The damaged barcodes cause problems particularly in production and warehousing, because in many cases
their identification with conventional barcode readers is not possible. Here we deal with only a special part of
damaged barcode identification. In this paper a prediction system is presented, that analyses the regularities
of the material flow. The application of such a system can be an effective help with the reconstruction of the
incomplete visual information

Bohács, Gábor

Kulcsár, Béla

English

Periodica Polytechnica (Budapest University of Technology and Economics)

PERIODICA POLYTECHNICA SER. TRANSP. ENG. VOL. 27, NO. 1–2, PP. 113–119 (1999)COMPARISON OF THREE DIFFERENT METHODS IN THEPREDICTION OF THE MATERIAL FLOW IN A MATERIALHANDLING SYSTEMGábor BOHÁCS and Béla KULCSÁRDepartment of Building and Materials Handling MachinesTechnical University of BudapestH–1111 Budapest, Bertalan L. u. 7-9, HungaryTel./Fax: 0036-1-463-3059e-mail: bohacs-gabor@eagt.bme.hu, kulcsar-bela@eagt.bme.huReceived: Dec. 10, 1999AbstractThe damaged barcodes cause problems particularly in production and warehousing, because in manycases their identification with conventional barcode readers is not possible. Here we deal with onlya special part of damaged barcode identification. In this paper a prediction system is presented, thatanalyses the regularities of the material flow. The application of such a system can be an effectivehelp with the reconstruction of the incomplete visual information.Keywords: barcodes, prediction, material handling systems.1. IntroductionNowadays the flexible manufacturing and logistic systems require real time pro-cessing of even complex information. This can be fulfilled only with the use ofappropriate software and hardware tools. In our current research we are engagedin the reconstruction of incomplete information of barcodes in a complex materialhandling system. It seems to be necessary, because the information of the bar-code comes also forward as control parameter for the material handling system, somissing information often causes functional problems (KULCSÁR, 1998). Barcodedamaging happens particularly in the manufacturing and warehousing processes.The damage means that a part of the label becomes unrecognizable.In this article we will be dealing with only a part of the results. In our currentresearch we try to reconstruct the missing information with an image processingsystem based on a CCD matrix camera. Next we summarize the functioning of thesystem, that recognizes all the visual information. The experiments implementedin the laboratory of the Department of Building and Materials Handling Machines.The hardware was installed at a certain point of a roller conveyer system (Fig. 1).The unit loads with barcodes travel in the conveyer system. The requirementsagainst the processing software are the following:• The decoding of the information has to pass off quickly, in order that the114 G. BOHÁCS and B. KULCSÁRFig. 1. The experimental systeminformation becomes available before the unit reaches the next branch in theconveyer system.• If a character of the code damaged so much that more than one character suitsto the damaged one, then the output must consist of all the codes that fit.The implemented system using fuzzy inference fulfils all the requirementsabove.So, in this paper we present a prediction system that helps the image processingsystem by the decoding of the information. Next the necessity of the predictionsystem will be described.2. Necessity of the Predicting SystemIn this research the experimental conveyer system is regarded, as a material handlingsystem in a manufacturing facility. The independently driven parts of the conveyerare marked withG1, G2 . . . . The unit loads may travel in only one direction, andthere can be only one unit load in each driven part. Certain points of the conveyerare regarded as workplaces (marked withM1, M2 . . .) with deterministic processtimes. Let’s implement various production programs in the system. Shortly afterthe beginning of the process, the flow of the unit loads becomes chaotic, because ofthe waiting times at a workplace, and the interlocking in the conveyer. For exampleif a productA has to be manufactured atM2 but productB has not, then a unitB thatfollows unit A, will overtakeA. That also means, that the CCD camera will observea chaotic material flow. Despite it, because of the fixed process and transport times,there will be some regularity in the material flow. That makes reasonable the usingof a prediction system. The output of the prediction is the most likely code for