A Real-time Planning and Scheduling Model in RFID-enabled Manufacturing

Transcription

1 Proceedngs of the 2014 Internatonal Conference on Industral Engneerng and Operatons Management Bal, Indonesa, January 7 9, 2014 A Real-tme Plannng and Schedulng Model n RFID-enabled Manufacturng Ray Y. Zhong and George. Q. Huang Department of Industry and Manufacturng Systems Engneerng The Unversty of Hong Kong, Hong Kong, Chna Q. Y. Da School of Informaton Engneerng Guangdong Unversty of Technology Guangzhou, Chna T. Zhang, T.Y. Luo and D.P. Ln 3 Huaj Dengyun Auto Parts (Holdng) Co., Ltd. Zhaoqng, Chna Abstract As the ncreasng use of RFID technology n manufacturng companes, the real-tme producton data collecton s nadequate. Companes especally small and medum-szed manufacturng enttes, contemplate to mplement advanced plannng and schedulng (APS) so as to acheve real-tme producton. Ths paper, motvated by a reallfe case, proposes a real-tme plannng and schedulng model to realze meanngful APS n the RFID-enabled manufacturng envronment. Experments results show ts outperformance over other three rule-based approaches used by the case company. Based on ths model, a real-tme Kanban s adopted for supportng the coordnaton between planners and schedulers n the real-world ambence. Keywords Advanced Plannng and Schedulng, RFID, Real-tme, Rules, Shop floor Manufacturng 1. Introducton Real-tme producton plannng and schedulng refers to allocate varous manufacturng resources lke labours, equpment and materals to tasks through effcent and effectve ways to meet certan performance requrements [1]. However, n most practcal envronment, plannng and schedulng are ongong reactve processes where a varety of unexpected dsturbances s usually nevtable. The statc approaches, developed to solve ths problem, are often mpractcal n real-lfe cases [2]. Advanced plannng and schedulng (APS), adopted the dynamc schedulng prncple, has been ncreasngly used n the manufacturng companes where unpredctable events may cause changes n the scheduled plans and feasble schedules may turn nfeasble when they are carred out n shop floors [3]. However, the development of APS s lmted snce t requres perfect data to generate perfect decsons [3-6]. The companes have no such real-tme producton data collecton manners n place [7]. Therefore, manual operaton-based APS domnates these companes whch have faced large number of dffcultes [8]. In order to facltate the producton data capture, rado frequency dentfcaton (RFID) technology has been used [9-23]. Usng the RFID, large number of manufacturng data could be real-tmely collected. These data nclude the statuses and avalabltes of varous manufacturng objects such as equpment, labors and materals whch are determnstc throughout the producton processes [24, 25]. The nformaton s able to support actual and meanngful APS whch s regarded as an adaptve system that can handle any dsturbances from the manufacturng stes so as to meet constrans lke delvery date or makespan etc [6]. One of the key elements to mplement the APS n RFID-enabled manufacturng envronment s a real-tme plannng and schedulng model. To ths end, ths paper ntroduces such a model whch ntegrates a real-lfe case that has been used RFID for supportng ts shop floor producton over fve years. Ths model s based on two key concepts: hybrd flow shop (HFS) and real-tme job pool, whch are used for facltatng the producton plannng and schedulng wthn the RFID-enabled ambance such as shop floor manufacturng [26, 27]. Several research questons are concerned n ths paper. The frst queston s what strateges could be used n producton plannng level to meet the objectves such as mnmzaton of delay etc. The second queston s what objectves should be set n the schedulng level and what workng mechansms could be adopted so as to satsfy

