Graells, Espuña, Puigjaner - 1992 - Optimization of process operations in the leather industry

7/27/2019 Graells, Espua, Puigjaner - 1992 - Optimization of process operations in the leather industry

1/8

OPTIMIZATION OF PROCESS OPERATIONSIN THE LEATHER INDUSTRYM. Graells, A. Espufiaand L. Puigjaner

Universitat Politecnica de Catalunya, Chemical EngineeringDpt,E.T.S.E.I.B., Diagonal 647, 08028 BARCELONA

ABSTRACTIn this work, the solution to the optimization of process operations in the leather industry isstudied in its full complexity. The interest of this study is twofold: for one thing, it shows howscheduling optimization problems can be solved at the industrial scale level, and on the otherhand it represents an step forward in creating a general framework to deal with resourceconstrained acyclic production scheduling problems for practically sized problems. Theoptimization procedure uses hierarchical decomposition of the global MINLP problem intosubproblems, that take into account the acyclic problem present in the short-term periods, whichimplies eventual task rescheduling in order to minimize loses in resources resulting from thedesired uniform production loads. Results from several case studies are discussed. In all casesvalidation of the results obtained has been made through the extensive use of actual plant data andthe experience provided by leather manufacturing experts.

KEYWORDSBatch process; optimization; scheduling; flexible manufacturing.

INTRODUCTIONProcess industry is highly oriented towards specialities manufacturing, with special emphasis insuch industrial sectors that allow for product diversification from limited shared resources. Thiscalls for flexible manufacturing structures and discontinuous or semicontinuous mode ofoperation. The low efficiency of this kind of processes can be enhanced through planning andintegration of all available resources (resources logistics) in already existing plants, and flexibledesign (process logistics) in grass-roots installations. The efficient design and operation of suchplants results in a complex optimization problem due to market fluctuations that affect demandand consequently the right product combination to be manufactured by the equipment units at anytime interval.For one thing computer-aided scheduling is gaining adepts as far as traditional industriesemploying batch and semicontinuous operation see it as an extension of current practice. Inresponse to these needs a number of firms are marketing commercial scheduling systems whichassist in the management of scheduling information (Musier and Evans, 1990). Surprisinglyenough, scheduling optimization is not usually contemplated in commercial packages, the mainreason being the difficulty in providing a general purpose simulator able to model a broad rangeof process industries and thereby making it (in terms of hardware and software) too costly to thesmall/medium enterprise due to the variety and specificity of these types of production facilities.However, it should be understood that optimization requires that one way or the other, plantmodeling must be done at the highest possible level of accuracy. This is a hard reality thatrequires further investigation, since it is not clear, neither in economical nor in technical grounds,that a more powerful interface incorporating object oriented structures capable of representing

8221


2/8

S222 European Symposium on Computer Aided Process Engineering-ISOAKINGUNHA\KINGUMINGPl.ESHINGDWMlNGBATINGPICKUNG

TCmpV'lyrc lAd hymldlty cQOkg1cd .tgre

C TANNING

STORE: TANNED SKINScu.SSII'IED fORDIFfERENTUSAGES

Rl!TANNINGDYEINGfATUQUORlNG

IAIRHANGINGI, I HOTAIRTUNNEl.DRYER). ORTING

WNATEUSsETRIMMING

DRY SHAVING

IMANUALTACKING!' IAlTl'OMA11CTACKlNG !.SPRAYINGG

DRYMlU1HG POUSJUNGA NIFlEX

(*) Productdependent optionaloperationFig. 1. General PlantSchemeTable 1. Product 2 recipe

PRODUCT2: 'GLOVING' BATCH: 3200SKINSTIMING

Operation Equipment Productivity Man-power Prep. Load. Oller. Unload. Clean.Sammying 2 200 x 2 1 x 2 0.1 (0) 8 0.2Initialshaving 3 150+210 1+ 1 0.15 8.9 0.3

