A Review of Optimization Models and Techniques for Maintenance Decision Support Systems in Small and Medium Industries.

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MICEET

2OO8

COMMITTEE

III{TERNATIONAL AI}YISORY COMMITTEE
: Muhammad Arief (Indonesia)
Chair

Mernbers : Dadang suryamiharja (Indonesia)

Keigo Watanabe (JaPan)
Azah Mohamed (MalaYsia)
David V. Thiel (Australia)
Muharnmad Tola (Indonesia)
ME id Al-Dabbagh (Australia)
Josaphat Tetuko sri sumantyo (Japan)
Ivan Azis (Indonesia)
Harnzah Berahim (Indonesia)

IITTERITATIOI\AL PROGRAM
Chair
Mernbers

lY

:
:

C OMMITTEE

Salama Manjang (Indonesia)
Lukito Edi I'{ugroho (Indonesia)
Tumiran (Indonesia)
Eniman Syarnsuddin (Indonesia)
Hamzah Hilal (Indonesia)
Rhiza S. Sadjad (Indonesia)
Syamsir Abduh (Indonesia)
ZUfajri B. Hasanuddin (Indonesia)

Mochamad Anshari (Indonesia)
Rafiuddin SYam (Indonesia)
Anton $atria Prabuwono (Malaysia)
Elyas Palantei (Indonesia)
Armin Lawi (Indonesia)

ORGANIZING COMMITTEE
General Chair
Co-Chair
Secre

Nadjamuddin Harun
Zahjr Zairntddin

tariat
Yusri Syam Akil
Intan Sari Areni
Fitrianti Mayasari

Chair

\fembers

Finance Committee
Chair

Sri Mawar Said
Ansar Suyuti
Zaenaf. Muslimin

\f embers
Publication Committee

Chair
h{ernbers

:
:

Mukhtar Saleh
Tahir Ali

Rachmat Santosa

Local Arrangements Committee
Chair
: A. Toyib Raharjo
fuIembers
: Indrabayu
Indrajaya
Joseph Galla
Herman Rombe
Andani
Syafruddin Syarif
Christoforus Yohannes
Ingrid lrlurtanio
Dewiani
Gassing
Indar Chaerah Gunadin
Yusran
Ikhlas Kitta

ill

ldn. Bhd, Lingkaran Teknokrat Timur, 63000 cyberjaya, selangor, Malaysia;
-Faculty
of Engineering, Multimedia University,

Malaysia)

Indonesia)
i'

tlrl*

l\z

-'l j'ovel Randorn Frequency Generator Methodfor Anti-Jamming Communication
system, Eko Patra T.w.t, Arwin D.w. sumaril2 {lDepartment of Electronics,
Indonesian Air Force Academy, Yogyakarta, Indonesia; 2school of Electrical
Engineering and Informatics, Bandung Institute of Technology, Bandung,

ill-"'*l*

-"*-L
-

125

Error Performance Analysis of Cooperative Relaying Communications with Fixed
Gain and csl-Assisted Relays, sirmayanti {The state polytechnic of ujung

Pandang)

l3o

:exsion 7. Computer Engineering & Informatics (CEI-2)

i : IEI

Designing MultiAgent-based Information Fusio,n System, Arwin Datumaya
Wahyudi Sumarilz, Adang Suwandi Ahmad2 {lDepartment of Electronics,

Indonesian Air Force Academy, Yogyakarta - .55002, Indonesia; 2School of
Electrical Engineering and Informatics, Bandung Institute of Technology,

Bandung,Indonesia)

87

-r"'9CEI Context-Based Information Retrieval of Athtetic Sport Management System
(ASA[S), Nuridawati Mustafa, Muhammad Haziq Lim Abdullah, Norazlin
Mohammed, Nur Filzah Zainan {Faculty of lnformation and Communication

''rt.rgp1

Technology, Universiti Teknikal Malaysia Melaka, Locked Bag 1200, Hang Tuah
1aya,75450 Ayer Keroh, Melaka, Malaysia)

144

Preliminary Analysts of Dynamic Fleet Management Support System, Abdulah
Fajar, Anton Satria Prabuwono, Nanna Suryana Heiman, Zulkifli Tahir

