each operator and consdered the total throughput of the lne as the obectve functon to be maxmzed. At the frst step they formulate the optmzaton model and analyze t va analytcal approach. Then the researchers used C++ as a programmng 0
19 envronment and appled genetc algorthm n order to fnd the best combnaton of preventve mantenance tasks. In addton they construct an expermental desgn to set and analyze the parameters of genetc algorthm and utlz the Taguch method and statstcal analyss to valdate the results. Fnally an applcaton of the approach was performed n an actual producton lne of car engnes. Leou (2003) presents an optmzaton model to fnd the optmal preventve mantenance schedule for a multcomponent system. He consders total cost of operatons and mantenance actvtes along wth relablty as the crtera of the system and transfer them nto the obectve functon by defnng degree of volaton from requred relablty. In addton he defnes mantenance crew and duraton of mantenance as the system s constrants. He apples hs optmzaton model n a case study wth sx electrc generators and utlzes genetc algorthm as the optmzaton methodology to determne the best preventve mantenance schedule. Han et al (2003) consder the recursve nature of falure rate between preventve mantenance cycles and develop a nonlnear optmzaton model based on repar cost preventve mantenance cost and producton loss cost n a producton system. They apply a genetc algorthm as the optmzaton technque and menton that ther model can be consdered n decson support systems for mantenance and ob shop schedulng. Brs et al (2003) consder cost and avalablty as the systems crtera n ther research. They optmze a model ncludng cost n the obectve functon and avalablty as the constrant by usng a genetc algorthm to fnd the best preventve mantenance schedule. They use a tmedependent Brnbaum mportance factor to generate the ordered sequence of frst nspecton tmes and utlze MATLAB to calculate the system avalablty va a Monte Carlo smulaton approach.
20 Lmbourg and Kochs (2006) propose several technques to represent the decson varables n preventve mantenance schedulng models that use heurstcs and metaheurstcs optmzaton algorthms. They test varous nonstandard approaches and compare them to bnary representatons by a heurstc algorthm and the computatonal results show that effectveness of ther approaches. In addton they apply some modfed crossover and mutaton procedures n a genetc algorthm and show the mprovement n performance of ther algorthm n terms of computatonal tme and accuracy. Other research on the applcaton of genetc algorthms to mantenance optmzaton has been recently done by Lapa et al (2006). They consder flexble ntervals between mantenance actons and menton the advantage of ths assumpton over the common methodologes of contnuous fttng of the schedules. They develop a model that ncludes preventve and correctve mantenance actons and the assocated cost wth them outage tmes relablty of the system and probablty of mperfect mantenance. Because ther model s a nonlnear largescale optmzaton model they utlze a genetc algorthm as the soluton procedure. In addton and as a case study they apply ther model to a hghpressure necton system to measure the effectveness of ther methodology. Verma and Ramesh (2007) group systems and subsystems of a large engneerng plant nto hgher modular assembles (HMA) and apply a multobectve preventve mantenance schedulng method. They model ths problem as a constraned nonlnear multobectve mathematcal program wth relablty cost and nonconcurrence of mantenance perods and mantenance start tme factor as elements of the obectve functons and use a genetc algorthm to solve the model. Shum and Gong (2007) recently present an applcaton of a genetc algorthm for optmzaton of preventve mantenance schedulng of a producton machne. They consder 2
21 mantenance and replacement frequency along wth purchasng strategy and the sze of the mantenance workforce as the decson varables and the total cost as the obectve functon. They examne the effect of these costs on the optmal mantenance schedule n numercal example. Other metaheurstcs have been used as the combnatoral optmzaton technques to solve mantenance schedulng problems. Samrout et al (2005) use an ant colony algorthm to optmze the problem that was prevously optmzed va genetc algorthm. They defne seres of component mantenance and nspecton perods and use MATLAB as the programmng envronment Hybrd Algorthms Km et al (994) combne genetc algorthm wth smulated annealng n order to optmze a largescale and longterm preventve mantenance and replacement schedulng problem. In ther research the acceptance probablty of a smulated annealng method s consdered as a measure for ndvdual survval n the genetc algorthm. By usng ths approach they acheve a near optmal soluton n a short perod of tme compare to the computatonal tme of smple genetc algorthm. As a case study they optmze a longterm mantenance schedulng problem of a thermal system. Tan and Kramer (997) develop a general framework for preventve mantenance optmzaton n chemcal process operatons. They assume a Webull model for falure rate and consder dfferent mantenance actvtes that can be performed. They develop a methodology that combnes Monte Carlo smulaton wth a genetc algorthm to solve opportunstc mantenance problems wth a nondetermnstc obectve functon. They apply ther approach to two case studes to 3
