Multiple Periodic Preventive Maintenance for Used Equipment under Lease



Similar documents
How To Calculate Backup From A Backup From An Oal To A Daa

HEURISTIC ALGORITHM FOR SINGLE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON THE DYNAMIC PROGRAMMING

MULTI-WORKDAY ERGONOMIC WORKFORCE SCHEDULING WITH DAYS OFF

Capacity Planning. Operations Planning

A Model for Time Series Analysis

An Optimisation-based Approach for Integrated Water Resources Management

A binary powering Schur algorithm for computing primary matrix roots

Spline. Computer Graphics. B-splines. B-Splines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II

Pedro M. Castro Iiro Harjunkoski Ignacio E. Grossmann. Lisbon, Portugal Ladenburg, Germany Pittsburgh, USA

Lecture 40 Induction. Review Inductors Self-induction RL circuits Energy stored in a Magnetic Field

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

Cooperative Distributed Scheduling for Storage Devices in Microgrids using Dynamic KKT Multipliers and Consensus Networks

A robust optimisation approach to project scheduling and resource allocation. Elodie Adida* and Pradnya Joshi

Y2K* Stephanie Schmitt-Grohé. Rutgers Uni ersity, 75 Hamilton Street, New Brunswick, New Jersey

APPLICATION OF CHAOS THEORY TO ANALYSIS OF COMPUTER NETWORK TRAFFIC Liudvikas Kaklauskas, Leonidas Sakalauskas

Levy-Grant-Schemes in Vocational Education

ANALYSIS OF SOURCE LOCATION ALGORITHMS Part I: Overview and non-iterative methods

GUIDANCE STATEMENT ON CALCULATION METHODOLOGY

Prices of Credit Default Swaps and the Term Structure of Credit Risk

Index Mathematics Methodology

HEDGING METHODOLOGIES IN EQUITY-LINKED LIFE INSURANCE. Alexander Melnikov University of Alberta, Edmonton

Optimal maintenance of a production-inventory system with continuous repair times and idle periods

COMPETING ADVERTISING AND PRICING STRATEGIES FOR LOCATION-BASED COMMERCE

SPC-based Inventory Control Policy to Improve Supply Chain Dynamics

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM ))

The Virtual Machine Resource Allocation based on Service Features in Cloud Computing Environment

Managing gap risks in icppi for life insurance companies: a risk return cost analysis

Genetic Algorithm with Range Selection Mechanism for Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System

An Anti-spam Filter Combination Framework for Text-and-Image s through Incremental Learning

Cooperative Random Walk for Pipe Network Layout Optimization

The Rules of the Settlement Guarantee Fund. 1. These Rules, hereinafter referred to as "the Rules", define the procedures for the formation

SHIPPING ECONOMIC ANALYSIS FOR ULTRA LARGE CONTAINERSHIP

INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT

A New Method to Evaluate Equity-Linked Life Insurance

Market-Clearing Electricity Prices and Energy Uplift

A Background Layer Model for Object Tracking through Occlusion

A Heuristic Solution Method to a Stochastic Vehicle Routing Problem

Linear Extension Cube Attack on Stream Ciphers Abstract: Keywords: 1. Introduction

The US Dollar Index Futures Contract

Pavel V. Shevchenko Quantitative Risk Management. CSIRO Mathematical & Information Sciences. Bridging to Finance

(Im)possibility of Safe Exchange Mechanism Design

12/7/2011. Procedures to be Covered. Time Series Analysis Using Statgraphics Centurion. Time Series Analysis. Example #1 U.S.

Linear methods for regression and classification with functional data

An Architecture to Support Distributed Data Mining Services in E-Commerce Environments

RESOLUTION OF THE LINEAR FRACTIONAL GOAL PROGRAMMING PROBLEM

THE USE IN BANKS OF VALUE AT RISK METHOD IN MARKET RISK MANAGEMENT. Ioan TRENCA *

Social security, education, retirement and growth*

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

CLoud computing has recently emerged as a new

Trading volume and stock market volatility: evidence from emerging stock markets

Omar Shatnawi. Eks p l o a t a c j a i Ni e z a w o d n o s c Ma in t e n a n c e a n d Reliability Vo l.16, No. 4,

Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds.

