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E o Cod Irl o Bol Ido Slo

Kasemset', V KachitvichyanukufIDepartment ofndustrial Engineering, Chiang Mai Univesiy, Chiang Mai, Tailnd

2ndustria and Manufacturing Engineering, Asian nsiue of Technoogy, Pahtani, Tailand(ckjane@hotmail.com , voraas@ait.ac.h)

Abstract - This paper presents the app lication ofsimulation-based procedure to identify bottleneck in a jobshop environment. Ten jobs ten machines job-shop problem(OxO JSP) is used to test the simulation-based procedure for identiing the system bottleneck. The result from thecase shows that the choice of condence interval level (CL)used in throughput mean comparison has eect on the

bottleneck selection. Two scenarios are presented with wodifferent CL of throughput mean, 75% CL and 90% CL.

The result from the experiment shows that when % CL isincreased, the judgment of bottleneck identication can bechanged. Thus, it is necessary to use appropriate number ofrep lications to match with the required %CL used in the

simulation. In general, more replications are necessary whenhigher percentage of condence interval level is required.

Keords - Simulation, Bottleneck identication,Theory of Constraints (TC), Condence interval level (CL)

I. INTRODUCTION

Theor of Constraints (TOC), developed by Goldratt

and Cox in [], concenates on how to manage theconstraint of the system, the bottleneck resource orcapacity constraint resorce (CCR), to drive more incomeand for company to srvive in the real world business.

In order to implement this policy, there are ve stepsintroduced in []; (i) bottleneck identication, (ii)bottleneck exploitation, (iii) bottleneck subordination, (iv) bottleneck elevation d (v) retu to the rst step butprevent inertia from being the next constraint.

The bottleneck identication is the key to TOCimplementation because the bottleneck is the onlymachine at can limit the overall system performance. Ifthe ue system bottleneck c be identied, the

succeeding steps to improve system performance candenitely work.

To locate bottlenecks, many researchers pposed the bottleneck identication methods that utilized only staticdata. In [], [3] and [4], throughput rate was used toidenti the bottleneck. In [5], the expected load wasconsidered and compared with available load to d theCCR.

Under ob-shop environment, it is dicult to identithe real system bottleneck because different products havetheir own routes and usually share machines. In the casethat the bottleneck machine is located behind anothermachine that operates at a slow rate, it may be dicult to

971424403110/$2600 2010 IEEE

identi the bottleneck simply om e utilization or enumber in queue.

[6], simulation technique was applied to collectdata and used ese data to calculate two factors, the number of obs in queue for a resource and resourcecriticality factor. These two factors are used to identi bottlenecks in a ob-shop. In [7], [8], [9] and [0], bostatic data d data collected om simulation were usedto classi bottleneck candidates d identied e real

system bottlenecks via simulation.The obective of this study is to present theapplication of simulation-based procedue proposed in [7when it is applied to ten jobs ten machines ob-shop problem (lxl0 JSP) in the process of bottleneckidentication. The test, illusates the effect on the bottleneck selection when the condence inteal level(CL) used in throughput mean comparison is changed.

The orgaization of this paper is as follows. Thebased method, simulation-based procedue for bottleneckidentication proposed in [1] is ntroduced n Secton 2.The experiment on changing condence interval level inorder to identi the system bottleneck is presented in

Section 3. Conclusion and Discussion are addressed inSection 4.

II. BASED METHOD:SIMULATION-BASED PROCEDURE FOR

BOTTLENECK ENTIFICATION

Simulation-based procede for bottleneckidentication proposed in [7] is applied with l x 0 JSP inthis study. The following sections are e explanation ofthe procedure.

Prcedure ssumtions

This procedure is applied based on e followingassumptions;

) The target demand exceeds the system capacity.) All operation times and set up times e

exponentially distributed.

192

doted Procedure

order to identi the real system bottleneck, threefactors are introduced in [7];

) Machine/process utilization: these data can be

directly collected from the simulation model. Machines or

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processes having hi utilizations e selected to bebottleneck candidates.

