Modeling of Biological Manufacturing Systems for Dynamic Reconfiguration_uremovic

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Modelling of Biological Manufacturing Systems for Dynamic ReconfigurationKanji Ueda' (2), ari Vaarioz, Kazuhiro Ohkura'

'Department of Mechanical Engineering, Kobe University, Kobe, JapanDepartment of Information and Computer Science, Nara Women's University, Nara, Japan

Received on January 3,1997

Abst ractThe concept of Biological Manufacturing Systems (BMS) aims at dealing with dynamic changes in externaland internal environments in product li fe cycle from planning o disposal, based on biologically-inspired ideassuch as self-growth, self-organization, adaptation and evolution. This paper describes modelling of BMSat afloor level and focuses on system reconfiguration. Computer simulation using the principle of self-organization shows that the proposed EMS model indicates adaptive behavior to the changes in productsdemands due to external environment and malfunction of manufacturing cells as an internal environment, andit provides the possibility of dynamic reconfiguration of manufacturing systems.Kevwords: Manufacturing Systems, Dynamic Environment, Genetic Information

1. In t roduct ionToday's manufacturing faces significant trends of culturaldiversification, ifestyle individuality, activity globalizationand environmental consideration. These trends can besummarized as growing complexity and dynamics inmanufacturing environments. It is not easy to deal with thecomplexity and dynamics [16] y existing ideas with top-down type control of centralized systems, so that manynew ideas have been proposed for next generationmanufacturing, such as Fractal Company [15],HolonicManufacturing [7], Random Manufacturing [4] andSustainable Industrial Production [11.Biological Manufacturing Systems (BMS) was proposed[9,1 1 1 as a next generation manufacturing system concept.BMS aims to deal with non-predeterministic changes inmanufacturing environments based on biologically-inspired ideas such as self-growth, self-organization,adaptation and evolution. It also covers whole productlife-cycle from planning o disposal. EMS[lo-141as beendeveloped in connection with new fields of computerscience such as Evolutionary Computation and ArtificialLife [6], nd it has also been involved in a couple ofresearches including Intelligent Manufacturing SystemsProgram[5]and CAM-International project. Beside these,BMS has been discussed in several articles [3, 8, 61.This paper describes modelling of BMS at a floor levelbased on evolution and self-organization, and it focuseson dynamic reconfiguration of manufacturing systems toadapt to changes in products demands and malfunction ofmanufacturing equipment, as an external and internalenvironment, respectively.2. ConceDt of BM SBiological organisms are capable of adapting themselvesto environmental changes and sustain their own life by

showing functions such as self-recognition, self-growth,self-recovery and evolution. These functions oforganisms are displayed by expressing two types ofbiological information, i.e. genetic information evolvingthrough generation (DNA-type) and individually achievedinformation during one's lifetime (EN-type). Unification ofbiological information with individuals makes livingsystems complex but adaptive.All system elements in BMS such as work materials,machine tools, transporters, robots, etc., are comparableto autonomous organisms, as shown in Figure 1 . Productsdevelop from raw materials expressing their own DNA-typeinformation. Manufacturing equipment "raises" he productusing mainly BN-type information. The product continuesto learn BN-type information as knowledge through itslifetime. As a result the product is able to deal withmalfunctions autonomously, to be easily recycled anddisposed, and to evolve its design for the next generationproducts [51.

M : Machine ToolR :RobotTi :Testing InstrumentTr :TransporterTo: ToolFig. 1 Concept of BMS at floor level