22 compare the results obtaned from the proposed model wth the results acheved from analytc approach and Monte Carlo smulaton wth a neural network. Fnally they menton the advantages of ther approach over other approaches. Marseguerra et al (2002) develop a condtonbased mantenance (CBM) model for multcomponent systems and use a Monte Carlo smulaton model to predct the degradaton level n a contnuously montored system. They apply a genetc algorthm to optmze the degradaton level after mantenance actons n a multobectve optmzaton model wth proft and avalablty as the obectve functons. In addton they consder the smulaton model to descrbe the dynamcs of a stressdependent degradaton process n loadsharng components. Based on the computatonal results they menton that the combnaton of a genetc algorthm wth Monte Carlo smulaton s an effectve approach to solve the combnatoral optmzaton problems. Shalaby et al (2004) develop an optmzaton model for preventve mantenance schedulng of multcomponent and multstate systems. They defne sequence of preventve mantenance actvtes as the decson varables and the summaton of preventve mantenance mnmal repar and downtme costs as the obectve functon. In addton they consder system relablty mnmum ntervals between mantenance actons and crew avalablty as the constrants of ther model. Fnally a combnaton of genetc algorthm and smulaton was utlzed to optmze the model. Allaou and Artba (2004) present a combnaton of smulaton and optmzaton models n order to solve the NPhard hybrd flow shop schedulng problem wth mantenance constrants and multple obectve functons based on flow tme and due date. In addton they consder setup tmes cleanng tmes and transportaton tmes n the model and menton that the performance of the algorthm can be 4
23 affected by the number of the breakdown tmes. Fnally they prove that the effectveness of the smulated annealng algorthm s better than other heurstc algorthms wth the same condtons. Suresh and Kumarappan (2006) develop an optmzaton model and use a combnaton of genetc algorthm wth smulated annealng. The authors apply ther method to determne the preventve mantenance schedule n a power system. They menton that the method could produce better solutons f some changes and modfcaton are made to the soluton procedure. As a case study they test the method on 62unt state electrcal system of Vctora. Samrout et al (2006) present another paper about the combnaton of an ant colony algorthm and genetc algorthm to optmze a largescale preventve mantenance problem. They dvde the obectve functon of ther problem nto two sectons and then utlze each algorthm to mprove the sectons separately. They menton that usng hybrd algorthm n a largescale problem s more effcent than the smple algorthm MultObectve Algorthms Multobectve preventve mantenance optmzaton models have been presented n several papers. Kral and Petrovc (995) present a novel approach n preventve mantenance schedulng of thermal generatng systems. The authors develop a largescale multobectve combnatoral optmzaton model wth three obectve functons and a set of the constrants. They consder mnmzaton of total fuel costs maxmzaton of relablty n term of expected unserved energy and mnmzaton of technologcal concerns as the obectve functons. In addton they defne mantenance duraton mantenance contnuty mantenance season mantenance 5
24 sequence of thermal unts of the same class lmtaton on smultaneous mantenance of thermal unts and lmtaton on total capacty on mantenance due to labor and resources as the constrants. They develop a multobectve preventve mantenance schedulng software based on a multobectve branch and bound algorthm mplemented n FORTRAN. Fnally the researchers apply ther methodology to a real system of 8 power plants wth 2 thermal unts wth mantenance classes over 3 weeks as the plannng horzon. Chareonsuk et al (997) develop a multcrtera preventve mantenance optmzaton model to fnd the optmal preventve mantenance ntervals of components n a producton system. In ths study the authors consder an agebased falure rate for components by fttng a Webull dstrbuton to the data and defne expected total cost per unt tme and the relablty of the producton system as the man crtera. In followng they utlze a preference rankng organzaton method for enrchment evaluatons (PROMETHEE) as the soluton approach and defne the alternatve decsons as the preventve mantenance ntervals. By usng ths approach they can aggregate preferences of alternatves by combnng the weghted values of the preference functons of the complete set of crtera. As a case study they apply ther methodology n a paper factory and used PROMCALC as the optmzaton software. Fnally they menton the advantage of ther approach n whch decson makers and managers can nput varous crtera nto the model and do senstvty analyss on the optmal solutons. Konak et al (2006) present a comprehensve study on multobectve genetc algorthms and ther applcatons n relablty optmzaton problems. They revew 55 research papers and demonstrate the recent technques and methodologes. Quan et al (2007) develop a novel multobectve genetc algorthm n order to optmze 6
25 preventve mantenance schedule problems. They defne the problem as a multobectve optmzaton problem by consderng the mnmzaton of workforce dle tme and the mnmzaton of mantenance tme and menton that there s a tradeoff between the obectve functons. As the soluton procedure they use utlty theory nstead of domnancebased Pareto search to determne the nonnferor solutons and show the advantage of ths method va numercal example. Taboada et al (2008) present a recent study n ths area. They develop a multobectve genetc algorthm n order to solve multstate relablty desgn problems. The authors utlze the unversal moment generatng functon to measure the relablty and avalablty crtera n the system. They appled ther approach nto two examples; the frst one s a system of fve unts connected n seres n whch each component has two states functonng properly or falure and the second one s a system of three unts connected n seres. In ths system each component has mult states wth dfferent levels of performance whch range from maxmum capacty to total falure. They utlzed MATLAB as the programmng envronment and shown the effectveness of ther approach n terms of computatonal tmes and obtaned nonnferor solutons Smulaton Models Monte Carlo Smulaton Bottaz et al (992) present the results of a systematc collecton of actual falure tmes and preventve and correctve mantenance actvtes of 900 buses over a perod of fve years. They create an updatable database to estmate the falure dstrbutons and to evaluate the nfluence of systematc preventve and correctve mantenance actons. They consder the total cost and avalablty as the obectve 7