{Industrial Computing Department, Technical University Malaysia Malacca,
Locked Bag 1200, Ayer KerohTs4sa, Malacca Malaysia)

l4g

'r-iCEI A Review of Optimization Models and Techniques for

Maintenance Decision
Zulkifli Tahir, Anton Satria
Prabuwono, M.A. Burhanuddin, Habibullah Akbar {Industrial Computing
Department, Faculty of Information and Communication Technology, Technical
University of Malaysia Melaka,Locked Bag 1200, Ayer Keron,li+SO Melaka,
Support Systems in Small and Medtum Industries,

Malaysia)

153

012CEI A Binary Tree Construction from Its Preorder and Inorder

Traversals, Armin
Lawi {Department of Mathematics, Faculty of Mathematics and Natural Science,
Hasanuddin University,

013CEI Assessing

Indonesia)

fig

e-Government Security Risk: A Preliminary Study,Irfan Syamsuddin
of Ujung Pandang, Makassar, Indonesia; 2Techno-

{'State Polytechnic

Management , Economics and Policy Program, College
National University South Korea)

014CEI

of Engineering, Seoul
162

ComponenbBased, Automatic HDL and C Code Generation
for Control System
Design Using Metamodeling Techniques, Andreas Voget {Department of

Electrical Engineering, Faculty of Engineering, Hasanuddin Univeisity,

Indonesia) 166

ix

Ol 1CEI
ru)irffiiuLruiiii,ai:

-,: : - resia

November 13-14, 2008

e\\- of Optimrzation Models and Techniques

\Iaintenance Decision Support Systems
in Small and Medium Industriss
l-jir"ili Tahir, Anton Satria Prabuwono, M.A. Burhanuddin, Habibullah

Akbar

Industrial Computing Department
Facalty of Information and Communication Technologt
Technical University of Malaysia Melaka
Locked Bag 1200, Ayer Keroh,
75 4 5

:.:-i:iili

'xrusnrn&.ir"rth

n

0 Melalca, MalaYsia

ra, antonsatria, burhanuddin, habibullah ralOutem.edu.my

is based on the fact that decision support
for maintenance process in commonly

meeded

maintenance functions with varieties
mnnuuris nnd techniques have been proposed for
ilmffr',ffinil$trmk The aim of this r€search was to identify

papers into tsn areas maintenance techniques as shown in Fig.
1.

irrr||luur:-

n models and techniques to
support system in small and

conduct
medium
., A s1 stematic literature revielv w&$ performed
r: ln-fCIrmation. The results shown several trends
ffiffi "frrnr,'ts area. Next. the research direction has been
;ru:xi"riinn

mp, *d'h-S

rhe svstems.

I.
,'*r'* ',-

IxTRonUCTION

oi maintenance functions for maintenance
rnly industries has gro\#ing rapidly. A

:;': end publications in the field rnaintenance
*, .,,1; '-; and techniques have be en published to
r-

tf:,;:r\"eness of maintenance process. As a part of
for maintenance decision support system in
r,;; 'r*rn industries. we need to collect the related
rrrl, : those areas. The specific objectives on this

:i'rru$

_.,rnrrrlrr:r*i

Fig.

llulr

tir,r field

of

techniques has been described as follow:

A) Total Productive

'-

rnaintenance decision models and

field of maintenance decision

I f:;ggest directions for future fesearches in this field'

il.

B)

ManqTENANCE TncnxIQUES

iln;e techniquss have been introduced. References [1]

;;ssified various maintenance techniques from

sTg-

979-18765-0-6

54

the

[

1

5]).

A set of tasks
generated on the basis of a systematic evaluations that are
Reliahility Centered Maintenance (RCM):

used

r

:.rn;e strategy (such as predictive, preventive,
r, i ,Jr corrective) can be determined. There are many

it is

to develop or optirnize & maintenancs pfogfam.
RCM incorporates decision logic to ascertain the safety

technique is the procedure that used by
rnanagement for doing their rnaintenance
:rocess. It is expected that the most appropriate

rilrmllitfi:inence
:

Maintennnce {TPM);

rnaintenance theory which has been introduced by
Nakajima [2] that identified rnaintenance activities as the
five major pillars, and then, now have improved become
eight pillars, consists of health and safefy, education and
training, autonomous maintenance, planned preventive
maintenance, quality maintenance, focused irnprovement,
support systerns, and initial phase managernent (t3]-

ilL]es.

i, n: ,:,entif1 trends in the
ilt;;:l;r]a S)'Stem.