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3. Modellina of BMS 3.2 Self-oraanization-based ModelIn orderto realize BMS, there are at least two issues to beexamined. One is to develop the embedding technology ofbiological information in artifacts, and the other is tounderstand the general behavior of BMS, that is atheoretical aspect of the concept. Modelling of BMS hereis related to the latter. The modelling theory is closelyrelated to methods in Artificial Life such as GeneticAlgorithm, Genetic Programming, Evolutionary Strategy,L-system, Cellular Automata, Reinforcement Learning andImmune Networks rat her than symbolic approaches inArtificial Intelligence.In the following two concrete examples are given: onebased on evolution and one based on self-organization.On both these examples the emphasis is on the dynamicand adaptive behavior o environmental changes. The rawmaterials or jobs entering he manufacturingsystem is notknown beforehand. Neither the internal condition of thesystem is known at the time of input. Without theseinformation he production planning n the traditional senseis not possible. The production requests are carried by theinput (DNA-type information) and the manufacturingsystem must adapt in real-time to new requests. In otherwords, the input is looking for manufacturing equipmentcapable of processing its request, and the manufacturingequipment is looking for input they are capable ofprocessing. This concept also enables automatic recoveryof the system in the caseof malfunctions of manufacturingequipment. The manufacturing equipment is calledmanufacturing cell here by mapping from biological cell[13]. The production request received by the system is apart of external environment, while the state ofmanufacturing cell is a part of internal environment.3.1 Evolution-based ModelThere are two population n this model: work materials andmanufacturing cells. According to DNA-type information,the work material searches around looking or a cell with acertain objective. When it finds the objective cell, it tellsrequest for morphology change, and waits for cell's reply.The cell checks the material arrived within a certain time,and replies to the material's requests. After all thechanges in morphology are completed, the material goesto a final cell (goal).In he case of manufacturing cells, theirpositions are thesubject to be evolved. In the caseof raw materials, theirparameters defining the moving direction towardmanufacturing cells will be evolved. The mathematicaldescription of the model has been developed in [12].Evolution Strategies [2] are employed here for the group ofcells and the two kinds of work materials. The fitnessfunctions are as follows:f,. = C,,time + C,,request (1)fw i =C, , time+ Cw2gro w+Cw3collision+ ,, refused ( 2 )where, C ,, t ime, request , grow, collisionand refusedare weight constants, processing time, degree ofachievement, degree of growth, the number of collisionsand the number of being refused by cell, respectively.

In his model the movement of work materials is created byusing "attraction" fields [13, 14). By calculating theattraction forces in the virtual space we can createdynamic simulation where manufacturing cells "attracts"jobs being "sensitive" for the attractions. The DNA-typeinformation includes here the product descriptiondetermining what processes the product requires, i.e.,what kind of attraction fields to be sensitive. DNA-typeinformation could be given for the manufacturing cells todescribe the above attraction fields and their dynamics.These could be furthermore evolved similarly to theevolutionary model. The BN-type nformation could includehowthe transporter selects a particular machine, and howthis selection can be reinforced by experience.4. Simulation Results4.1 Simulations bv Evolutionarv ModelThe simulation model employs two kinds of product,WORK1 and WORK2, six kinds of cells 1 to 6 of tw odifferent processing types, A and B, with three levels ofprocessing speeds, high, medium and low, and a goal cell,as shown in Figure 2. The process sequence, i.e., DNA-type information for WORK1 is A ->B -> goal, and that forWORK2 is B->A -> goal.

~~manufacturing processingcell No. type1 A2 A3 A4 B5 B6 B7 goal

processingspeedhighmediumlowhighmediumlow--_-

Fig. 2 Specifications for manufacturing cellsCase 1: Change of External EnvironmentThe production request is given as external environment,but it changes. The simulation consists of serial requestsnumberedR1 and R2. These requests changes every 100generations in a sequence R1 >Ft2->R1->R2.. . The firstrequest R1 consists of WORKl andWORK2, both6 units.The second request R2 consists of WOR Kl 18 units andWORK2 2 units.Figure3 shows an example of simulation result, indicatingthe rearrangement of manufacturing cells and the path ofworks. WOR Kl and WORK2 start from the upper left andlower right comers of the floor field, respectively. Eachfollows its own genetic information and aims for the goalwhile searching cells. In the beginning, works showrandom searching manner. After some generations, allworks are able to reach the goal. By the 99th generation anordered structure is formed as shown in (b), where the pathfor WORKl emerged as cell 1 -> 5 or 6 > 7, and that forWORK2 as cell 4 -> 2 or 3 -> 7. At each generation thesystem will get a little better, measured by the totalprocessing ime. Just after the change of request (at each100th generation) he system shows chaotic behavior, butalready after a few generations the organization has beenstabilized rather well. In a long run the organization willfluctuate between two configurations according to the type