26 functons apply Monte Carlo smulaton approach to evaluate and compare dfferent mantenance polces and present the computatonal results. Bllnton and Pan (2000) develop a model whch s based on the use of Monte Carlo smulaton to determne the total falure frequency and the optmum mantenance nterval for a parallelredundant system. The authors present a modfed dstrbuton functon assumng an exponental dstrbuton for component useful lfe perod and the Webull dstrbuton for the wear out perod. The procedure ncludes constructon of a mathematcal model and defnton of the stoppng rule n smulaton for a parallelredundant system. They state that f the shape parameter β of the Webull dstrbuton ncreases the optmum mantenance nterval decreases. Fnally they show that a twocomponent parallelredundant system s a bass structure n mnmal cut set analyss that s used n evaluaton of power systems relablty. Zhou et al (2005) present an approach for sequental preventve mantenance schedulng based on the concept of age reducton due to mperfect mantenance actons. They consder an assumpton for the tme of mperfect mantenance actons based on requred relablty of the system. They utlze a hybrd recursve method based on an assumed mprovement factor and ncreasng falure rate and develop an optmzaton model wth a mantenance cost rate n the lfe cycle of the system as the obectve functon. Fnally they apply Monte Carlo smulaton and descrbe how ther computatonal results can be used n decson support systems for mantenance schedulng. Marquez et al (2006) develop a smulaton model to fnd the best preventve mantenance strategy n semconductor manufacturng plants. The authors model the age of equpment avalablty of equpment mantenance actvty backlog and preventve mantenance polces and consder dfferent wafer 8
27 producton scenaros n a Monte Carlo contnuous tme smulaton model. They analyze and compare the dfferent mantenance strateges on the status of manufacturng equpments and operatng condtons of the wafer producton flow. Furthermore they descrbe how the combnaton of age and avalabltybased models ncreases the throughput and provdes better results than the smple agebased models DscreteEvent and Contnuous Smulaton Goel et al (973) present a smulaton model and develop a statstcal analyss that consders three dfferent types of preventve mantenance actvtes for components by defnng stochastc and determnstc decson varables as well as unavalablty and cost as the obectves. In addton they make a 2level sequental fractonal factoral desgn n order to facltate ther smulaton. By desgnng the smulaton model based on expermental desgn approach ther model produces the preventve mantenance schedule for ground electroncs systems. Burton et al (989) develop a smulaton model to evaluate the performance of a ob shop. In ths research the effectveness of the preventve mantenance schedulng under dfferent condtons such as shop load ob sequencng rule mantenance capacty and strategy s determned and presented. Krshnan (992) develops a smulaton model to determne the mantenance schedule for an automated producton lne n a steel rollng mll plant. He consders three dfferent mantenance polces as opportunstc falure and block wth the percent of avalablty as the obectve functon. He shows that the exstng mantenance polcy only ncludes the falure and block mantenance actons. By 9
28 usng the hstorcal data of mantenance actvtes n the smulaton model the optmal preventve mantenance schedule s obtaned n the form of checklst. Mathew and Raendran (993) present a smulaton model n order to determne the frequency of the shutdown for perodc system overhaul preventve and correctve mantenance and nspectons n a sugar manufacturng plant. They utlze a tmedependent smulaton model to mnmze the total cost ncludng mantenance costs and downtme losses. Paz et al (994) develop a twostage knowledge base for a mantenance supervsor assstant system. Ths knowledge base nteracts wth the mantenance manger on a perodc bass to select the proper preventve mantenance plan for the next perod. The frst stage deals wth an obectorented computer smulaton model to montor dfferent preventve mantenance schedules that nclude preventve mantenance polces staffng polces downtme costs smultaneous downtme practces travel tme mpacts and blockng stuatons as the systems specfcatons. In addton they consder overall machne avalablty crtcal machne avalablty worker utlzaton cost of the mantenance actvtes and work order completon tme as the systems crtera. At the second stage they make a knowledge engneerng envronment to use the computatonal results obtaned from a smulaton model and send feedback to the frst stage. Joe (997) develops a smulaton model n order to evaluate dfferent preventve mantenance strateges for a fleet of vehcles of the St. Lous metropoltan polce department. He utlzes GPSS as the smulaton software analyzes several polces to mprove the effectveness and effcency of operatons and presents the best polcy. Savar (997) develops a smulaton model n order to nvestgate effect of dfferent preventve mantenance strateges n a ustntme producton system. He 20
29 constructs a smulaton model on a 5staton producton system and consders throughput rate average equpment utlzatons and total worknprocess as the performance measures of the producton system. After runnng the smulaton model and analyzng the computatonal results he mentons that preventve mantenance and correctve mantenance polces have a hgh mpact on the performance measures of ustntme producton systems and by combnng the mantenance actvtes and ustntme operatons one can mprove the effectveness of the ths knd of systems. MohamedSalah et al (999) develop a smulaton model n order to acheve opportunstc mantenance strateges n a multcomponent producton lne. The authors consder two dfferent strateges and defne total cost as the functon of preventve and correctve mantenance actvtes as well as fxed cost due to any stop or falure n producton lne. The frst strategy assumes that the mantenance actvtes are allowed on all nonfaled components f the dfference between the expected preventve tme of nonfaled components and the falure nstant of faled components s less than certan value. The second one consders that the mantenance actvtes are allowed on all nonfaled components f the dfference between the expected preventve tme of nonfaled components and the preventve tme or correctve nstant of faled components s less than certan value. They utlze PROMODEL and descrbe that the total cost functon has a unque optmum. Fnally they express that the optmal nterval of mantenance for the strateges s 5.5 and 3.5 days respectvely. Greasley (2000) presents a smulaton model to fnd the optmal mantenance plannng n tran mantenance depot for an underground transportaton faclty n UK. He develops a smulaton based on two dfferent stuatons. The frst stuaton assumes there s no random arrval and the second one consders random arrvals 2