2 both the objectves from plannng and schedulng level. The thrd queston s how the plannng and schedulng level nteract wth each other to ensure the plans and schedules could be carred out strctly n executon. Ths paper examnes several strateges used n the case company so as to fgure out whch strategy s sutable for a certan condtons frstly. Secondly, n schedulng level, several objectves such as total tardness, makespan and maxmzaton of equpment utlzaton are proposed followng a backward propagaton strategy. Fnally, wth the assstance of RFID technology and a real-tme Kanban, plannng and schedulng partes could nteract and ther executons are controlled. In case of dsturbances such as equpment breakdown, a warnng wll be sent and the processng jobs could be adaptvely re-arranged or manually ntervened. The rest of ths paper s organzed as follows. Secton 2 outlnes the real-tme manufacturng envronment where problem descrptons are summarzed. These problems come from a real-lfe manufacturng company. It focuses on reportng the RFID-enabled real-tme shop floor manufacturng envronment and the key workng prncples. Secton 3 ntroduces the plannng and schedulng model whch s based on the research background so as to acheve real-tme producton. Secton 4 reports on the experment from a numercal study so as to unravel the feasblty and practcalty of the proposed model. A real-tme coordnaton mechansm s reported usng the Kanban system. Secton 5 concludes ths paper by gvng our fndngs and future work. 2. Problem Descrpton A. RFID-enabled Shop floor Producton The research background s based on a real-lfe RFID-enabled shop floor producton whch could be smplfed as Fgure 1. The establshments lke the deployment of RFID readers, tags, communcaton networks are followed a strategc approach [26]. A real-tme Kanban plays an mport role n coordnatng the producton plannng and schedulng wthn the RFID-enabled shop floor manufacturng envronment [28]. Fgure 1. RFID-enabled real-tme shop floor producton. The operaton wthn the above producton envronment (regarded as HFS n ths paper) contans several steps. Frst of all, n plannng level, producton orders are controlled by a real-tme planner whch s responsble for sequencng them n an optmal to meet the objectves. Secondly, n schedulng level, the sequenced producton orders are converted nto jobs managed by a real-tme job pool for the 1 st stage. Actually, a producton order s splt nto several jobs each of whch carres 180 peces. Thrdly, the real-tme job pool for the 1 st stage releases jobs to a real-tme job pool for ndvdual equpment when the operator read hs/her RFID staff card (a tag) to get jobs. He/she read the tags attached on the materals to get job nstructons and other requred nformaton. Fourthly, when the operator fnshes a job, he pats hs staff card to nform the delvery of materals. Meanwhle, the job enters the job pool for next stage automatcally. The four steps wll be carred out contnuously wthn the producton shop floor untl a job reaches the end manufacturng stage. The producton plannng and schedulng problem wthn the RFID-enabled envronment can be descrbed as two levels: plannng and schedulng. 216

3 B. Producton Plannng In producton plannng, a set of producton orders PO { PO 1, PO 2,... PO n } should be sequenced n a sutable and optmal manner. Several objectves such as total tardness Tardness etc. should be satsfed. Here, ths paper consders the changeover tme of equpment tools ( CT ) that s because dfferent products should be processed by dfferent equpment tools accordng to ther materals. Ths tme greatly affects the total processng tme. In ths case, CT takes a certan proporton of tme when consderng the producton plannng. Ths paper analyss the quanttatve aspects of the nfluence of CT on the plannng level decson-makng. Each PO 1 n has several attrbutes whch are expressed by a set whch s regarded as varables n the models proposed n ths paper. C. Producton Schedulng In producton schedulng, a set of jobs J { J, J 2,... J n } should be sequenced. Jobs are splt from PO each of whch may dvded nto several jobs. That means jobs from a specfc PO have the same prortes, due date, product category etc. Jobs are managed n the real-tme pools for varous stages. The most mportant concern s the frst stage because the followng stages (stage 2, 3 K) are based on the accomplshment of jobs n stage 1, real-tme RFID nformaton as well as varous dspatchng rules. 3. A Real-Tme Plannng and Schedulng Model A. Assumpton Ths paper consders the model under several assumptons whch are lsted as follows: 1) Producton orders could be released to the frst manufacturng stage thoroughly. 2) Tme spent on changeover of equpment tools wll be consdered when processng jobs wth dfferent materals. 3) There are fve degrees for the prorty system expressed by nteger 1, 2, 3, 4, and 5 whch s wth the smaller value represented hgher prorty. 4) Each job/batch has a standard quantty-180 peces. 5) Jobs are released to equpment through RFID event-drven mechansm. 6) Tme spent on materal delvery s gnored. 7) Equpment operators are not concerned. 8) Once a job s started, t cannot be stopped unless the equpment breakdown. 9) Ths model allows emergency orders and manual ntervenes on real-tme job pools. 10) The buffers are unlmted. B. Formulaton Indces and sets PO set of producton orders J set of jobs PO producton order CT changeover tme of tools N total number of PO J job M total number of J K total number of stages L total number of equpment n stage number of tool changeover U capacty of equpment j n stage j Input varables P processng tme of PO weght of PO (prorty) ST setup tme of PO 217