theMATERIAL HANDLING SYSTEM 115following unit load. That output will be taken in consideration by the determinationof the output of the image processing system.Therefore the reliability of the whole system improves. Certainly the outputof the image processing system is always “stronger” than the prediction.The training data creation for the prediction system will be produced using aSiman simulation software. The codes are three character long Code 39 barcodesthat change after every production step. The first character is the product identifier,the second is the destination code and the third determines how many productionsteps have been completed. The codes of the unit loads are the members of the timeseries used by the prediction models.In this paper we consider three different production programs:1. Only one product is manufactured (maximal number of unit loads in thesystem: 7, number of production steps: 4).Production sequence:M2 − M4 − M3 − M12. Two products are manufactured (maximal number of unit loads in the system:6, number of production steps: 5).Production sequence for product 1:M1 − M4 − M3 − M1 − M3Production sequence for product 2:M3 − M1 − M2 − M4 − M33. Three products are manufactured (maximal number of unit loads in the sys-tem: 10, number of production steps: 3).Production sequence for product 1:M3 − M4 − M1Production sequence for product 2:M3 − M2 − M4Production sequence for product 3:M1 − M2 − M3Production sequence for product 4:M2 − M1 − M4Next we survey the different models used by the prediction.3. Predicting with AR ModelsThe autoregressive schemes (KENDALL – ORD, 1990) are a very important classof linear time series models. It has been very successively used in the time seriesforecasting.A pth-order scheme may be written as follows:y = δ + a1yt−1 + a2yt−2 + . . . + ap yt−p + εt . (1)The model is denoted withAR(p), and thea1 . . . ap coefficients are the autoregres-sive coefficients.δ is a constant term, andεt is called the error term. The goal isthe determination of the autoregressive coefficients. For these time series we usedthe standard least squares estimate method (JANG, 1997). The advantage of thismethod is the relatively low computing time. The only question is, which order tochoose for the model. It depends certainly on the process itself. There are methodsbased on the autocorrelation and partial autocorrelation functions to estimate theorder of the model. We have tried to determine the order this way, but the obtainedfunctions were not appropriate for this purpose. That gave an early warning thatthe AR models will not suit very well to the time series.116 G. BOHÁCS and B. KULCSÁR4. Predicting with ANFISThe ANFIS is a very well known neuro-fuzzy network (JANG, 1993). The modellingof various nonlinear systems has very successively employed it. This networkimplements fuzzy if-then rules with adaptive parameters. The ANFIS structure isshown inFig. 2. In this example network, there are three different barcodes:K1,K2, K3, four arbitrary chosen fuzzy rules, and two input variables:ft−1, ft−2 (theprevious two observations). The node functions for this case are described below:Fig. 2. ANFIS structureLayer 1 determines the values of the membership function. In this case themembership function (µA) has only two values:µi j = 1, if ft−i = K j and µi j = 0, 1 otherwise. (2)Layer 2 multiplies the incoming signals and sends the product out. These productsare called ‘firing strengths’ of the rule. Layer 3 implements normalization: eachfiring strength is divided by the sum of all firing strengths. The node function ofLayer 4 for thei th node:O4i = w′i(pi ft−1 + qi ft−2 + ri ). (3)The parameters (pi , qi , ri ) are called consequent parameters. In this case theoptimizing will change these parameters. Layer 5 computes the sum of the overalloutput. The training of the parameters is carried out as batch learning, using thesequential formulas of the least squares estimation (JANG, 1997).5. Predicting with CANThe CAN (Connection Analyzing Network) is an own developed one, that is verysuitable for predicting time series containing complex information. The architectureis shown inFig. 