RDF 4 3200ISh . 1 0.5 0.5 (2) 8 0.5 (2) 0.3Horsingup 5 400 1 .- 8 0.2Sammving 6 200x2 1x2 O! S 0.2Air drvine 9 4200124h. 0 .. 7.1 (I ) 24 3.6 (I ) 0.2Molissa 11 400x2 2x2 .. 4 0.1Schaedel 12 120 1 0.2 26.7 1Trimmin2 14 l00x4 Ix4 .. S 0.3Polishinz 23 150 I 0.2 21.3 0.8Finiflex 24 200 1 0.2 16 0.6

(0) Extraman-powerneeded.Productivity is given in numberof skinsper hour and specifying theproductivities of thedifferent

suitableequipmentworking inphase.Operation timeis specifiedwhenit is independent of batchsize.Man-power is alsogivenin workersper equipmentfor all suitable equipmen t.


3/8

European Symposium on Computer Aided Process Engineering-l S223any plant configuration should be a better alternative to specialized process engineering housesmaking specific plant applications of standard low cost scheduling optimizers ready forinterfacing with the specific applications.It is our feeling that, while most academic work focus in finding exact methods resulting inoptimal solutions to the general resource constrained scheduling problem hoping that cheaper andfaster hardware will make them capable and attractive to solve industrial size problems, paralleleffort should be devoted to build the adequate methodology to find the best solutions for specificscheduling problems associated to specialities manufacturing. We are convinced, that a numberof specialized sectors will benefit more from this specific approach that may prove to be uniquein many cases, while the academic exercise should also take profit of the rigourosity and detailthat the specific problems convey, by incorporating the same philosophy into higher levels ofabstraction (Puigjaner, 1992).In this work, the leather industry has been chosen to illustrate the kind of scheduling problemposed by the specialities manufacturing. The leather industry falls under the category of "jobshopnetwork" production facility with specific connotations. That means in fact that most of theequipment can be shared by different final products although their individual recipes may varyfrom product to product, and that the production lines for each one may follow different pathsalong the network depending on the availability of the resources required at the right time.A specific characteristic of this type of industry is the shared use of limited resources at discretelevels. Such is the case of labor; that several operations may share different tasks at differenttimes. As a consequence, the production scheduling problem becomes acyclic (aperiodic)originating in an increased source of bottlenecking situations. Production planning becomes alsospecially complex due to the multi-site characteristics of the production facilities that require theadequate management of utilities at a particular site, the allocation of production to tasks, and theinter-site transportation requirements.The solutions offered to the optimization of process operations in the leather industry are indeedspecific to this type of industry but the philosophy used can be the logically transferred to otherindustrial sectors. Toward this end, several case studies have been analyzed and some of theresults obtained are commented looking for a broader application of the strategies presented here.

PROBLEM DEFINITIONThe leather industry constitutes a trade-union manufacturing sector where production quality is ata premium and is strongly influenced at every production step by product to product specificrequirements and available raw materials characteristics. Even in highly automated plants, skilledlabour is required, that may be critical at some production stages. Therefore, sharing labourresources along the production chain becomes of paramount importance to reduce manufacturingcosts and constitutes a complex constraint in the scheduling optimization process.This can be better understood by taking a look to the process operations (Fig.1). In general,pickled skin is the actual raw material which is externally acquired and stored at controlledtemperature and humidity conditions, since pickling and other preliminary operations (soaking,unhairing, liming, fleshing, deliming and bathing) are not carried in the plant, but are specific ofthe external supplier origin. Tanning is the first stage common to all product recipes, which isfollowed by the classification stage.However these stages are not taken into account in the making of the actual production planning;production rate of both stages together is large enough to assume that they do not causebottlenecking problems, and so tanned and sorted skins are always available from its intermediatestorage. As it is indicated in Fig. 1, most of the following process operations are productdependent optional steps. Under such circumstances, batch size cannot be specified beforedeciding each skin destination as well. Actually the batch size of each product is defined by thelowest capacity of the equipment or groups of equipment amongst those used in the subsequentoperations established in the recipes of each product.