4 S F d Q PT P ST S F d 1 jk start tme of PO fnshed tme of PO due date of PO quantty of products n PO raw-materal type of PO processng tme of J setup tme of start tme of J J fnshed tme of J weght of J (prorty) due date of J t RFID tme stamp of job obtaned at stage j by machne k 2 t jk RFID tme stamp of job fnshed at stage j by machne k Q quantty of tems n J PT ms = 1 0 jk = 1 0 raw-materal type of J f equpment s otherwse f job s processed at stage j on equpment k otherwse Objectve functons and constrants Plannng level N Mnmze ( F d ) CT 1 (1) Subject to. 1 5, nteger (2) 1 Q (3) 0 N (4) N P max( d ) 1 (5) F S ST P (6) ms L (7) N Maxmze Schedulng level K L U (8) 1 j1 j 218

5 Subject to. P P / Q/180 (9) (10) d d (11) PT PT (12) Q 180 (13) F S ST P (14) 1 t ST (15) jk 2 tjk F (16) jk N J (17) N Q M (18) M K L N 2 tjk F (19) 1 j1 k1 1 In the formulaton, key concerns of plannng and schedulng level are covered. In plannng level, Eq 1 consders the mnmzaton of total tardness and the tme spent on changeover. Constrant 2 defnes the weght of each producton order. Constrant 3 determnes the quantty for each order and constrant 4 confnes the boundary of number of tool changeover. Constrant 5 ensures the soluton space. Constrant 6 defnes the fnshed tme. Constrant 7 guarantees that each order must be processed. In the schedulng level (1 st stage), Eq. 8 concerns the maxmzaton of equpment utlzaton n each stage. Constrant 9 defnes the processng tme n a specfc stage wth an equpment. Constrants 10 to 13 reflect the relatonshp and nteracton wth plannng level. Constrant 14 unravels the fnshed tme for each job. Constrant 15 and 16 ndcate that the real-tme producton actvtes could be ensured to execute the planned and scheduled results strctly. Constrant 17 ensures that every job can be processed and 18 guarantee the satsfacton of product quanttes ordered from customers and certan level of WIP. Constrant 19 guarantees the delvery date of all producton orders. Theorem 1. f N P max( d ), then k, 0 k N k, F d Proof. Let PO { P, P,... P } opt 1 2 N denotes an optmal sequence from the model, then ( P d 1 1 ) 0, k ( P P d ) 0, 1 P d F S ST P F P k, 0 k N When N P max( d ) 1 k P d 0 1 k F d