30 and nvestgates the effect of the arrval on servce level performance measures. He utlzes ARENA as the smulaton software and shows the effectveness of the mantenance polces obtaned by the smulaton model. Chan (200) presents a smulaton model to analyze the effects of preventve mantenance polces on buffer sze nventory sortng rules and process nterruptons n a flow lne of a push producton system. He presents the performance of the producton system under dfferent operatonal condtons and preventve mantenance polces. Duffuaa et al (200) present a generc conceptual smulaton model for mantenance systems. They defne ths smulaton model by constructng seven modules ncludng an nput module mantenance load module plannng and schedulng module materals and spares module tools and equpment module qualty module and fnally a performance measure module. The authors menton that ths model could be used to develop a dscrete event smulaton models n one of the commercal smulaton softwares. In addton they suggest that by usng ths model one can evaluate the need for contract mantenance and effect of avalablty of spare parts on performance measures n the system. Devulapall et al (2002) develop a smulaton model n order to determne the best preventve mantenance polces for brdge management systems (BMS). They utlze STROBOSCOPE as the smulaton software and examne the condtons of brdges under dfferent strateges. They apply ther model to a set of brdges n Vrgna and argue that the model can be used to provde varous mantenance polces for a brdge management system. Alfares (2002) presents a smulaton model to obtan the preventve mantenance schedule for components of a detergentpackng lne and consders two dfferent stuatons n hs model. The frst one assumes a constant tme nterval that s not 22
31 affected by mantenance actons or unexpected falures. In the second stuaton the tme nterval s affected and restarted by mantenance actons or unexpected falures. In order to mnmze the total cost he develops a smulaton model to optmze the mantenance schedule of components for each stuaton. Houshyar et al (2003) present a smulaton model to measure the mpact of preventve mantenance schedulng on the producton rate of a machne. They utlze PROMODEL to make the smulaton model and consder two dfferent scenaros for the smulaton run. They use statstcal analyss on the smulaton outputs n order to determne the mpact of recommended yearly preventve mantenance on the producton throughput of the machne. Fnally they menton that the preventve mantenance polcy does not affect the producton rate but can reduce yearly mantenance costs of the system. Han et al (2004) develop a fnte tme horzon model to acheve preventve mantenance schedulng of manufacturng equpment based on setback based resdual factors and use smulaton to solve the model. They menton the consstency of computatonal results and shown that smulaton s a useful and effectve method to solve such models. Jn et al (2006) develop a preventve mantenance optmzaton model for a multcomponent producton process. They defne a combnaton of mechancal servce repar and replacement actvtes for each component and use Markov decson process to present the transton functon of probablty for mantenance actvtes. In addton they consder requred relablty of the system as the constrant and total preventve mantenance cost as the obectve functon of the model. A smulaton approach was utlzed to fnd the optmal schedule as the soluton procedure. The authors descrbe that consderng the combnaton of 23
32 preventve mantenance actvtes can reduce more cost n comparson wth the stuaton that dfferent actvtes are consdered separately. One of the most recent studes on applcaton of smulaton n preventve mantenance schedulng s presented by Hagmark and Vrtanen (2007). They develop a smulaton model to determne the level of relablty avalablty and correctve and preventve mantenance at the early stage of desgn. Ther method consders repar tme delays and effect of preventve mantenance on the system s falure observed by condton montorng and dagnostc resources. Yn et al (2007) recently propose a smulaton model n order to analyze the dynamc structure of mantenance systems. The researchers consder varous subsystems such as preventve mantenance subsystem defects subsystem condtonbased subsystem falure subsystem correctve mantenance subsystem and performance subsystem and utlzed SIMULINK to buld up the model. They analyze the structure of components and the relaton of ther constrants n a mantenance system and present the advantages of the model over classcal stochastc process methods n a numercal example. In addton they menton that obtaned smulaton results express the dynamc nature of mantenance systems Age Reducton and Improvement Factor Models Nakagawa (988) presents a basc and notable approach for models that utlze mprovement factor. The work has been referenced by many researchers. He develops two analytcal models n order to fnd the optmal preventve mantenance schedule based on an assumpton of ncreasng falure rate over tme. The frst model called a preventve mantenance hazard rate model calculates the average 24