2) Utilization factor (p): From [11], he utilizationfactor, based on queuing theor, M/Ml/K, is calculatedfrom p = A / J where A is the average number of unitsadded to the time buer per nit time nd J is the average

serice rate unit per unit time. Both A and J can beobtained om simulation model. Bottleneck cdidatesare selected om machines or prcesses with high

utilization factors.3) Bottleneck Rate Rb Rb is the rate (pts per it

time or jobs per ui ime) of he orkstation hainghighest long-term utilization frm Basic Factory Dynamicfrom [2]. Machines or processes with Rb are consideredas bottleneck candidates.

The three factors above can be used to identi potential bottleneck candidates.

Among all bottleneck candidates, the station thatmeets following three criteria is identied to be real e

system bottleneck;) High value of the machine/process utilization2) High value of the process utilization factor3) Low value of the product bottleneck rateThen, simulation is re-run again to conrm that (i) the

true bottleneck is selected and (ii) no other bottlenecksremain in the system (in case of multiple bottlenecks).

Real bottlenecks are identied when the simulation yields improving in system throughput by relaxing thatmachine constrints.

III. THE APPLICATION JSP CASE

By applying the method proposed in [13] to evaluatethe performance measre of the system, the meancondence interval of system throughput at 75% CL isconstructed ad comped with based situation. Theimprovement in the system is obsered when no overlapof he CL's between the CL from the based scenario dthose om the test cases (crently used 30 replications).

In is paper, 10x JSP is adopted. The detail of thisproblem can be found in [14].

Applying the simulation-based procedure om [7],the three factors, the utilization of machine, the utilizationfactor or p and the bottleneck rate or Rb are sho in

Table I.From this problem, machine number 7 meets all three

criteria addressed previously to quali as the system bottleneck. Then, he simulation is used to conrm thatthe tre bottleneck is selected and no other bottlenecksthat still remain in he system (in case of multiple

e e

bottleneck). The result om e simulation is shown inFig. l .

TABLE I1T DAA LAN L ND ANALYZED DAA

Machine no.% Rb

Utilization p (part/min)0 40.35 1.052 0.111 (P.5, P. lO)

1 41.68 0.940 0.111 (P.6)

2 1. 1.058 .111 .1 a 3 41.42 0.946 0.111 (P.5 and 6)

4 45.04 0.9770.11 (P.4)

5 48.37 1.1090.111

(P., 7 and 10)

6 43.74 1.100 0.111 (P.2)

7 .1 1.14 .12 .1 a.111 .68 47.60 1.047

9 33.76 1.094 0.111 (P.8)

Note: 1) Bold letters mean hat one of tee ctea S met order to

identi bottleneck cdidate.2) Example for machine no. 3 Rb 0.111 (P.5 and 6) means forproduct 5 d product 6 bottleneck rate is 0.111.

From Fig. 1, the result shows that when increasing theseventh machine's capacity in the test scenario, esystem throughput mean cnnot be improved whencomparing at 75% CL (There is some overlapped area ofe condence interval). It means that oer bottlenecksmay exist in the system. Thus, the simulation should bere-rn again to nd more system bottleneck candidates.

By relaxing the capacity of machine number 7, thethree factors used to identi the bottleneck are collectedand compared again (See Table II)

TABLE II DAA FRM LAN L AND NALYZED DAA

Machine no.% Rb

Utilization p (part/min)

0 45.11 1.067 0.111 (P.5, P.10)

1 43.20 0.954 0.111 (P.6)

2 2.2 0.964 .111 .1 a 93 43.41 0.997 0.111 (P.5 and 6)

4 46.81 1.067 0.111(P.4)5 47.89 1.132

0.111 (P.1, 7 d10)

6 40.36 1.056 0.111 (P.2)

7 25.00 1.0468 47.14 1.040

9 38.08 1.26 .111 (P.8)Note: 1) Bold letters mean hat one of tree ctea S met order toidenti bottleneck candidate.