6 Ths theorem ndcates that f the total processng tme s bgger than the maxmal value of due date. At least one of the producton orders must be delayed. Theorem 2. Let P P / Q/180, then P P, Q Q. Q /180 Q /180 Proof. Let a producton order PO splt nto /180 Q batches (a batch s a job). Each batch has Q 180 and the processng tme s P P / Q/180. If Q 180, /180 1 Q then P P, Q Q ; If Q 180, /180 2 Q P P / Q/180 Q /180 P P 1 Then, Q Q s obvous. Q /180 Theorem 2 unravels the relatonshp between a producton order and ts related jobs. The total processng tme of jobs splt from an order s equal and less than a processng tme of the order. Whle, the total quantty of tems from batches s equal or larger than the quantty of products n an order. That means, n real-lfe manufacturng cases, producton partes always produce a lttle bt more tems than the requrements from the customers snce there are some defects and emergency orders. Keepng a certan level of WIP s necessary and sgnfcant n such stuatons. Table 1: Producton Orders PO P d Q PT A B C D A C C 4. Experments and Analyss Experments of numercal studes are carred out n ths paper. The experments are based on two stages n the hybrd flow shop. Stage 1 has two equpment and stage 2 has three equpment. There are 7 producton orders wth ther attrbutes, as shown n Table I. Accordng to our real-lfe concerns, we gnore the setup tme due to the specfc equpment features. Thus, processng tme s the crtcal nput varables n these experments. Table 2: Experment Results Approach Sequence Tardness CT Prorty-based Materal-based SPT Proposed Model A. Producton Plannng Level The purpose of ths experment s to compare the proposed model and decsons based on some rules whch are used n our collaborators. Two key factors are concerned. One s total tardness and one s total tme spent on 220

7 changeover of equpment tools. The results are shown n Table 2. From Table 2, the results obtaned from the proposed model are compared wth three approaches used n the producton department n the case company. These approaches are prorty-based rule, materal-based rule and shortest processng tme (SPT) prncple. Prorty-based rule sequences the producton order accordng to ther weght/prorty ( ). If the weght s equal, SPT wll be used. Materal-based rule groups the materals wth same attrbutes and combnes SPT prncple. SPT sequences the producton orders accordng to ther processng tmes. Due date wll be consdered secondly under the stuaton of equal processng tmes. Usng four methods to solve the producton plannng problem, the sequenced results are lsted n Table 2. Each sequence s evaluated by two key factors: total tardness and tme spent on changeover of equpment tools. The four methods are examned wth the total tardness 87, 94, 49 and 2 respectvely as well as total tme upon changeover of equpment tools are 32, 24, 24 and 32 (Here, a unt of tme for equpment tool changeover s 8). Compared wth the three approaches, the model proposed n ths paper outperforms them n terms of total tardness as well as sum of total tardness and total tme upon changeover of equpment tools. Table 3: Jobs for Schedulng J P d Q PT A A B B B C C D D A A A C C C C C C Fgure 2: Smulaton Results n Gantt Chart B. Producton Schedulng Level Table 3 demonstrates the splt jobs from the producton orders lsted n Table 1. There are 18 jobs each of whch contans the standard quantty that s 180 peces. After releasng from producton plannng, the jobs n the real-tme job pool for 1 st stage are sequenced as: The objectves concerned n schedulng level are maxmzaton of equpment utlzaton and makspan. To ths end, real-tme job pool n stage 1 releases jobs to job pools for ndvdual equpment gven some dspatchng rules lke same materals grouped mechansm etc. Each equpment should process jobs as soon as possble so that next stage can get the jobs mmedately. Based on the workng mechansm, the model soluton s shown n Fgure 2 whch ndcates the optmal sequences that each equpment processes whch job at what tme n a Gantt Chart form. In Fgure 2, the red rectangle represents the tme spent on equpment tool changeover when nstallng and unnstallng tools wth dfferent materals wthn jobs alternatvely. The other type of rectangle represents the processng tme of a job n a specfc equpment. From Fgure 2, the total tme spent on the changeover s 64. At 221