33 falure cost of mnmal repars along wth costs of preventve mantenance and replacement under the assumpton that preventve mantenance actons reduce the next effectve age to zero the falure rate s assumed to ncrease wth the ncreasng the frequency of preventve mantenance actons. Furthermore ths model assumes that mantenance actvtes take place at fxed ntervals between each predetermned replacement. The second model called an age reducton preventve mantenance model consders the average falure cost of mnmal repars as well as costs of preventve mantenance and replacement by assumng the age reducton after each mnmal repar. In order to fnd the optmal schedule both models are optmzed by calculus methods. He apples the models n a numercal example and descrbes that based on obtaned computatonal results the second model s more practcal than the frst model. Jayabalan and Chaudhur (992) propose another referenced work on age reducton and mprovement factors models. They develop an optmzaton model and a branchng algorthm that mnmzes the total cost of preventve mantenance and replacement actvtes. They assume a constant mprovement factor and defne a requred falure rate. In addton they assume a zero falure cost and do not consder tme value of money for future costs. Ther algorthm determnes the optmal schedule of mantenance actons before each replacement acton n order to mnmze the total cost n a plannng horzon. They utlze FORTRAN to mplement the algorthm and prove the effectveness of the algorthm va several numercal examples. Dedopoulos and Smeers (998) develop a nonlnear optmzaton model to fnd the best preventve mantenance schedule by consderng the degree of age reducton as the varable n the model. The researchers defne mprovement factor tme and 25
34 duraton of preventve mantenance actvtes as the decson varables consder fxed cost and varable cost for mantenance actons and defne the varable cost as a functon of the degree of age reducton the duraton of the acton and the effectve age of the component. Moreover they present the falure rate n each perod as a recursve functon of age reducton from a prevous perod and consder the net proft as the obectve functon of the model. They mplement the model n GAMS and use GAMS/MINOS optmzaton software. Fnally the effectveness of the model s shown va three numercal examples. Martorell et al (999) present an agedependent preventve mantenance model based on the survellance parameters mprovement factor and envronmental and operatonal condtons of the equpment n a nuclear power plant. They consder rsk and cost as the crtera of the model based on the age of the system and made the senstvty analyss to show the effect of the parameters on the preventve mantenance polces. They express that the results obtaned from ther model are dfferent from those resulted from the models that do not consder the mprovement factor and workng condtons. Ln et al (200) combne the models were developed by Nakagawa (988) and present hybrd models n whch effects of each preventve mantenance acton are consdered by two aspects; one for ts mmedate effects and the other one for the lastng effects when the equpment s put to use agan. The authors construct two models that reflect the concept of mantanable and nonmantanable falure modes. In the frst model they assume that preventve mantenance and replacement tme are ndependent decson varables and consder the mean cost rate as the obectve functon to be mnmzed. In the second model they assume that preventve mantenance actvtes are performed whenever the falure rate of the system exceeds the certan level and lke the frst model the mean cost rate s consdered as 26
35 the obectve functon. Fnally they present numercal examples to show the applcaton of the developed models and menton that for a system wth a Webull lfe dstrbuton optmal schedules can be acheved analytcally but for the general case t cannot be solved by analytc methods. X et al (2005) develop a sequental preventve mantenance optmzaton model over a fnte plannng horzon. They defne a recursve hybrd falure rate based on the mprovement factor and ncreasng falure rate n order to estmate the systems relablty n each perod of plannng horzon. In addton they consder the total cost of preventve mantenance actvtes and assume that mean cost n each perod s a functon of requred relablty and the mprovement factor. Fnally they utlze a smulaton approach to optmze the model and menton that the computatonal results can be used n a mantenance decson support system of ob shop schedulng. Jaturonnatee et al (2006) develop an analytcal model n order to fnd the optmal preventve mantenance schedule of leased equpment by mnmzng a total cost functon. They defne mantenance actons as preventve and correctve each wth assocated costs and then consder the concept of reducton n falure ntensty functon along wth penalty costs due to volaton of leased contact ssues. They present a numercal example for a system wth Webull falure rate solve the model analytcally and examne the effect of penalty terms on the optmal preventve mantenance polces. BartholomewBggs et al (2006) present several preventve mantenance schedulng models that consder the effect of mperfect mantenance on effectve age of component. The researchers develop optmzaton models that mnmze the total cost of preventve mantenance and replacement actvtes. In ths study they assume a known falure rate to express the expected falures as a functon of age and consder age reducton n the effectve age based on the concept 27
36 of an mprovement factor. They develop a new mathematcal programmng formulaton to acheve the optmal mantenance schedule and utlze automatc dfferentaton as the numercal approach nstead of analytcal approach to compute the gradents and hessans n the optmzaton procedure whch s the global mnmzaton of nonsmooth performance functon. Fnally the effectveness of the presented model and algorthm s shown n several numercal examples. ElFerk and BenDaya (2006) present an agebased hybrd model for mperfect preventve mantenance. The authors revew dfferent polces and the models developed by other researchers and propose a new sequental agebased analytcal model. They assume that the mperfect preventve mantenance actvtes reduce the effectve age of the system but ncrease the falure rate and presented mathematcal formulatons to determne the adustment factors for both falure rate and age reducton coeffcent. They construct an optmzaton model based on ther analytcal models consder the mnmzaton of the total cost as the obectve functon and solve the optmzaton model va a new heurstc algorthm for a numercal example. One of the recent works on methods for estmatng age reducton factor s by Che Hua (2007). In ths research he consders an optmal preventve mantenance for a deteroratng onecomponent system va mnmzng the expected cost over a fnte plannng horzon. He develops a model for estmatng mprovement factor to measure the restoraton of component under the mnmal repar. The proposed mprovement factor s a functon of effectve age of component the number of preventve mantenance actons and the cost rato of each mantenance acton to the replacement acton. Fnally the researcher could obtan the optmal preventve mantenance schedule for a case study wth the Webull hazard functon by applyng a partcle swarm optmzaton method. 28