3. In this case the nodes correspond to the certain barcodes passingby the camera. Every node implements both input and output functions. The ‘hxy ’MATERIAL HANDLING SYSTEM 117Fig. 3. CAN structureparameters are functions (called: weight function) and not real values as usual bythe neural networks. The system also uses ‘pointers’. The pointer points to a valueof the ‘t ’ axis of a weight function. The value of a pointer increases from step bystep (0, 1, 2 . . .). The training can be carried out both off line and on line way. Thei th step of the training consists of the following procedures (in this stepfi = Kx ,maximal node number:N):1. Creation of a new pointer for allhxy (y : 1..N) functions.2. Increase of the values of thehyx (y : 1..N) weight functions by one at thepoints shown by the belonging pointers.3. Increase of the value of all pointers by one.The 1st and 3rd step of thei th step of testing is identical with the trainingphase. In the second step, the output of each ‘x ’ node is computed by summing thevalues of thehyx (y : 1..N) weight functions at the points shown by the belongingpointers. The output of the whole network corresponds to the identifier with thebiggest output value.6. Experimental ResultsWe have analyzed each production program with all three methods. In order tocompare the results of the various methods we use the ‘number of correct answers’(NOCA). The NOCA increases step by step if: round(outputmethod) = factual, oth-erwise remains unchanged. In words the NOCA counts, how many times is thedifference between the output and the actual code smaller than for any of the othercodes.The results are summarized inTable 1.The AR models give good results only for the simplest case (1st program),therefore it doesn’t seem to be suitable for predicting so complex systems. Other-wise the result of the AR scheme depends very much on the order of the model.118 G. BOHÁCS and B. KULCSÁRTable 1. Experimental resultsProductionprogramnumberNumber ofobser-vationsNOCAARNOCAANFISNOCACAN1 2508 2231 2114 22462 577 135 559 5213 685 123 140 316Fig. 4 shows the NOCA for the 1. production program. It can be seen, that a higherorder AR model may give worse results than a lower one. We could not find anyconnection between the art of the process and the order of the model.Fig. 4. NOCA for different order AR modelsThe ANFIS models give very good results for the first two cases. That makesthis method suitable for this research. The only disadvantage of the implementationof this system is that the automatic construction of such a network is not possible.Let’s suppose that the number of occurring barcodes is 7, and we would like toregard the previous 20 observations. That would make 720 rules by automatic rulecreation. This great number of inputs is necessary, because a single change inthe input has got a long-term influence for the following codes. Therefore beforetraining only the most significant rules must be chosen. The number of these rulesis certainly not obvious from the system structure. To determine these rules we needknowledge of the whole teaching data and a separate program. So the parametersof the ANFIS structures are as follows:• Case 1: for 20 previous data: 21 rules• Case 2: for 20 previous data: 18 rules• Case 3: for 3 previous data: 52 rulesMATERIAL HANDLING SYSTEM 119The third case is so chaotic, that even for a low number of inputs, the imple-mentation of at least 52 rules is reasonable. So the subjective factor is unfortunatelynot avoidable.In general we can say, that the best results are given by the CAN network. Thegood results are due to the new architecture (every node can be input and outputas well), and the application of weight functions instead of single real numbers.It has also a big advantage: by the determination of the structure no subjectiveinformation is needed, the construction happens automatically. The CAN has alsoa further advantage, the computing time. The determination of weight function inCAN took only some minutes (for ANFIS the determination of parameters took1.5-6 hours). Therefore the CAN network seems to be the most suitable for ourfurther research, but its application for other purposes is also thinkable.References[1] JANG, J. (1993): ANFIS: Adaptive-Network-Based Fuzzy Inference system.IEEE Transactionson Systems, Man, and Cybernetics. Vol. 23 No. 3 pp. 665–685.[2] JANG, J. – SUN, C. – MIZUTANI , E. (1997): Neuro-fuzzy and Soft Computing, Prentice-Hall,ISBN 0-13-261066-3, Upper Saddle River.[3] K ENDALL , M. – ORD, K. (1990): Time Series, Edward Arnold, ISBN 0-85264-295-4, London.[4] K ULCSÁR, B. (1998): Ipari logisztika,LSI Oktatóközpont A Mikroelektronika AlkalmazásánakKultúrájáért Alapítvány, ISBN: 9635772424, Budapest.

COMPARISON OF THREE DIFFERENT METHODS IN THE PREDICTION OF THE MATERIAL FLOW IN A MATERIAL HANDLING SYSTEM

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COMPARISON OF THREE DIFFERENT METHODS IN THE PREDICTION OF THE MATERIAL FLOW IN A MATERIAL HANDLING SYSTEM

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