8 the frst stage, jobs are processed by two equpment after ther groupng wth several crtera. At the second stage, jobs fnshed at stage 1 enter the real-tme job pool whch assgns jobs to dfferent machnes. Fgure 3: Coordnaton usng a real-tme Kanban C. Coordnaton Usng A Real-tme Kanban Plannng and schedulng are coordnated through a real-tme Kanban whch ntegrates the real-tme RFID executon data from manufacturng stes and upper decsons lke plans and schedules. The coordnaton mechansm s based on three key elements: machne layout, jobs layout and jobs progresses. Machne layout uses a TreeGrd component to dsplay the equpment structure n dfferent manufacturng stages. It ntends to facltate planners and schedulers to revew the jobs arrangements for dfferent equpment groups/ndvdual equpment. Jobs layout utlzes a TreeGrd component to organze the jobs from real-tme job pools for a specfc equpment or group, for example, n dfferent workng days or shfts. Jobs could be adjusted here manually by end-users gven the real-tme stuatons from manufacturng stes. Jobs progresses use Gantt Chart to show the fulfllment of a job at an equpment wth certan materals. It enables decson makers (e.g. planners and schedulers) to real-tme montor the jobs. In case of dsturbances, for nstance, a delay occurs at a job, t gves warnngs so that affected partes are able to mmedately coordnate to adjust the plans/schedules so as to avod producton order delay. Through the approach, the plannng and schedulng acheve a real-tme and coherent; lkewse, the feasblty and practcalty of plans and schedules are more reasonable. Furthermore, the collaboraton wthn dfferent manufacturng partes s enhanced to a level that s graphcal and on-tme. 5. Concluson and Remarks Ths paper ntroduces a real-tme plannng and schedulng model n a RFID-enabled manufacturng envronment. Ths model s based on hybrd flowshop (HFS) and real-tme job pools whch am to combne the theoretcal and practcal aspects seamlessly n the real-lfe applcatons. A numercal experment study shows the feasblty and practcalty of the potental mplementaton of ths method. Experments results report that the proposed model outperforms over the rule-based approaches that used n the case company. Several aspects are sgnfcant n ths paper. Frst of all, ths paper sets up a model that consders the plannng and schedulng decsons nteractvely wthn the RFID-enabled real-tme plannng and schedulng. RFID technology s used to dentfy varous manufacturng resources that the model concerns about. Secondly, a real-tme Kanban s adopted to coordnate the plannng and schedulng level, thus, the gaps among plannng, schedulng and executon are brdged. That means the decsons lke plans and schedules could be real-tmely reflected on manufacturng frontlnes such as work-cell or equpment, whle, the real-tme statuses of frontlne objects are fed back to the decson-makng partes for supportng ther further assessment. Thrdly, ths proposed model concerns some practcal elements whch are pad crtcal attenton n ths case. Practcal partes lke planners and schedulers can easly understand and nterpret. Future researches wll be carred out n three dmensons. Frst, the processng tmes or standard operaton tmes (SOTs) are largely dfferent gven the ndvdual operator s skll level, shfts, dentcal machnes etc. Data mnng approach should be used for dscoverng the more practcal SOTs and ther key mpact factors. Second, after usng the more practcal SOTs, an mproved plannng and schedulng model should be establshed to 222