4/8

S224 European Symposium on Computer Aided Process Engineering-IIn this work, detailed plant simulation has been carried out that cover the different manufacturingstages from pickled skins to "finiflex'' operation, totalling 25 steps that are differently shared orby passed according to the individual recipes of the final products. The following workinghypotheses have been assumed, based on industrial practice: Every production step is carried out in a batch/semicontinuous equipment. Set-up and clean-up times are considered for each unit. Task transfer mixed policy, zero wait (ZW)/no intermediate storage (NIS), is permitted. Sharing manpower is the main constrained resource. The jobshop network configuration is considered. Batch processing advancement or postponement is allowed.Then, the optimization of process operations is attempted by minimizing the total elapsed timerequired to produce all products in the required quantities (makespan) for a specified time horizonand under particular demand and utilities constraints. While the time horizon contemplates acontinuous period of time, flexible stop and starting production times can be introduced,according to manufacturing needs.Problem Formulation

(2)< _1+ 1B1

The problem of optimum production scheduling in the specific case of the leather industry can beformulated as an MILP problem for minimizing the makespan with the no intermediate storageand ZW policies and other appropriate constraints mentioned earlier. In order to derive thecorresponding models, mixed product campaigns will be considered with sequences of n = 1,2,.. . , N batches of i = 1, .. . , I different products. The assignment of batches consisting of onlyproduct i to the production sequence can be denoted by a binary variable Yin and the constraints:I

Yin E {O,l} V i,n L Yin = 1 V n (1)i = 1

The number of batches of each product i, with batch sizeB], has to meet the demand D]:N N

Ni = LY in ~ ~ ~ Vi Ni = LY inn=1 1 n=1

Also, let us define the total number of batches N:1

(3)

Now, let us consider every task carried out at stage j, j=l, , M, constituted by five subtaskss, s=l, ......, S (S=5) which may include (Fig. 2) waiting time (TW) only after the operationsubtask.

5=1 8=2Set-up load 8=3operation TW s=4unload

T I jn T FjnFig.2.Set of substasks considered in every task starting at time TIjn and finishing at TFjn

Given the set of processing times tijs and the stability of intermediate product i at stage j, Xij,both are related by the equation:I S=5

TFjn=Tl jn+ LY in { Lt i j s+TWjnXi j} Vj,ni=1 8=1

(4)


5/8

European Symposium on Computer Aided Process Engineering- I 5225where:rw; ~ D v j ,n ; xij E {D, 1} V i,j (5)Time relationship between stages of successiveslots is obviously:

j = 1, .... , M; n=l, ...., N-1 (6)and the timerelation betweenconsecutivestagesof the sameslot is givenby:

ITl j+1,n = L Y in (Y ij TI ;+I ,n + ( 1 - Yij ) TF j+1,n-l) j = 1,...., M; n=l, .... , Ni =) (7)which takes into account the possibilityof recipescarriedout along Ji stageswith Ji $ M:

Y.. E {D,l}1J V i,jM

r, =I Yij sMj = )

(8)The batch or semicontinuous operationof the unit contemplated can bedescribedby (seeFig. 3):z.. E{D1}1J '

a)z ij= 1

V i,j

FI .In(9)

1 1 1 1 ~ l l l I l I l I lTl j+1,n Ff----

b)z = 01J

TI ~n U ~ Z ? 2 Z 2 ~ : ! I wmTl j+l,n ~ W-...' -----

Fig.3. Unit operation: a) semicontinuous operation; b) batchoperationI 1

TI;+I,n=L Yin ( 1-zjj ) { r i , + LtijS - ti ,j+l,l } +i= l s=1I~ y . . t. : {L.J In 1Ji = 13rr, + L tijs + rw., ij - ti ,j+l,l }s = 1 j = 1,.... , M-1; n =1,.... , N(10)

Initial times are calculated by considering the preparation timesof all stagesof first slot:I j . i

TIll = max { L Y i1 ( t ijl -L(t ir2+ tir3) - till) } (11)J i=1 r=lHence, the makespanevaluationis given by :T t = max {TF jn}j,n (12)thatwill beminimizedunder the utilitiesconstraints.