Techniques Classification [1]

Each classification and available literature of maintenance

""''i :r:.:dbe the classifications and available literafure
itlltl|llt":trfl*

t Maintenance

and operational consequsnces of failures and identifies
the rnechanism responsible for those failures ([16]-[21]).

C)

Preventive Maintennnce (PM): it is an important
rnaintenance activity. Within a maintenance organization
it usually accounts for & major proportion of the total

153

Proceedings of The I"u' Makassar Infemarional Cr:nference on
Elecmical Engineering and Informarics
Hasanuddin Universiry, Makassar, Indonesia November 13 "L4,2008

most quantitative. The SMM aFpsd
more quantitative, involving the rffi

maintenancc effort, PM may be described as the care and
servicing by individual involved with maintenance to
keep equipment/facilities in satisfactory operational state

models that integrate technicim-

by providing for

semantic inspection, detection, and
correction of incipient failures either prior to their
occulrencs or prior to their developrnent into rnajor
failure. Some of the main objectives of TpM are to:
enhance capital equipment production life, reduce critical

J)

D)
E)

predetermined limit. If the machine cannot
tolerance? a CBM is initialized (t12l-t4Sl)"

hold

a

Computerized Maintenance Managemerct System
it is one of a computer softwars program that
to assist in the planning, management, and
administrative functions required for effective
maintenance. These functions include the generating,
planning, and reporting of work orders (wos); the
(CMMS);
designed

ri
&

broader than that of RCM and TPM [6S$..
rtrsfr Based Maintenilnce (RBM).' R.BM

maintenance strategy meeting the
minimization of hazard caused bv

equipment breakdowns, allow better planning and
scheduling of needed maintenance work, minimize
production losses due to equipment failures, and promote
health and safety of maintenance personnel ([BJ, [g], lzzJ[41]).
Conditton Based Maintennnce (CBM); The pM service is
based on some reading, measurernent going beyond a.

operational aspects from business

SMM views rnaintenance from

equipment and a cost effectiv.

ffi

,t*r*gy itrm"

III" MaTNTENANCE OprnrrzATTor
Maintenance optimization is the proces$ m
balance of maintenance requirement snffi n
economic, technical or others. The goels b

for ililib
in the system and identifying ec Wfl

appropriate maintenance technique
equipment

the maintenance technique should be conduc@d

best requirement, maintenance target
equipment reliability, and system

s

References [ 1] have presented various resouruer fr
maintenance optimization models as shown in fq:"

developrnent of tracery history; and the recording of parts
transactions 1491. One of the research by t50] propose

decision making grid (DMG)

F)

to

support the CMMS

AHP

applications ([5 1 ]-t571)
Predictive Maintenence: it is consists in deciding whether
or not to maintenance a systern according to its state {[58],

MCDM
Gailbraith
Organization Modeling

tsel).

MAIC

G) Maintewtnce Outsourcing: this refers to transferring
workload to consider with the goals of getring higher

MILP

**tr

quality maintenance at faster, safer and lower cost. The
others goal are to reduce the number of full-tirne
equivalents (FTEs) and concentrate organization's talent,
energy and rssources in to areas called core competencies

**'ig" Fetrinet
'!r Bayesian

(t601-t621)

H)

Effectiveness Centered Maintenance (ECM).' it stresses
"doing the right things" instead of "doing things right".
This approach focuses on system functions and customer

service, and has several feafures that are practical to

Fig. 2 Maintenance Optimization Classificariom l. j

Each classification and available literafure of
optinrization models has been described as follow':