Attribution Strategies and Return on Keyword Investment in Paid Search Advertising

Optimal portfolio allocation with Asian hedge funds and Asian REITs

Ground rules. Guide to the calculation methods of the FTSE Actuaries UK Gilts Index Series v1.9

Temporal and Spatial Distributed Event Correlation for Network Security

Efficiency of General Insurance in Malaysia Using Stochastic Frontier Analysis (SFA)

Insurance. By Mark Dorfman, Alexander Kling, and Jochen Russ. Abstract

Event Based Project Scheduling Using Optimized Ant Colony Algorithm Vidya Sagar Ponnam #1, Dr.N.Geethanjali #2

Selected Financial Formulae. Basic Time Value Formulae PV A FV A. FV Ad

Template-Based Reconstruction of Surface Mesh Animation from Point Cloud Animation

Portfolio Loss Distribution

MORE ON TVM, "SIX FUNCTIONS OF A DOLLAR", FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi

PerfCenter: A Methodology and Tool for Performance Analysis of Application Hosting Centers

Public Auditing for Ensuring Cloud Data Storage Security With Zero Knowledge Privacy

DESIGN OF OPTIMAL BONUS-MALUS SYSTEMS WITH A FREQUENCY AND A SEVERITY COMPONENT ON AN INDIVIDUAL BASIS IN AUTOMOBILE INSURANCE ABSTRACT KEYWORDS

Cost- and Energy-Aware Load Distribution Across Data Centers

Financial Time Series Forecasting: Comparison of Neural Networks and ARCH Models

A GENERALIZED FRAMEWORK FOR CREDIT RISK PORTFOLIO MODELS

Chapter 8: Regression with Lagged Explanatory Variables

Kalman filtering as a performance monitoring technique for a propensity scorecard

Pricing Rainbow Options

[ ] Econ4415 International trade. Trade with monopolistic competition and transportation costs

Optimization of Nurse Scheduling Problem with a Two-Stage Mathematical Programming Model

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.

Analysis of intelligent road network, paradigm shift and new applications

Morningstar Investor Return

A multi-item production lot size inventory model with cycle dependent parameters

Stochastic Optimal Control Problem for Life Insurance

A Real-time Adaptive Traffic Monitoring Approach for Multimedia Content Delivery in Wireless Environment *

A NEW ACTIVE QUEUE MANAGEMENT ALGORITHM BASED ON NEURAL NETWORKS PI. M. Yaghoubi Waskasi M. J. Yazdanpanah

Time Series. A thesis. Submitted to the. Edith Cowan University. Perth, Western Australia. David Sheung Chi Fung. In Fulfillment of the Requirements

Estimating intrinsic currency values

Global supply chain planning for pharmaceuticals

Transcription:

Mulle Perodc Prevenve Manenance or Used Equmen under ease Paarasaya Boonyaha, Jarumon Jauronnaee, Member, IAENG Absrac Ugradng acon revenve manenance are alernaves o reduce he used equmen alures rae whch have more dsruons han he new. The omal manenance olcy wll eecve o he mos alure decrease. Ths research s o deermne he omal PM acons ha mnmze he oal manenance cos or leased equmen when we consder he enales or equmen alures rears me over lm. The ormulaed models are combned rom he advanages o sequenal PM erodc PM olces. Assumon o alures orm s aled by Nonhomogeneous Posson rocess (NHPP) alures dsrbuon s arased Webull. The omal soluon s acheved rom he omal ugrade level wo merames aroach whch s obaned he omal o me nervals o carry ou PM he omal level o PM acons. Index Terms Perodc revenve manenance, Relably, ease, Used equmen. I. INTRODUTION Equmens under leasng s he one sraegy o busness managemen he mos moran o leasng busness s a relably. The used equmen should be ocused because o alure rae s hgher han he new one less relably. The ugradng acon suable PM are necessary o decrease alure rae, moreover hey can reduce he oal manenance cos. Mul erodc revenve manenance olcy or he new equmen s combnng he advanages o sequenal erodc PM olcy under lease condon whch s more lexble han mlemenaon [7. Ths olcy s aly rom sequenal PM olcy or leased equmen whch a un s revenvely mananed a unequal me nervals. Usually, he me nervals become shorer shorer as me asses, consderng ha mos uns need more requen manenance wh ncreased ages [5 erodc PM olcy or leased equmen, unle he sequenal PM olcy, whch a un s revenvely mananed a xed me nervals o T, =,,..., over he lease erod. Ths mles ha he mlemenaon o erodc PM olcy s more convenence han he sequenal PM olcy because he me nervals beween successve PM acons are consan [. The lease erod s dvded n o wo sages whch s he rs he second lease erod. Each erod have equal me nervals or PM acons, bu he requency o each PM acons wll be deren n he rs he second lease erod e.g., he rs Paarasaya Boonyaha s wh he Thammasa Unversy, Bango, Thal (corresondng auhor o rovde hone: +668-4-638; e-mal: boonyaha@homal.com). Jarumon Jauronnaee, was wh Thammasa Unversy, Bango, Thal. She s now wh he Dearmen o Indusral Engneerng, Thammasa Unversy, Thal (e-mal: arumon@engr.u.ac.h). lease erod, he PM acons are carred ou a erodc mes o T, =,,..., n he second lease erod, he PM acons are carred ou a erodc mes T /, =,,...,. Frequency o PM acons n he second erod s hgher han he rs because o ncreasng alure rae corresondngly. The searaed lease mng wll be consdered aer he equmens have been used A years ugradng wll be carred ou beore lease. Any nervenen alures over he lease erod, we wll assume o be reced hrough mnmal rears. II. MODE FORMUATION We use he ollowng noaon: F ( ) = alure dsrbuon uncon ( ) ( ) = alure densy uncon assocaed wh F ( ) r = alure rae [hazard uncon assocaed wh F ( ) λ ( ) = alure nensy uncon wh no PM [= r ( ) λ ( ) = alure nensy uncon wh PM acons Λ ( ) = cumulave alure nensy uncon wh no PM = λ ( x) dx Λ ( ) = cumulave alure nensy uncon wh PM acons = λ ( x) dx N ( ) = number o alures over [, Y = me o rear G ( y) = rear-me dsrbuon uncon g ( y) = rear-me densy uncon [= dg ( y) / dy = lease erod = s lease erod = nd lease erod T = erod o me nsan o carry ou PM = number o PM acons over he s lease erod = number o PM acons over he nd lease erod = me nsan or erod h PM acon over he s lease

= me nsan or erod h PM acon over he nd lease = reducon n nensy uncon due o acon over he s lease erod = reducon n nensy uncon due o h PM h PM acon over he nd lease erod A = age o equmen beore lease x = ugrade level beore lease (ercenage o A ) = ugrade cos u ( ) = cos o PM acon resulng n a reducon nensy uncon over he s lease erod ( ) n = cos o PM acon resulng n a reducon nensy uncon over he nd lease erod = oal cos o PM acons τ n = average cos o M acon o recy alure = oal cos o M acons = rear me lm [arameer o lease conrac n = enaly cos er un me rear no comleed whn τ [Penaly- = enaly cos er alure () > φ φ = oal cos due o Penaly- = oal cos due o Penaly- A. ease onac N [Penaly- For lease erod, aer A years, ha comose o +, when have a requency o PM acon more han, ha bene o relably ncreasng. The equmen s leased or a erod wh wo yes o enaly resulng rom alures. Penaly-: The lessor ncurs a enaly he me o rear alure exceeds τ. e Y denoe he me o rear, hen here s no enaly τ ( Y τ ) Y >τ [. Y a enaly Penaly-: The lessor ncurs a enaly cos alure ha occurs over he lease erod [. B. Modelng Falures PM Acons n or each Equmen alures are reced hrough mnmal rears he rear mes are small relave o he mean me beween alures. We assume ha equmen had been used beore new lease. Furhermore, a he rmal lease no PM needed a early hase o equmen because o low alure. As a resuls, s no worh o PM, however he lessor has rovded PM when ever equmen aled. The rs alures mng s a dsrbuon uncon F () alures rae r ( ) s an ncreasng uncon o me. For he rs erod, he lessor carres ou erodc PM wh a erod o T T / over he second lease erod o comensae ncreasng o alure rae. The me nsans o PM acons are gven by = T, =,,..., or he rs lease erod = + T /, =,,..., or he second lease erod. Each PM acon resuls n a reducon n he nensy uncon. The reducons resulng rom he h h PM n he rs erod s gven by or PM n he second erod [7. The rs erod, he alures over he lease erod or used equmen, no PM acon beore lease ha occurs accordng o he nensy uncon gven by () = λ ( A + x) λ or + () Where = = x hs reer o no ugrade case = A x. = = ( A ) s consraned as ollows = λ x ( ) A + x = λ or () The second erod, () = λ ( A + x) λ (3) or + = = = A + where x s consraned as ollows ( A + x) = = = λ (4) or ; Fg. Plo o alure nensy uncon o boh erod o. os o essor () os o M Acons e N ( ) s a number o alures over [, s a mean cos o rear. The cos o rearng alures s gven by = N( (5) () os o PM Acons The cos o PM acon deends on he reducon resul n he nensy PM cos uncon. We model hs hrough a xed cos he varable cos s gven by