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of request. It can be said that the BMS model enables toadapt to external changes through self-organization. C as e 2: Change of Internal Environment

(a) random behavior (b)self-organizationadapting o R l0 5

\4

(c) reconfigurationadapting to R1 >R2 (d) reconfigurationadapting o R2->R1Fig. 3 Self-reconfigurable behavior adapting to changesof production request. (9: generation)

starting point of work 1/ starting point of work 2

(a) random behavior

(c) malfunction ofcells 1 and 4I

lq;(9=49)(b) self-organization\

(9=99)(d) replacing cells 1and 4 with 2 and 5

(e) replacing cells 3 and6 with recovered cells (f) reconfigurationincluding all cellsFig. 4 Self-reconfigurablebehavior adaptingtomalfunction of cells (9: generation)

Figure4 shows reconfigurable behavior of the BMS modelwhich adapts itself to the case of malfunction andrecovery of manufacturingcells. After showing a randombehavior in the initial state (a), the system emerges astationary structure using all cells as can be seen in (b).Malfunction occurs on two cells of 1 and4 with the highestprocessing speeds at the 50th generation, and itcontinues until the 100th generation. The system self-organizes by excluding the disabled cells during themalfunction as in (d). When the cells recover at the 100thgeneration, the system aims for including the cellsbecause their processing speeds are high as shown in (e).It achieves high performance in terms of the totalprocessing time, by employing all cells so that the path forWORK1 emerges as cell 2 or 3 -> 4 -> 7, and that forWORK2 as cell 5 or 6 -> 1 -> 7, as shown in the state of (f).4.2 Simulations bv Self-Oraanizational ModelThe following example is taken from the real factoryenvironment. The problem s drilling holes on printed circuitboards (PCBs). In this particular case, there is a highvolume of several types of boards to be processed everyday. Also there is a great variety of machines with differentcapabilities.The problem is how to distribute the boards to the drillingmachines assuming that the number of transporters andavailable machines varies daily depending on whether theyare working or not. The problem decomposes into job,machine, and transporterdescriptions.Each ob is definedby the properties of boards it consists of. Similarly, eachmachine is defined by its processing capabilities. Thematching of job requirements and machine capabilitiesillustrated in Figure5. Exact matches are given priority.

By accuracy requirement/caoabilitv class By boadtable sizeBoard Machine Board MachineA- - 300:---Cf 600

800 800- . priority matches- - - - *secondary maches

is

Fig. 5 Matching problem of PCBs anddrilling machines.Each drilling machine creates attraction fields according oits capabilities. For example, in this case the attractionfields are for the size of the table and the accuracycapability. On the other hand, each transporter becomessensitive for a particular attraction field according to theproperties of the job it's carrying. The overall shop floor isillustrated in Figure6.The attraction fields are designed for this particular case.The priority of matching is defined by using a largerattraction field than for secondary matches. The useddynamics is defined so that input attractions are initiallyon, but are turned off when an input is received. When theprocessing is almost finished, they are turned back on (in

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Collects empty Collectstransporters Working finished jobsfield/ to be processed a finished ob field

\'EMPTY' Leaves a job Picks up )READY'

Dispatching Queue of empty 1 \1 of jobs transporters Malfunction \ \Queue of jobs \to be processed ,'

\\\

. c - - - - - - - - - - - - - - - - - - - - - - - - - -*,

calsabilitv I finlri

Fig. 6 Factory floor layout for drilling machines andtransporters navigating to "attractive" machines.