A)

enhance the performance of maintenance practices and
encompasses cors concepts of quality management, TpM
and RCM. The ECM approach is more comprehensive as

il

Analytical Hierarchy Process (AHP): it was
Thoma$ L. Saaty [66] a$ mathematical decisim
model to solve complex decision making pr
there are multiple objectives or criteria consic
requires the decision makers to provide jud

compared with TPM and RCM, It is composed of people
participation, qualify improvement, and maintenance

the relative importance criterion for

strategy development and performance rneasurement [63].
Str*tegic Maintenance Managenxent (SMM).' In the SMM
approach, maintenance is viewed as a multi disciplinary

alternative ([67], [68]).
Adultiple Criteria Decision Making (MCDM):
selecting between alternates is a relatively c

activify. This approach overcome$ some

of the the

deficiencies of RCM and TPM approaches as these do not
deal with issues like operating load on the equipment and
ints effect on the degradation process, long term strategic
issues and outsourcing of maintenance, etc. in addition,
these approaches to large extent are qualitative or at the

r54

Fuzzy Linguistic

**,'&F Markovian Deterioration

B)

often difficult task. MCDM refers to

each

the

decision and planning problerns involving m
generally collectittg requirements. The Decisiom
(DM) one reasonable alternative from among r
available ones {[69], [70]).

ISBN: ?78-979-

r

87

'

011CEI

)'{akassar International Conference on

- ,nci informatics
""," )uiakassar,

Indonesia November 13-14, 2008

of events A and B both ocsurring can be written as the
probability of A occurring multiplied by probability of B
occurring given that A has occulred ([82], t83l). This is
written as:

The theory believes that 'othe greater
; r* -, : f the task, the greater amount of information

i processsd between decision makers during
r,l of the task to get & given level of
.** -;' Industries can reduce uncertainfy through
:

: - -:t

i,,':i

-:::rg

,**"-r Modeling: References [72] reviews
* :- r i organization models, s.g. advanced
l-:

:.;sical rnodel (ATM), Eindhoven {Jniversity of

::",

model(EuT), total quality managemen!
,, : i,ai ;*d#;;fir-i;;#Crir'ajl[-r?-u'riir;
:
*

:, ;=':jrtf--jffH;,1?j-i,l'fu

-

'.'rrr"r'*'

.

'i

P(A and B) =P(A) P(B/A)

and coordination, often by rules, hierarchy,

.#

:

x-JTl

technology (IT) and showed that integrated
:,-r-planfling of production with maintenance,

K)

Simulation: t84l and tSsl use the Monte Carlo simulation
to determine optimum maintenanc€ policy and for
modeling on continuously monitored systems. [86] has
used simulation model to reduce maintenance and
inventory cost for a manufacturing system with stochastic
itern failure, replacement and order lead times. [87]
demonstrates application of sirnulation models to evaluate
maintenance policies for an automated production line in
a steel rolling mill.
IV.

Aet-erences

::

::slurn support system,

"* , -;"

MAIC for maintenance of

nlant.

"n?et' Linear Programming (MILP); It is vsry
;rrnervork for capturing problems with both

1r

[73] have presented a krrowled"ge-

;

r,'cislons and continuous variable. This includes

;: * ;i: problems, control of hybrids

system,

,::Ji

istic: Fuzzy logic was inhoduced by Dr Lofti
,,, '
: - UC/Berkeley in 1960s as a superset of
:' -. : "lal or Boolean logic that has been extended to
,:r; r : - i concepts of partial truth
- truth values between

lurff:

-

: r::1r, trLle" and "completely false". It

-*'

nENI.TIFICATION

1) From all described publications, there are limited
models have been implemented in industrial

maintenance process. The data problems and the gap
between theories and practice have always become the

,:-affine (PWA) system (including approxirnation
* : rer system), and problems with
non-convex

:. ii4).

I

several trends that related in the field of maintenance decision
support systems, each of which have been described as follow:

:': ii,,

-:'-

TnnT{DS

After a brief description of classific,ation in rnaintenance
optimization models and techniques, we have identified

reason.

