Abstract
In this paper, we develop a practical decision making tool to analyze and detect system bottlenecks for serial production lines with multiple Geometric machines and finite buffers. Systematic approaches are investigated to quantify the overall system performance-the production rate and its interactions with characteristics of individual machines. Two types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems where machine's downtime is significantly longer than the cycle time.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 13952-13957 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 50 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 2017 |
Keywords
- Throughput analysis
- bottleneck analysis
- bottleneck indicators
- geometric machine
ASJC Scopus subject areas
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 50, No. 1, 07.2017, p. 13952-13957.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Identifying Bottlenecks in Serial Production Lines with Geometric Machines
T2 - Indicators and Rules
AU - Kang, Yunyi
AU - Ju, Feng
N1 - Funding Information: Abstract: In this paper, we develop a practical decision mafling tool to analyΩe Abstract: In this paper, we develop a practical decision mafling tool to analyΩe Abstract: In this paper, we develop a practical decision mafling tool to analyΩe manadchdineteesctansdysftienmitebbouttffleernse.cSflys sΦtoermsaetriicalappprorodaucchteiosnarleiniensvewsittihgamteudlttioplequGaneotimΦyettrhiec and detect system bottlenecfls Φor serial production lines with multiple Geometric machines and finite buffers. Systematic approaches are investigated to quantiΦy the machines and finite buffers. Systematic approaches are investigated to quantiΦy the overall system perΦormance-the production rate and its interactions with charac-overall system perΦormance-the production rate and its interactions with charac-teristics oΦ individual machines. Two types oΦ bottlenecfls are defined and their teristics oΦ individual machines. Two types oΦ bottlenecfls are defined and their pcoerrrΦoesrpmoanndcien.gTihnedsiecamtoetrhsoadrsepdroevriivdedqutaontpirtoavtiivdee toguolisdeΦolirnemsoodnelinimg,paronvailnygΩinsgy,staenmd corresponding indicators are derived to provide guidelines on improving system perΦormance. These methods provide quantitative tools Φor modeling, analyΩing, and perΦormance. These methods provide quantitative tools Φor modeling, analyΩing, and improving manuΦacturing systems where machine’s downtime is significantly longer timhapnrotvhiengcymclaentuimΦacet.uring systems where machine’s downtime is significantly longer improving manuΦacturing systems where machine’s downtime is significantly longer than the cycle time. © 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keyworfis: Throughput analysis, geometric machine, bottlenecfl analysis, bottlenecfl Keyworfis: Throughput analysis, geometric machine, bottlenecfl analysis, bottlenecfl Keyworfis: Throughput analysis, geometric machine, bottlenecfl analysis, bottlenecfl indicators 1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION Improving the perΦormance oΦ production systems is Improving the perΦormance oΦ production systems is Improving the perΦormance oΦ production systems is important Φor practitioners in the industry. Ideally, important Φor practitioners in the industry. Ideally, maintenance service should be Φully scheduled and maintenance service should be Φully scheduled and continuously improvements should be perΦormed at continuously improvements should be perΦormed at saollurtcimesessu. cHhoawsebvuerd,geint, pspraacctei,cea,nduteechtonilciimanitse,donrley- all times. However, in practice, due to limited re- sources such as budget, space, and technicians, only sources such as budget, space, and technicians, only opnarettoimΦ teh. eMsoyrsetoevmerc,aans btheesemrvaincuedΦacotruuripnggrapdreodceasts part oΦ the system can be serviced or upgraded at boneceotmimese.mMoroerecoovmerp,liacasttehde(em.ga.n,uaΦdadctituirviengmapnroucΦaescs- one time. Moreover, as the manuΦacturing process becomes more complicated (e.g., additive manuΦac- becomes more complicated (e.g., additive manuΦac- turing, composite material manuΦacturing, renew- turing, composite material manuΦacturing, renew- able energy manuΦacturing), practitioners have to able energy manuΦacturing), practitioners have to mafle decisions Φrom numerous maintenance options mafle decisions Φrom numerous maintenance options TanhdertehΦoerire,imidpenatcitΦsyionng aonvedraimll psryosvtienmg tpheerΦfolermy acnocme-. and their impacts on overall system perΦormance. ThereΦore, identiΦying and improving the fley com- ThereΦore, identiΦying and improving the fley com- ponents, or the bottlenecfl machine, is oΦ great im- ponents, or the bottlenecfl machine, is oΦ great im- portance. Bottlenecfl machine reΦers to the machine that has Bottlenecfl machine reΦers to the machine that has Bottlenecfl machine reΦers to the machine that has mthaencbeig. gInestterimmspaocΦtmoanintehneanocveeraanlld simysptermovepmeernΦotrs-, the biggest impact on the overall system perΦor- tmhaencbeo.tItnletneercmfls ΦoaΦllms aiinntetnhaencreelaantidonimshpiprovbeemtwenetesn, mance. In terms oΦ maintenance and improvements, the bottlenecfl Φalls in the relationship between the bottlenecfl Φalls in the relationship between manchcien,esuucphtimase porrodduowctniotinmreatwei.thThsyestreamtiopnaerleΦoris- machine uptime or downtime with system perΦor- tmhaantcieΦ,asumcahchaisneprisodcuonctsiiodnererdatae.s Tthheecrriattiicoanlacloemis- mance, such as production rate. The rationale is that iΦ a machine is considered as the critical com- that iΦ a machine is considered as the critical com- dpuonceenhti,ghi.ley, dae manchdiendepnaeretsd,sittois ceoxnpteicntueodutshlyatptrhoe- ponent, i.e, a machine needs to continuously pro- duce highly demanded parts, it is expected that the duce highly demanded parts, it is expected that the tohpeermataicnhginteimcoeuΦlodrrtehciesivmeamchaineteinsalnocnegimenmouegdhiataenldy operating time Φor this machine is long enough and the machine could receive maintenance immediately the machine could receive maintenance immediately o★nce Φailed. On the other hand, a machine that ★onTcheisΦawiolerkd.is OsunpptohrteedoitnheprarthabyndN,SFa GmrancthiNnoe. CthNaSt- 1★63T8h2i1s3waonrkd iFsosuunpdpaotirotnedoifnSptaatret KbyeyNLSaFboGrraatnotryNoof. ACuNtSo-- This work is supported in part by NSF Grant No. CNS- m6o3t8iv21e3SiamnudlaFtoiounndaantdioCnoonftrSotla,tJeilKineyUnLiavberosriatyto.ry of Auto- 1638213 and Foundation of State Key Laboratory of Auto- motive Simulation and Control, Jilin University. motive Simulation and Control, Jilin University. Publisher Copyright: © 2017
PY - 2017/7
Y1 - 2017/7
N2 - In this paper, we develop a practical decision making tool to analyze and detect system bottlenecks for serial production lines with multiple Geometric machines and finite buffers. Systematic approaches are investigated to quantify the overall system performance-the production rate and its interactions with characteristics of individual machines. Two types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems where machine's downtime is significantly longer than the cycle time.
AB - In this paper, we develop a practical decision making tool to analyze and detect system bottlenecks for serial production lines with multiple Geometric machines and finite buffers. Systematic approaches are investigated to quantify the overall system performance-the production rate and its interactions with characteristics of individual machines. Two types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems where machine's downtime is significantly longer than the cycle time.
KW - Throughput analysis
KW - bottleneck analysis
KW - bottleneck indicators
KW - geometric machine
UR - http://www.scopus.com/inward/record.url?scp=85044277735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044277735&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2017.08.2217
DO - 10.1016/j.ifacol.2017.08.2217
M3 - Article
AN - SCOPUS:85044277735
SN - 2405-8963
VL - 50
SP - 13952
EP - 13957
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 1
ER -