( ) = a + b, =,,..., (6) or he rs lease erod ( ) = a + b, =,,..., (7) For he second erod wh a > b. Hence, he oal cos o PM acons s gven by (, ) = ( a + b ) + ( a + b ) (8) = = where =,,..., =,,..., () Ugrade oss The ugrade cos u (x) s an ncreasng uncon o x gven by ϕ ( x) ( ) /( A u x = ω x e ) (9) Where > ϕ > whenω s a scale arameer ω ϕ s a shae arameer o u (x) (x) Penaly oss Penaly- resuls rom alure o comlee a rear whn a seced me τ. e Y (a rom varable rom a dsrbuon G ( y) ) denoe he me o recy he h alure, N(. Then, he oal Penaly- cos ncurred s gven by N ( φ, Y, τ ) = max[, Y τ () = Penaly- resuls whenever here s any alure over he lease erod, he lessor ncurs he enaly- coss hs s gven by φ ) = max[, N( ) () { } n III. MODE ANAYSIS A. Execed number o alurwes The equmen alures wh no PM acons occur accordng o a Non homogeneous Posson rocess (NHPP) wh nensy uncon λ ( ) = r( ) where r() s alure rae [hazard uncon assocaed wh he dsrbuon F() we dene [3 Λ( = λ ( ) d () The execed number o alures over he lease erod, wh no PM acon, s gven by E[ N( = Λ( A + x) Λ( A x) (3) The execed number o alures over he lease erod, wh PM acon, s gven by E[ N( ( A + x) Λ ( A x) = Λ ( ) ( ) = = (4) where =,,..., =,,..., B. Execed os () Execed M cos From (5) (4), he oal execed cos o M acons s gven by E( ) = E[ N( (5) he used equmen consderaon n (5) resul n E( [ Λ ( A + x) Λ ( A x) x A + x ) = ( A + ) ( ) (6) = = () Execed Penaly- cos From () (4) he oal execed Penaly- cos s gven by E[ φ, Y, τ ) = E[ N( ( y τ ) g( y τ ) Usng negrang by ars on (6) resuls n dy E[ φ, Y, τ ) = E[ N( ( G( y)) dy τ (7) (8) () Execed Penaly- cos From () (4) he oal execed Penaly- cos s gven by E[ φ ) = ne[ N( (9) ombnng all o coss, execed M cos, oal PM cos, ugrade cos, execed Penaly- cos execed Penaly- cos, yelds he oal execed cos o he lessor gven by ) = N( + ( a + b ) + ( a + b ) = = ϕ ( A x) + ωx /( e ) + N( ( G( y)) dy τ + n N( () =,,..., where =,,..., We can dene = + τ ( G( y)) dy + Then, () can be rewren as ) = Λ ( A+ x) Λ ( A x) ( ) ( ) A+ x A+ x = = + ωx /( e ϕ ( A x) ) + = n ( a + b ) + =,,..., = where =,,..., ( a + b ) ()