2) It seems a lot of maintenance

optirnization models and
techniques have been published for this arsa. Various
simulating tools and mathematical models have been
attempted. Although the irnprovernent of IT (both soft

and Hardware) can support to easy develop of the
system with law cost and systematic modules, it is
limited work has directed toward developing into

was

operational applications such as computerized system

llliiiur,-::,:"

lllilllllln:.T

'

r'-'irl t75]. By the term "fuzzinsss" Zadeh meant
transition from

:"! in which there is no sharp

;ik:

:

:::f

3)

Ship 1767,

,, , ; it't Deteriorntion: Markovian deterioration is

or DSS. It can be said that the impact of decision
making within a maintenance organization has so far
been limited.
Some of the publications have designed the integration
existing maintenance optirnization models and

of

techniques such as DMG, ECM, SMM and RBM. It
can be notice that a new potential research have existed

a

to
4)

irnplement them

r;i'r,l

ilp:.,;,"'"

i:ient (t77]-tB0]).
r "' Petri nets, it is one

of sevsral

mathe matical

-::lE languages for the description of discrete
*fi.: 'r* :'"-;ted system. A Petri net is a directed bipartite
,4r:-;::: in which the nodes represent transitions (i,e.
r

rrL,,

I ,:.

'.fi':,",.*-.i events that may occur), places (i.e. conditions),

j
,K l

:-rected arch (that describe which places are prs *
:
: post conditions for which transitionsxS l ].
,
J,;- : - . -ut: Bayesian statistic is based on Bayes' rule or
* :::ronal probability,
It is well larown that probability
'rj*
luu

0

"s " g7g

DSS

Much design of CMMSs is as a device for analysis and

coordination
1rlffi ;

in the real CMMS or

applications.

to

storehouse

the

maintenance

information, such as a PM tool and a maintenance
work planning tool. However, less of them have
embedded with decision support modules to be linked
in the actual industrial maintenance process.

V.

RESSnRCH

DmpcnoN AND AcTIvITIES

The direction is highlighting to emerging trends that have
been identified. The futures researchers can conduct the
maintenance decision support system with consider the

.l 9765-0.6
155

Proceedings of The 1" Makassar Intemational conference
on

Elecrical Engineering and Informarics
Hasanuddin university, Makassar, Indonesia November 13-14, z00B

finding trends. The following step is suggested to conduct the

internal and externat data) and knowledge qi.G"
and explicit knowledge) available in various
the industries or its external environment. Donisi
be conduct from identify the related data
with decision making models.

systems.
I

)

choosing the rnaintennnce techniqttes: it is the
to follow in
industrial maintenance process. There are ten
techniques that we can choose frorn this papers
description. Each of thern have specific characteristic.
We can choose the most appropriate techniques that
can be irnplemented in the real industrial maintenance
fundamental steps that we must concern

2)

3)

'.\
VI. CoNCLUsIoNS

we have reported the ongoing research project

process.

conduct maintenance decision support systems for
rnedium industries. The research is develnped with id

Data Analysis: it is a fundamental issue in optirnizing
maintenance decision making. The decision based on
incorrect infonnation may be useless or harmful. Some
times data is seerns to be plentiful, by rnay not be of
quality of the quality expected.
Maintenance information system interfuce: one of
popular interfaces in industries is CMMS. It is mainly
serves as databases. The rnaintenance data in CMMS is
the most important information to help maintenance
departrnent rnaking the right decision for getting the
right maintenance strategies. Fig. 3 is described bf
tggl
as prototype to get decision out from maintenance data

the

from published

mai

to develop the system.
ACKNOWLEDCMENT

The authors would like to thank Faculty of Inforrnmi

Communication Technology, Technical Univensmy
Malaysia Melaka for providing facilities and Mrnisur
science, Technology and Innovation Malaysia for

in CNTMS indusrries.

MAINTENAHCE

appropriate literature

optirnization models and techniques. Ttre results have
the trends that related in the fields arsa. Irlext, those
be definitely giving benefit for assist the ressarch proplr

support.
REr.nnrNCES

DATA IN

Employee $ki[s Data

lll

Maintenance Budget Data

PM Inspe*tion Data

Customer Request & Complaints

Repairs Parts Data

Equipment Priority Data

Vendor Data

t}J

Downtime Data

Labor Performance Data
Time and Attendance Data

fr*il

Equipment Data
Work Order Data

t3l

A. Garg and s- G.

Desgmukh, "Application and

ca-se

maintenance management: literafure review and directions," Jr
Qrtality in Maintenilnce Engineering, vol. 12, no. 3,2006.

s. Nakajima, Introduction to TpM; Total productive Mai
Productivity Press, USA, lggg.

s.