2) Example for machine no. 3 Rb 0.111 (P.5 and 6) means forproduct 5 and product 6 bottleneck rate is 0.111.

B Cs! .61 4oo1-------------3:e+ O-=o 3o8e-=03-------4 e+0st >U0 Adi2 D +3Total - 8001------------269:+-='.-:7:+O-0 ----- 4 e 03

Fig. 1. 75% CL comparison of hrouput mean for 1 test

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From Table II, there is no machine at meets alltree criteria to quali as the system bottleneck.Machines number 2 and 9 are identied to be thebottleneck candidates. The simulation test is carried outto d the second system bottleneck. this case, two

scenios are set up. each scenario, each bottleneckcandidate capacity is added in addition to the increaseof the capacity of machine number 7.

Firs Senario ly 75% CL

The result in Fig. 2 shows that the throughput ofscenarios 2 and 3 show signicant improvements overthe based case. Thus, machines number 2 and 9 re alsoidentied as system bottlenecks. Thus, for this scenrio,the conclusion is that there are tee bottleneckmachines: machines number 2, 7 and 9.

Second Senario ly 90% CL

In this scenario, 90% CL is used instead of 75%CL. When e percentage of CL is increased, thedecision of bottleneck identication can be changed asin Figre 3.

From this study, with 10x0 JSP, when % CL isincreased to 90%, only machine number 7 and machine number 9 are identied to be the system bottleneck (when 75% CL is adopted there re three bottleneck

machines (machine number 2, 7 and 9)).

IV. CONCLUSION AND DISCUSSION

This paper aims to show the application of thesimulation-based procedre for bottleneck identication

in ob-shop problem. 10x JSP is used as a test

J Cal C I an tObk

problem. The eect of changing condence intervallevel when this procedure is applied is obsered aswhen % CL used to compare system troughput mean isincreased, the udgment of bottleneck identication may

be changed.

In the based method, e simulation test is conductedwith the based scenario at 75% CL based on 30

replications. In case of small number of rus with thehigh %CL, the different in troughput mean sometimescnot be clearly obsered so e bottleneck may not becoectly identied. Thus, e selected %CL shouldmatch with the number of s used. Where higher%CL is required, more replications are necessary.

However, this decision on bottleneck identicationhas eect on the bottleneck exploitation andsubordination phase because e schedule must beconsidered rst at the bottlenecks. Although, is

sitation occurs, the nal (5

) step of TOCimplementation can be applied to handle is issue because when the TOC system is adopted, the systemperformance must be evaluated to conrm that there is

no hidden bottleneck remaiing in the system. The 5

step of TOC implementation works as iterativeimprovement step that makes the system uder TOC become self-adusted when the system changes in the bottleneck location.

Furthermore the advantage of this procedure whenit is applied with large-scale size problems is to reduce

the number of potential candidates by considering threecriteria as mention earlier. Thus, the number ofsimulation test cn be reduced in order to identi thesystem bottleneck.

Bl1SNI \1Se 2 , 61. 003To 'oo1------------2:43.:003,;+:;8.:003 -_ 4003Spnmio 1: Adg 7 oy 2 , 003To H 800f-----------7"_----- 4.0032,9 00 0Spnio 2 Addg 2 "To 800f:r'e0032 .92& .0'3 3 2&003

Spnio Adg 9 \ 3,o03 JTo H e003-------.".=-- 4HOC39 eOe00Fig 2 75%CL comparison of throuput mean for 2nd test

sd C.. fan r _Obi

P:\S .00 ---------:.:;"F003_t;:----- 4 . +0032 34e003 289e+003SNlmio Ad 7 o 2 ,8 /0To TH 8001------------.,".-O0-.=.-":"'5:-0----'.+003Senmio 2 Add + 2 3 .09 003To 8001=>.,4eO02 84e+003 V ' ,35e. o03Senmio 3: Ad 7 + 9 I 3. 1 5. 0'To TH 0031-l;+- ,0032 1 .+0 3.003

Fig 3 90CL comparison of throuput mean for 2 Test

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