order to receive next input) as well as an "empty"transporter attraction (in order to get the outputtransported).The result of the above simulation is a dynamic schedule,that can adapt to external and internal disturbances. Forexample, we can create malfunctions for the cells or thetransporters, and the schedule will adapt in real-time tothese changes. The main merit of the method is not to findthe optimal schedule, but a method to give a dynamicschedule in very dynamic environment. This example isnow to be verified against currently used manualscheduling. Other test cases in real world application arenow under work.5. ConclusionOneof the main issues that we have toso lve toward nextgeneration manufacturing is how we can deal with growingcomplexity and dynamics in manufacturing environment.In his study, BMS proposed as one of candidate for nextgeneration manufacturing systems has been modeled withfocusing on the capability of configuration in dynamicenvironments.The simulation using the proposed model clearlydemonstrates the adaptive behavior to the changes ofproduction demands due to external environment and themalfunction and recovery of manufacturing cells asinternal environment in the manner of self-organizationwithout external control. Also, the modelling stronglysuggests that BMS is capable of real time scheduling, inparticular, the proposed system is not necessary to reachthe optimal, but adapt even in the case of very dynamicdisturbance of manufacturing environment.

The concept of BMSwill be extended from the floor level t othe handling problem of recycling and disposal.6. AcknowledqmentThis study has been supported in part by the grant of IMSProgram, Grant-in-Aid of the Ministry of Education,Science and Culture of Japan, the grant of NissanFoundation, and the grant of Research for the FutureProgram of the Japan Society for the Promotion ofScience7. References(1) Alting, L., Jorgensen, J., 1993, The Life-Cycle

Concept as basis for Sustainable IndustrialProduction, Annals of the CIRP, Vol. 42/1: 163-167

(2) Back, T., Schwefel, H.P., 1993, An Over View ofEvolutionary Algorithms for Parameter Optimization,Evolutionarv Comoutation, MIT press: 1-10.

(3) Boer, C.R., Jovane, F., 1996, Towards a New Model ofSustainable Production: ManuFuturing, Annals of the

(4 ) Iwata, K., Onosato, M., 1994, Random ManufacturingSystems: a New Concept of Manufacturing Systemsfor Production to Order, Annals of the CIRP, Vol. 4311:

(5) Kurihara, T., Bunce, P.. Jordan, J., 1996. NextGeneration Manufacturing Systems in the IMSProgram, Advances in Production ManagementSystems, IFIP: 17-22.

(6) Langton, C.G., 1989, Artificial Life, Addison-WesleyPub. Comp. Inc..

(7) Markus, A., Kis Vancza, t., Monosstori, L., 1996, AMarket Approach to Holonic Manufacturing, Annalsof the CIRP, Vol. 4511: 433-436.

(8) Tharumarajah, A., Wells, A.J., Neems, L., 1996,Comparison of the Bionic, Fractal and HolonicManufacturing System Concepts, Int. J. ComputerIntegrated Manufacturing Vol. 913: 21 7-226.

(9) Ueda, K., 1992, A Concept for Bionic ManufacturingSystems Based on DNA-type Information, Proc. ofIFIP 8th Inter. PROLAMAT Conf., Tokyo: 853-863.

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Complexity in Manufacturing Systems, Proc. of 27thCIRP International Seminar on ManufacturingSystems, Ann Arbor: 69-78.(13) Vaario, J., Ueda, K., 1996, Self-organization inManufacturing Systems, Proc. of Japan/USASymposium on Flexible Automation, ASME, Boston,

(14) Vaario, J., Ueda, K., 1996, Biological Concept of Self-Organization in Flexible Automation Systems,Advances In Production Management Systems, IFIP:

(15)Warnecke, H.J., 1993, The Fractal Comoany, Springer-Verlag, Berlin.(16) Wiendahl, H.P., Scholtissek,p., 1994, Management

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Modeling of Biological Manufacturing Systems for Dynamic Reconfiguration_uremovic