37 Cheng et al (2007) present a paper about models for estmatng the degradaton rate of the age reducton factor. They present two optmzaton models whch mnmze the cost subect to requred relablty. The frst model has a perodc preventve mantenance tme nterval for every replacement and the second one contans the mantenance schedule where the tme nterval between the fnal mantenance and replacement s not constant. Lm and Park (2007) present three analytcal preventve mantenance models that consder the expected cost rate per unt tme as the obectve functon. In ths research they assume that each preventve mantenance actvty reduces the startng effectve age but does not change the falure rate and consder the mprovement factor as the functon of number of preventve mantenance actvtes. They also assume that the falure functon s based on a Webull dstrbuton and develop mathematcal formulaton for three dfferent stuatons; preventve mantenance perod s known number of preventve mantenance s known and number and perod of preventve mantenance s unknown. They derve the optmal preventve mantenance and replacement schedules by takng an analytcal approach and apply them to a numercal example to show an applcaton of ther models Applcatons Manufacturng and Producton Systems The applcaton of preventve mantenance schedulng has been wdely used n manufacturng and producton systems. For example Hsu (99) develops an optmzaton model n order to determne the optmal preventve mantenance schedules for a seral multstaton manufacturng system. He mentons that most of 29
38 models use smulaton at that tme but hs model s focused on mathematcal programmng approach. The computatonal results of hs study show that operatng features of the statons are nterrelated and one must nvestgate the effect of preventve mantenance actvtes on all statons at the same tme. Cassady et al (999) develop an ntegrated control chart and preventve mantenance schedulng to reduce the total operatng cost of manufacturng systems. The researchers formulate an economc model that ncludes the product nspecton costs process downtme costs and poor qualty costs and analyze t va a smulaton model. In addton they construct a smulatonoptmzaton model n order to evaluate and optmze the parameters of control chart and preventve mantenance strategy. They demonstrate ther approach n a numercal example and shown the feasblty and effectveness of ther methodology. Westman and Hanson (2000) develop a model to determne the mean tme to falure (MTTF) as a functon of the uptme for a workstaton n a multstage manufacturng system. The authors assume that the uptme of the workstaton has an ncreasng rate and s reduced f preventve mantenance actons are performed. They menton that ths methodology captures the flexblty and multstage propertes of manufacturng systems and can generate the preventve mantenance polces. Westman et al (200) formulate a mathematcal model to fnd the optmal producton schedulng va lnear quadratc Gaussan Posson functon wth state dependent Posson process. They consder the total cost of producton and mantenance polces as the obectve functon and demonstrate the applcaton of the model by a numercal example. Charles et al (2003) present a preventve mantenance optmzaton model n order to mnmze the total mantenance costs n a producton system. In ths paper 30
39 they consder the total productve mantenance correctve mantenance and preventve mantenance actons along wth producton operatons as well as the related assocated costs. They assume a Webull lfe dstrbuton and utlze MELISSA C++ as dscreteevent productonorented smulaton software to evaluate dfferent scenaros. As a case study they analyze a prototype semconductor manufacturng workshop to demonstrate the approach and mentoned that ths model has general structure that can be appled for other knd of manufacturng systems. Han et al (2004) develop a nonlnear optmzaton model to mnmze the cost of mantenance and replacement actons under the relablty constrants for producton machne n a producton system. Ther model consders Webull dstrbuton as the falure functon of the machne and can be used as a decson support system for ob shop schedulng. Sawhney et al (2004) present a smulaton model to determne mantenance strateges of a manufacturng system. Ther model s constructed for ntegratng reactve and proactve mantenance schedulng n order to ncrease productvty of operatons n the lean manufacturng structure. Preventve mantenance optmzaton s also used n semconductor manufacturng. L and Qan (2005) present a real tme preventve mantenance optmzaton model for cluster tools n a semconductor manufacturng system. They consder the standpont of the system and used genetc algorthm as the soluton procedure. In the area of applcaton of preventve mantenance n manufacturng and producton systems many researchers are nterested n ntegratng preventve mantenance and producton schedulng. Adzakpa et al (2004) present an applcaton of combnaton of mantenance schedulng and ob assgnment n dstrbuton systems. They develop an optmzaton model that consders the total cost of 3