6/8

S226 European Symposium on Computer Aided Process Engineering-l

N- l Ntagej = 1 L__ . . m ~ a - - - ~j = 21--" - 3 ~ ~ ~ ~ ~ ~ ~ ~ I ~ : ~- 1--j= 4

FigA. Makespan minimization effects on scheduling when Tt > TFMNThis formulation contemplates such cases as those shown in Fig. 4, where last scheduledproduct may not be necessarily the last produced, or the last production stage may not cover theend of the time slot.The calculation of utilities is made as follows. The contribution of consumption njsk to generalutility u pattern can be expressed by equations:IW njsku ( t) = ~ Yin' CO ijsku {8( t - T I ~ k s u ) - 8( t - T F ~ k s u ) } 'tI n,j,s,k,U1 =1 (13)

s . 1T I ~ s k U = rr, +Lt ijr + rw, .8(s-4) + OtijkSUr= ls - 1

T F ~ s k U = rr; +Lt i j r + TW jn .8(s-4) + Otijksur= 1

(14)

(15)

where: 8Ct) = { ? t ~ 0t> 0Kijsu = times utility u is used during subtask ijs; k = 1, ... , KijsuCOijsku = utility u power used the k!h time during subtask ijsOtOijsku = starting time, related to subtask start, of consumption WijskuOtijsku =finishing time, related to subtask start, of consumption Wijskuand utilities demand patterns are described and bounded by:

M N S = 5 Kijsu

w, (t) =L L L L Wnjsku(t) s W: axj= l n = l s= l k= l _00 < t < 00 'tIu (16)RESULTS AND DISCUSSION

Plant simulation and scheduling optimization has been carried out in a leather manufacturingindustry located nearby Barcelona. Several studies have been realized to demonstrate and validatethe potential of the algorithm and tools presented in this paper. Here, a demonstration case studyis summarized that illustrates some of the relevant features of this study, and the results obtainedare discussed.Batch size of each product is defined by the lowest capacity of the equipment or groups ofequipment among those used in the subsequent operations established in the recipes of eachproduct. That means a batch size of 3200 skins for each of the three given products whichcorresponds to the retanning tank maximum capacity.


7/8

European Symposium on Computer Aided Process Engineering-l 5227A sample recipe for one of theproducts is given in Table 1.Once provided the data supplied by each product recipe, the problem to be solved consists infinding the best production scheduling strategy to satisfy the expected demand, taking intoaccount the limited available resources in the existing plant. In this case the otherwise typicalrestraints imposed by the use of general utilities such as electricity, water and steam are unlikelyto cause overlapping problems. Therefore they will not be taken into account. However, thisassumption cannot be extended to manpower, which is restricted to 19 workers and imposes aserious limitation in theoverall process operation optimization.Demand estimation in number of skins is the following for each of the three products:

DO) = 180000 D(2) = 160000 D(3) = 125000and a time horizon of 4000 hours (approximatelyhalf a year) is available to satisfy such demandsat a working rate of 24 h./day.Initial simulation of operations under the restrictions imposed for all products and sequences ofproducts leads to campaign generation, evaluation and selection of the best ones to be scheduledat the production planning stage.Simulation of campaign [1,3,3] after satisfying different manpower restrictions is shown in Fig.6. The campaign notation means a sequence of a batch of product 1 and two batches of product3, and 20[1,3,3] means twenty times such a campaign as it is plotted in fig.6.

ICAMPAIGN ELABORATION

Fig. 5 Flowchart of the modified algorithm

As it can be seen, harder constraints(reduction of manpower from 23 to 18workers) mean the loss of batch sequenceperiodicity which is needed to evaluateaccurate changeover times and makeproper scheduling. This situation is likelyto arise whenever batch processing timesgrow larger, leading to eventual collapsebetween batches in terms of utilitiesrequired or batch schedule advancement.The algorithm has been convenientlymodified, including heuristic procedures,to cover these situations and to achieveoptimum scheduling (Fig.5).The resulting production plan based on thiscampaign elaboration and selection is thefollowing:

1[3] + 13[2] + 19[1,3,3] + 37[1,2]As a consequence of this production plan asurplus time of 32 hours is obtained withthe present demand satisfied.These results show how the aperiodicconstrained scheduling problem, caused byshared limited resources can beconveniently solved by an efficientscheduling system that minimizesmakespan under NIS and ZW policies inmixed product campaigns.