. Omzaon The omal arameers o he PM olcy are arameer values ha yeld a mnmum or ). We oban he omal values usng a wo rocess. In Sae one we aly. In Sae we derenal calculus mehod o oban ( T ) oban T by usng one-dmensonal mnmzaon mehod wh he erave rocedure. () Sae Fx, oban rom = T, < < <... < < b / (3) < < <... < < b (4) where, / < < < b / (5) As a resul, ) s a lnear uncon o consraned as ndcaed n (),(4),(3)-(5). Thereore, he omal values are he end ons o he consran nervals. Ths yelds + λ ( A x) < b / = or = b / > (6) where = A x = λ( A x) λ ( A + x) < b / = = = b / or > where = A + x = Ths mles ha he omal PM acon a (7) = T, =,,..., or = + T /, =,,..., s o reduce alure nensy by he maxmum amoun when < b / or < b / no o carry ou any PM when () Sae We oban b / or T b /., he omal T, by mnmzng T ) usng ( ) obaned rom Sage. One can oban T by usng one-dmensonal mnmzaon mehod wh he erave rocedure accordng o he algorhm gven by. Se : Gven =. Se : Evaluae sde consrans o T rom / + < T. / Se 3: =,,..., = + T /, =,,...,. Fnd T over he nerval / + < T / wh As a resul, ) s only a uncon o, rom () one dmensonal mehod se sze hen comue (4), he s consraned. Deermne he exreme on rom = / T = ( ) / T o ) by deermnng he rs aral dervaves o Se 4: omue rom ) corresondng o as below = T, =,,..., J ( T, ) / =... = J ( T, ) / = J ( T, ) /... = ( J, ) / = + T /, =,,...,. () Se 5: hen we have he consrans o as ollow Evaluae by lacng n (6) (7) resecvely. Se 6: Evaluae ) rom () Se 7: Se new, reea Se onwards unl + = max where max = Λ ( ) / a, hen go o Se 8. Se 8: Search or T whch yelds he smalles values or ). Usng hs, he omal PM acons are gven by T = ( ) he mnmum execed cos o he lessor gven by J ( T, ( T )). IV. NUMERIA EXAMPE We assume ha he alure dsrbuon or he equmen s gven by he wo-arameer Webull dsrbuon [. As a resul, β λ ( )( ) = β / α / α (8) wh scale arameer α > shae arameer β > (mlyng an ncreasng alure rae). Accordng o [3 we can assume α = because o he scale arameer α has no nluence o he model hey are wo or hree arameer Webul model. The rear me, Y, s a rom varable wh dsrbuon uncon G (y). We assume ha G (y) s also a wo-arameer Webull dsrbuon uncon gven by G( y) = ex[ ( y / ϕ ) m ; y (9) wh he scale arameer ϕ < he shae arameer m < (mlyng a decreasng rear rae). We consder he ollowng nomnal values or he model arameers

= 5 (years), = (years), = $, = 3$, = $, ( ω) =, u ( ϕ) =. n, a = $, b = 5$, τ = (days), β = 3, α = m =. 5, ϕ =. 5 A. The Omal Parameers or he PM Polcy As a resul, T =. 755 years ha means me nerval o PM acon s 3.3 monhs or he rs erod.66 monhs or he second erod we have, 7, u = =, = 7 ha means under leasng me, PM acons me s 7 he omal o ugrade level s 87% ha gve he mnmum oal manenance cos $9,88.. The omal arameers o mul erodc PM olcy wh ugrade cos are gven n Table. Table. The omal arameers or he PM olcy wh ugrade cos ( A = 5) A =5 x % T J ($) 3 4.8 9,984.78 4 9 39.93 73,846.8 6 9 6 35.35 4,69.76 8 8 3 3.47,74.93 87 7 7.755 9,88. 9 7 7.755,696.7 Table. The omal arameers or he PM olcy wh ugrade cos (comare A wh he several age) Ugrade A x % T J 63 7 7.755,34.79 76 7 7.755 4,67.9 3 8 7 7.755 6,6. 4 85 7 7.755 8,3.5 5 87 7 7.755 9,88. 7 89 8 3 3.47,683.59 9 9 8 3 3.47 5,4.98 Toal cos 3,. 5,.,. 5,.,. 5,. Toal cos age o equmen beore lease comarson. 3 4 5 6 7 8 9 Age o equmen beore lease Fg. Toal cos (J ) age o equmen beore lease (A) comarson Toal cos 4,.,. Toal cos ugrade level comars on We can comare he urher age o used equmen beween usng mul PM olcy wh ugrade mul PM olcy whou ugrade. The resuls are shown n he Table.3, ugradng acon have eeced o decrease oal manenance cos. Moreover he older used equmen, we have been releved oo much manenance cos wh mul PM olcy wh ugrade as shown n he Table.3,. 8,. 6,. 4,.,.. 3 4 5 6 7 8 9 Ugrade level Fg. Toal cos (J ) ugrade level (x%) comarson As Fg. we can noce ha he hghes ugrade level have no gven he mnmum manenance cos bu he ugrade level wll drecly varable wh age o he equmen as he resul n Table. Table.3 The omal arameers or he PM olcy wh ugrade cos whou ugrade cos Ugrade No ugrade A x % J J Δ J ($) 63 7,34.79 3 7,7.9 5,46. 76 7 4,67.9 35 39,77.7 4,57.4 3 8 7 6,6. 39 73,7. 56,559.9 4 85 7 8,3.5 4 9,79.75,47.7 5 87 7 9,88. 5 78,88.69 58,998.49 7 89 3,683.59 54 334,986. 3,3.53 9 9 3 5,4.98 58 54,584.3 56,369.5