40 mantenance actons as the obectve functon and avalablty n a gven tmewndow and precedence among consecutve standby obs and ther emergency as the constrants of the model. They show that ther problem s NPhard to solve and because of that they use a heurstc optmzaton algorthm to solve the problem. Yng et al (2005) develop an ntegrated model that smultaneously consders preventve mantenance and producton schedulng decson varables. Ther model mnmzes the total tardness of obs and makes a 30% reducton n expected total tardness of obs. Rezg et al (2004) present an ntegrated preventve mantenance and nventory control smulaton model for a producton lne wth multcomponent. The authors defne preventve and correctve mantenance actvtes along wth nventory control varables and parameters to develop approxmate analytcal models for the sngle machne under dfferent scenaros. In addton they utlze PROMODEL to construct an agebased smulaton model and apply genetc algorthm to optmze the parameters of the smulaton model and evaluate dfferent producton scenaros. Fnally they test ther methodology on three numercal examples of a producton lne and compare the computatonal results wth results obtaned from analytcal formulas. They menton that applyng combnaton of mantenance strateges producton plannng polces leads to a sgnfcant reducton of the total cost. Rezg et al (2005) present another paper n ths area. He and hs colleagues develop an ntegrated agebased preventve mantenance and nventory control smulaton model n a manufacturng system wth ustntme confguraton. They present two approaches; the frst one s a mathematcal model to determne the average cost per unt tme and the second one s the combnaton of smulaton and expermental desgn. They use MAPLE for solvng the analytcal model utlze PROMODEL for 32
41 smulaton and use STATGRAPHICS to analyze the data for expermental desgn and regresson analyss. The authors menton that both approaches could gve approxmately same results and exstng dfference due to approxmaton assumptons consdered n the analytcal model that was elmnated n the smulaton model. Sortrakul et al (2005) present an optmzaton model of ntegrated preventve mantenance plannng and producton schedulng for a sngle machne. The authors menton that these problems have been tackled separately n several papers but they have not been consdered together n real manufacturng systems. They consder the total weghted expected ob completon tme as the obectve functon and optmze the combnatoral optmzaton model va genetc algorthm. As the result they express the advantages and effectveness of ther approach that can be used to solve real manufacturng problems. Cassady and Kutanoglu (2005) develop and present an ntegrated preventve mantenance and producton schedulng mathematcal model for a snglemachne. They consder the total weghted expected completon tme as the obectve functon to be mnmzed. Ther model allows multple mantenance actvtes and explctly captures the rsk of not performng mantenance. They use a heurstc approach to solve the model and compare the obtaned computatonal results of ntegrated model wth the results acheved from the solvng preventve mantenance and ob schedulng problems ndependently. Leng et al (2006) present an ntegrated preventve mantenance schedulng and producton plannng multobectve optmzaton model for a sngle machne. They use chaotc partcle swarm optmzaton algorthm to solve the model and show the applcaton and effectveness va numercal examples. L and Zuo (2007) recently develop a smulaton model that determnes that mpact of preventve and correctve 33
42 mantenance actvtes on the total cost of nventores n a producton system. They apply smulaton as the soluton methodology to fnd the optmal number of falures and the optmal level of safety stock smultaneously and menton that combnng the preventve and correctve mantenance wth producton schedulng can reduce the large amount of total operatng cost n system. Kou and Chang (2007) develop an ntegrated producton and mantenance optmzaton model for a sngle machne based on cumulatve damage process and the effect of preventve mantenance polcy on the producton schedulng n order to mnmzaton of the total tardness. The authors express that n the optmal strategy f obs have certan process tme wth dfferent due dates the optmal producton schedule sorts the obs by earlest due date and f obs have certan due dates wth dfferent process tme t sorts them by shortest process tme. In addton they menton that the optmal mantenance polcy s a constrant on the producton schedule when machne shuts down due to cumulatve damage falure process. The computatonal results show that by ncreasng the number of obs the effect of obs due dates on the optmal mantenance polcy control lmt s decreased. Zhou et al (2007) demonstrate an age based preventve mantenance schedulng combned wth producton plannng optmzaton model n order to maxmze the avalablty of a producton machne. The authors use a heurstc algorthm to obtan the optmal schedule that mnmzes the make span. They also apply a smulaton approach to valdate the heurstc algorthm and to show ts effectveness to solve the flow shop schedulng problems of ntegrated producton and preventve mantenance. Ruz et al (2007) present comprehensve research n area of ntegratng preventve mantenance and producton schedulng. They defne three dfferent polces for preventve mantenance schedulng; preventve mantenance at fxed 34
43 predefned tme ntervals preventve mantenance for maxmzng the equpment avalablty and mantanng a mnmum relablty threshold over the plannng horzon. The mnmzaton of the total manufacturng tme of the sequence s consdered as the man crteron. The authors apply sx dfferent adaptatons of heurstc and metaheurstc algorthms to evaluate the last two polces for two sets of problems and menton that ant colony and genetc algorthm solve these problems effectvely. Fnally they conclude that ntegrated preventve mantenance and producton schedulng optmzaton problems along wth metaheurstc algorthms can be successfully appled n flowshop problems. In addton they suggest that one can defne more crtera and consder the problem as a multobectve optmzaton model Servce Systems Jayabalan and Chaudhur (992) present two dfferent preventve mantenance models for mantanng bus engnes n a publc transt network based on mnmzaton of the total cost over a fnte plannng horzon. They construct the models based on the concept of mean tme to falure (MTTF) of the engnes and assume the upper bound for the falure rates. The frst model s based on dfferent Webull falure functons between preventve mantenance actvtes and the second assumes that the each preventve mantenance acton reduces the effectve age of the system. The authors present the obtaned computatonal results and show the effectveness of the models n a real case study. Pongpech et al (2006) present an optmzaton model that mnmzes the total mantenance costs and penalty costs for used equpment under lease. They assume 35