9 enhance the precson of plans and schedules. Thrd, the proposed model wll be realzed n a RFID-enabled advanced plannng and schedulng system whch s able to assst small and medum-szed manufacturng companes to mprove ther producton plannng and schedulng n the near future. Acknowledgment Authors would lke to acknowledge fnancal supports from 2009 Guangdong Modern Informaton Servce Fund (GDIID2009IS048), 2010 Guangdong Department of Scence and Technology Fund (2010B ), 2010 Natonal Nature Scence Foundaton of Chna ( ). Internatonal Collaboratve Project of Guangdong Hgh Educaton Insttuton (gjhz1005) Guangdong Industry, Schools and Research Insttutons Project (2010A ). Specal acknowledgement s gven to the Huaj Dengyun Auto Parts (Holdng) Co., Ltd. and the crtcal comments from varous techncans and collaboratve colleagues. References [1] Ouelhadj, D. and S. Petrovc, A survey of dynamc schedulng n manufacturng systems. Journal of Schedulng, (4): p [2] Pnedo, M. L., Schedulng: theory, algorthms, and systems2012: Sprnger Verlag. [3] Hvolby, H. H. and K. Steger-Jensen, Techncal and ndustral ssues of Advanced Plannng and Schedulng (APS) systems. Computers n Industry, (9): p [4] Chen, K. and P. J, A mxed nteger programmng model for advanced plannng and schedulng (APS). European journal of operatonal research, (1): p [5] Errngton, J. Advanced plannng and schedulng (APS): a powerful emergng technology. n IEEE Colloquum on Next Generaton I.T. n Manufacturng Coventry, UK: IET, 3/1-3/6. [6] Zhong, R. Y., et al., RAPShell for RFID-enabled Real-tme Shopfloor Producton Plannng, Schedulng and Executon. Proceedng of 42nd Internatonal Conference on Computers & Industral Engneerng (CIE 42), July, 2012, Cape Town, South Afrca., [7] Huang, G.Q., et al., RFID-enabled product-servce system for automotve part and accessory manufacturng allances. Internatonal Journal of Producton Research, In Press. [8] Verderame, P.M., et al., Plannng and schedulng under uncertanty: a revew across multple sectors. Industral & engneerng chemstry research, (9): p [9] Lu, B.H., R.J. Bateman, and K. Cheng, RFID enabled manufacturng: fundamentals, methodology and applcatons. Internatonal Journal of Agle Systems and Management, (1): p [10] Spekman, R.E. and P.J. Sweeney II, RFID: from concept to mplementaton. Internatonal Journal of Physcal Dstrbuton & Logstcs Management, (10): p [11] Chaudhur, R. and R. Shankar, RFID n retal ndustry: ntegraton to applcaton n Indan perspectve. Journal of Advances n Management Research, (2): p [12] Dutta, A., H. L. Lee, and S. Whang, RFID and operatons management: technology, value, and ncentves. Producton and operatons management, (5): p [13] Lee, H. and Ö. Özer, Unlockng the value of RFID. Producton and operatons management, (1): p [14] Günther, O., W. Klett, and U. Kubach, RFID n Manufacturng2008: Sprnger Verlag. [15] Ivantysynova, L. and H. Zekow, RFID n Manufacturng: From Shop Floor to Top Floor. RFID n Manufacturng, 2008: p [16] Nga, E.W.T. and F. Rggns, RFID: Technology, applcatons, and mpact on busness operatons. Internatonal Journal of Producton Economcs, (2): p [17] Da, Q. Y., et al., RFID-enable Real-tme Mult-experment Tranng Center Management System. Internatonal Journal of Advanced Scence and Technology, : p [18] Lee, W. B., B.C.F. Cheung, and S.K. Kwok, Dgtal Manufacturng and RFID-Based Automaton. Sprnger Handbook of Automaton, 2009: p [19] Yn, S.Y.L., et al., Developng a precast producton management system usng RFID technology. Automaton n Constructon, (5): p [20] Da, Q. Y., et al. A RFID-enabled real-tme manufacturng hardware platform for dscrete ndustry. n Proceedngs of the 6th CIRP-Sponsored Internatonal Conference on Dgtal Enterprse Technology, Hong Kong, 2010, 66: p [21] Advances n Intellgent and Soft Computng Hong Kong, 66, : Sprnger. [22] Zhang, Y.F., et al., Implementaton of real-tme shop floor manufacturng usng RFID technologes. Internatonal Journal of Manufacturng Research, (1): p [23] Lu, W.N., et al., RFID-enabled real-tme producton management system for Loncn motorcycle assembly lne. Internatonal Journal of Computer Integrated Manufacturng, (1): p

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