V. ONUSION The roosed o hs aer s o research he mulle erodc revenve manenance olcy whch or used equmens under lease. Ugrade s he acon whch decrease alure rae oal manenance cos. Avalable mul erodc PM s more advanage han erodc PM cause o corresond wh degeneraon o he used equmen whle sequenal PM s more delcae o ado han mul erodc PM whch dvde me o wo nervals. Resuls o hs aer can suor leaser o carres ou he used equmen or lease wh he mnmum cos nvoe ros as long as he oal mnmum cos sll hgher han nves he new one. [3 S. Sheu,. Kuo, T. Naagawa, Exended omal age relacemen olcy wh mnmal rear, RAIRO: Recherche Oeraonalle, 7(3), 993, 337-35. [4 H. Wang, A survey o manenance olces o deeroraon sysems, Euroean ournal o oeraonal research, 39,, 469-489. REFERENES [ R. E. Barlow,.. Huner, Omum revenve manenance olces, Oeraons Research, 8, 96, 9-. [ R. E. Barlow, F. Preshan, Mahemacal Theory o Relably, New Yor: Wley, 965. [3 W. R. Blshche, D. N. P. Murhy, Relably, modelng, redcon omzaon, New Yor: John Wley & Sons,. [4. Y. heng, M.. hen, The erodc revenve manenance olcy or deerorang sysems by usng mrovemen acor model, Inernaonal ournal o aled scence engneerng, (), 3, 4-. [5 J. Jauronnaee, D. N. P. Murhy, R. Boondsulcho, Omal recenve manenance o leased equmen wh correcve mnmal rears, Euroean ournal o oeraonal research, 74, 6, -5. [6 D. n, M. J. Zuo, R.. M. Yam, General sequenal merec revenve manenance models, Inernaonal ournal o relably, qualy saey engneerng, 7(3),, 53-66. [7 X. u, V. Mars, A. K. S. Jardne, A relacemen model wh overhauls rears, Naval research ogscs, 4, 995, 63-79. [8 J. Medh, Socasc rocesses, New Delh: Wley Easern, 98. [9 H. Mormura, On some revenve manenance olces or IFR, Journal o he oeraonal research socey o aan, (3), 94-4. [ H. Mormura, H. Maabe, A new olcy or revenve manenance, Journal o he oeraonal research socey o aan, 5, 963a, -4. [ H. Mormura, H. Maabe, On some revenve manenance olces, Journal o he oeraonal research socey o aan, 6, 963b, 7-43. [ D. N. P. Murhy, D. G. Nguyen, Omal age olcy wh merec revenve manenance, IEEE Transacons Relably, 3, 98, 8-8. [3 T. Naagawa, Imerec revenve manenance, IEEE Transacons on Relably, 8(5), 979, 4. [4 T. Naagawa, Sequenal merec revenve manenance olces, IEEE Transacons on Relably, 37(3), 988, 95-98. [5 T. Naagawa, S. Osa, The omum rear lm relacemen olces, Oeraonal research Quarerly, 5, 974, 3-37. [6 P. D. T. O onner, N. Newon, R. Bromley, Praccal relably engneerng, Wes Sussex: John Wley & Sons,. [7 T. Nyamosoh, J. Pongech, Mulle-erodc revenve manenance olcy or leased equmen consderng wo yes o enaly The 37h Inernaonal onerence On omuers Idusral Engneerng, 7 [8 T. Nyamosoh, J. Pongech, Omal Mulle-Perodc Prevenve Manenance Polcy or eased Equmen Inernaonal DSI / Asa Pacc DSI7, 7 [onlne Avalable: h://as.nda.ac.h/resource/ascon_resource/ads7/aers/fnal_ 9.d [9 H. Pham, H. Wang, Imerec manenance, Euroean ournal o oeraon research, 94, 996, 45-438. [ J. Pongech, D. N. P. Murhy, Omal erodc revenve manenance olcy or leased equmen, Relably engneerng sysem saey, 5, -6. [ J. Pongech, D. N. P. Murhy, R. Boondsulchoc, Manenance sraeges or used equmen under lease, Journal o qualy n manenance engneerng, (), 6, 5-67. [ S. S. Rao, Omzaon heory alcaons, New Delh: Wley Easern, 978.