44 Webull dstrbuton as the falure functon for equpment develop a 4parameter model and apply a 4stage algorthm to solve t. They apply ther model to several numercal examples wth dfferent contract assumpton and analyze the optmal polcy n each stuaton. Martn (988) presents a preventve mantenance optmzaton model whch has been developed and mplemented by Columba Hosptal n Mlwaukee based on plant technology and safety management standards. The hosptal desgned ths program n order to use the optmal preventve mantenance plan for electrcal dstrbuton equpment wth consderng safety servceablty relablty and the total cost. Fard and Nukala (2004) study and revew the applcaton of dfferent stochastc process such as homogenous Posson process (HPP) nonhomogenous Posson process (NHPP) branchng Posson process (BPP) and supermposed renewal process (SRP) n preventve mantenance schedulng. They present current methods based on nonhomogenous processes for modelng and optmzaton of sngle and multcomponent systems. They assume that mantenance actons do not affect the falure rate of system; hence the NHPP can be appled to present and model reparable servce systems Power Systems Applcatons of preventve mantenance schedulng are not restrcted to manufacturng or servce systems. Power plants use preventve mantenance strateges to ncrease the relablty and avalablty of equpments. McClymonds and Wnge (987) present methods to acheve optmal preventve mantenance 36
45 schedulng for nuclear power plants though they have not been appled successfully. They consder the plant avalablty and relablty as the obectve functons and develop models based on assgnng resources to preventve and correctve mantenance actvtes. Zhao et al (2005) present an agebased preventve mantenance optmzaton model for a gas turbne power plant. They develop a model wth proft nstead of cost as the obectve functon and consdered power plant performance relablty and the market dynamcs effects n the model. In order to determne the effects of economcs on mantenance costs and frequences they utlze a sequental approach and show ts effectveness by usng real data of based load combned cycle power plant wth a gas turbne unt Chapter Summary In ths chapter recent work pertanng to methods and applcatons of preventve mantenance and replacement schedulng were revewed. They were categorzed as optmzaton models smulaton models age reducton and mprovement factor models and applcatons n manufacturng servce and power systems. We fnd that most studes focus on snglecomponent systems or smple and specfc systems whch s not always applcable for real and general systems. In addton not much work has been done n the area of age reducton and mprovement factor models. Hence we propose preventve mantenance and replacement schedulng models that deal wth multcomponent system and can be appled to a wde varety of systems. Because we use the concept of age reducton and mprovement factor n these models we also develop mathematcal and statstcal models to estmate the 37
46 mprovement factor for mperfect mantenance actvtes. These are our research contrbutons and they are appled to a real system. 38
47 Chapter 3 Optmzaton Models  Exact Algorthms 3.. Introducton Ths chapter wll present a new modelng approach to fnd optmal preventve mantenance and replacement schedules for multcomponent systems. We construct new closedform optmzaton models based on the cost and relablty characterstcs and solve them usng a standard optmzaton procedure. These models provde a general framework that can be appled and used n a wde varety of systems. Computatonal results show the feasblty of the proposed approach Formulaton Consder a new reparable seres system of components each subect to deteroraton. Each component s assumed to have an ncreasng rate of occurrence of falure (ROCOF) v (t) where t denotes actual tme ( t > 0 ). In ths paper we assume that component falures follow the wellknown NonHomogeneous Posson Process (NHPP) wth ROCOF gven as: v ( t) λ βt β for... (3.) where λ and β are the characterstc lfe (scale) and the shape parameters of component respectvely. The NHPP s smlar to the Homogeneous Posson Process
48 (HPP) wth the excepton that the falure rate s a functon of tme. For more on ths wellknown stochastc process see Ascher and Fengold (984). We seek to establsh a schedule of future mantenance and replacement actons for each component over the perod [0 T]. The nterval [0 T] s segmented nto J dscrete ntervals each of length T/J. At the end of perod the system s ether mantaned replaced or no acton s taken. We assume that mantenance or replacement actvtes n perod reduce the effectve age of the system and thus t s ROCOF. For smplcty we also assume that these actvtes are nstantaneous.e. the tme requred to replace or mantan s neglgble relatve to the sze of the nterval and thus s assumed to be zero however we do mpose a cost assocated wth the repar or mantenance acton. To account for the nstantaneous changes n system age and system falure rate we ntroduce the followng notaton. Let X denote the effectve age of component at the start of perod and X denotes the age of component at the end of perod. It s clear that: T X X + for... ;... T (3.2) J Mantenance Consder the case where component s mantaned n perod. For smplcty we assume that the mantenance actvty occurs at the end of the perod. The mantenance acton effectvely reduces the age of component for the start of the next perod. That s: X + α X for... ;... T and (0 α ) (3.3) 40
49 The term α s an mprovement factor smlar to that proposed by Malk (979) and Jayabalan and Chaudhur (992). Ths factor allows for a varable effect of mantenance on the agng of a system. Whenα 0 the effect of mantenance s to return the system to a state of goodasnew. When α mantenance has no effect and the system remans n a state of badasold. Note that the mantenance acton at the end of perod results n an nstantaneous drop n the ROCOF of component as shown n Fgure 3.. Thus at the end of perod the ROCOF for component s v ). At the start of perod + we fnd that the ROCOF drops to v ). ( X ( X Fgure 3.. Effect of perod mantenance on component ROCOF Replacement If component s replaced at the end of perod we fnd that: X + 0 for... ;... T (3.4).e. the system s returned to a state of goodasnew. The ROCOF of component ' nstantaneously drops from v ( ) to v (0) as shown n Fgure 3.2. X 4
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