Bonris, Total Productive Mai*tenance proven stratryw

techniqttes to k*rp equipment running at peak
fficiency, McGran
usA, 2006.

l4l
Job

Estimating
Utilization

,.-**-*;ts
**---**,*.

Labor

.,,|-

Work Order Status

!q**-*

Work Order Coeting

Lebor Ferforrnance

lsl

Work Order Prioritization

Overall Labor Productivity

Backfog Summary & Anatysis

lnventory Control & Management

t6l

Equipment Downtime & Cause Anatysis

Parts Availabitity Status

Maintenance Budgets Variance

Purchase Requistioning & Status
Vendor Performsnce Evaluation

Maintenance Budget Requirements

t7]

Equipment Repair Procedures

Customer Serviees Analysis

Planning & Scheduting Effectiveness

PM effectiveness

Overall Maintenance Effectiveness

t8l

F. K, wang, w- Lee, "Learning curve anarysis in total p
maintenance," Ornega, vol. Zg, no. 6, pp. 49l-g, 2001.
K. E' McKone, R. G. schroeder, and K. o. cua, "The impac nff
productive maintenance practices on manufacturing perfor:nn
Journal of operation Management, vol. 19, no. l, pp.ig-ss,2c*.;_

F. Ireland and B. G. Dale, "study of total productive mainm
implementation," Journal of euatity in Maintendnce Engineemry,
7, no. 3, pp. 183-g l, Z00l.

D. Das, "Total predictive maintenance: a comprehensive &aCIs
achieving excellence in operational systems," Iniustrial Ensir,w
Jourrtal, vol. 30, no. 10, pp lS-23,2001.
D. Gupta, Y. Gunalay, and M. M. srinivasan, '*The relati
befween preventive maintenance

;:;,'!.:l',r.-t.i.:,...:,:;.'-ii|-

-+-i_]i1i+!-.::.::;

Fig. 3 Maintenance Decisions Out [gg]

4)

maintenance

rt

a lot of
optimization rnodels with various

Choosing optimization models".

simulating'tools and mathematical rnodels have been
proposed in our area. Choosing the right one that
rnatch with our data measurement is one step to do. A
true maintenance optimization process continually

s)

t

i0l

[11j

l2l

monitors and optimizes the current maintenancs
program to improve its overall efficiency and

I

effectiveness.

n3l

Decision out:

In the process of

decision makiirg,

decision makers combine difrferent fypes

156

tel

seems

of data (i.;.

and

manufacfuring

r

performance'', European Journat of operational Research, v&
no. l, pp. 146-62,2001.
R- c. Gupta, J. sonwalker, and A. K. chitale, *,overall equi
effectiveness through total productive maintenance", prestige
af Adanagement and Research, vol. 5, no. L, pp. 6t -7Z,Z0Al.
R. Kodali, "Qualifieation of TpM benefits through AHp
Prodactivity, vol. 42, no. Z, pp 265-73, 2001.
R. Kadali and s, chandra, o'Analytic hierarchy process of justil
of total productive maintenance" , production ptanning and C
vol. I 2, no. 7, pp, 693-705. 2001.
T. Finlow-Bates, B. visser, and c. Finlow-Bates, "An inr
approach to problem solving: linking K-T, TeM, and RcA to
The TQIvI Magazine, vol. 12, no. 4, pp. 2&4-9,2000.

F. L. cooke, "Implementing TpM in plant maintenance:
organizational barriers," International iournal of
eualitTReliability Managemerxt, vol lT. no. g,Z0AA.

ISBN: ?78-?^79-18765

E

=imgs of The 1" Makassar International conference on
:i c"ai

Ol

lCEI

Engineering and Informarics

:.:llin

university, Makassar, Incionesia November 1i-14, z00B

K. .E. McKone, R. G. Schroeder, and K. O. Cua, "Total productive
[37] K. K. Lai, F. K. N. Laung, B. Tao, and S. y. Wang, .,practices of
rBmtenance: a contextual view," Journal in Operations Management,
preventive maintenance and replacement for engines: a case study,',
17, no' 2, pp' 12344,1999.
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