8/8

S228 European Symposium on Computer Aided Process Engineering-l

a)

b)

c)

c M P I N I C I 0 7;; W E T - B L U e :w S A M M Y I N G" S H A V I N CLOAD" RD "0 U N L O A DW I - I Q R S I N OSAMMYINOS ET. O UTT U N . D R Y .I -OAO. ,R DR Y .CONDIT .U N L O A QM O L I S S AS C H O E D E LLUNATEU .TRIMMING8 U F F I N GSHAVINGM A N . T A C .

~ O L l S H .AUT.TAC.S P R A Y I N GGLAZINGCONTILUXLOADMI l -UNOUNLOADI""INIFLEX

0 '00 2 00 '00 400 500 600 7CO 900'" ~ mm W 24 .0 -_ ..16 .0; 12 .0

I0. 0 ~ n.0 0 '00 200 '00 400 500 600 700 ao oT I ... E ( H , )C M P M E J 0 R .7

z W E T - B L U EW S A M M Y I N G" SHAVINGLOAD" RO '0 U N L O A DW " 'ORSINO

S A M ' - " Y I N Cs c-r. OUTTUN .DRY .LOADAI R DRY.CONDIT .I . J N L O A OM O U S S ASC"'OEOELL U N A T E U .TRIMMINGBUFFINGSHAVINGM A N . T A C .F"OLISH.A U T . T A C .SF>RAYINGGLAZINGCONTILUXLOA.DMI l -UNOU N L O A DnNIFLEx

0 '00 2 00 '00 4 00 500 600 70 0 80 0 I'" b ~ ~ ~ ~ - IW 24 . 0 I- ' 6 . 0 .. _--.-----_. I" 12 .':J I6. 0 I:J.O i0 '00 200 '00 4 00 500 600 700 ' 300

T I ... E ( H )C M P M E J 0 R .7;; W E T - B L U e :

W SAMMYING" SHAVINGLOAD" RO '0 U N L O A DW l-oORSINGS A M M Y I N GSE:T. OUTT U N . O ~ Y .L.OADA I" D ~ Y .C O N D I T .Ul' lL.OAOMOL.ISSASCI-lOE:DEL.L.:JNATE:U.T R I M M I N GBUF"Il ' INGSI- lAVINGMAN.TAC.

~ O U S ) o l .AUT.TAC.

S I = ' ~ A Y I N GQ L A Z I N OCONTIL.UXL.OADMILL.INGUNL.OADIl 'INIF'L.E:X

0 '00 2 00 '00 4 00 500 600 700 ao o24 . 0 II!!.O - - - - - - - - - ~ - ~ - - - ~ - : ~ ~ ~ ~ : - ~ i i \ i J t - - - ~ - l. -----... _..'2.0

0. 0 ~ . ~ , ~ "K 1 ~ ' ' J , I ~ 1 I' I " ~ 1 ' r V ~0. 0 0 '00 2 00 4 00 500 000 700 ao o

T I ... E ( H r )

AcknowledgementsPart of this work wasperformed under theauspices of theCIR IT -C ICYT(Project n? QFN894006). Also JOULEsupport (Contract nQ43) is thankfullyacknowledged.REFERENCESMusier, R.F.H. andL.B. Evans (1990).Batch ProcessManagement,Chemical EngineeringProgress. 86, 66-77.Puigjaner, L. (1992).Improving the Designand Management ofProduction Chains. InMulti SupplierOpera t ions . (inpress).

Fig. 6.a) Unrestricted case.b) Restricted caseresulting inundefined cycletime.c) Aperiodicscheduling.

Graells, Espuña, Puigjaner - 1992 - Optimization of process operations in the leather industry

Documents

Transcript of Graells, Espuña, Puigjaner - 1992 - Optimization of process operations in the leather industry