Reactive Fuzzy Dispatching Rule for Automated Guided

Alcides Xavier Benincasa, Orides Morandin Jr and Edilson R. R Kato. Federal University of Slo Carlos (UFSCar)

Computer Science Department Slo Carlos, SP, Brazil

{alcides, orides, kato} @dc.ufscar.br

Abstract - The Automated Manufacturing Sysiem ( A M ) scheduling complexity increases with producis cusiomizotion, due. dates, alternofive rouies and no foresee demand in ihe system. One of ihe imporiant aspecis in shopfloor is io ensure thai the paris will be at the right position and at the defined time. In ihis sense if's necessary io have a good iransporiaiion system witch allows flexibility and integration, considering a designed pelformanre to the manufacturing system. AGVs have been used to reach ihis pelformanre, bur there is a serious problem far ihe dispatching rule definition. There are some dispatching iechniques using single rule and composed ones, where the main idea is to consider a good peljbrmance for ihe entire manufacturing system. This work presents an approach for a definition of an AGV dispatching rule based on firzzy logic, ihai considers the actual siaius of the manufacturing system and takes the decision on real time.

Keywords: Dispatching Rule, AGV Dispatching, Automated Guided Vehicle, Automated Manufacturing Systems, Fuzzy Systems.

1 Introduction

Automated Manufacturing Systems (AMs) are being proposed in order to increase the performance of factory, mostly to deal with the necessity on producing different product types, and having high level of productivity.

The AMs has typically a high number of elements like: CNC machines, automated guided vehicles (AGVs), warehouse, et al. All the elements need to work on a synchronized way and having a c o m o n target.'

One of the most import elements in an AMs is the transport system, using for example AGVs.

Every transportation task can be done using an AGV, but to define witch vehicle will attend a request is not an easy task, mostly if it's intended to achieve a defined performance.

Also, if there is more than one request, it's difficult to define where an AGV needs to go or attend fust.

There are some published works about AGV dispatching using several rules, simple, composed or combined or even using different techniques [2 ] , [9 ] , [7 ] , ~ 9 1 .

It's also known that there is a considerable quantity of variables that can interfere or help to achieve a desired performance.

The dispatching rules can be classified in two categories: work center initiated and vehicle initiated [5].

Rules considering "work center initiated" are applied when there is more than one idle vehicle for only one request. Rules considering "vehicle initiated" are applied when there is more than one request for only one idle vehicle.

Each rule is characterized by the using of defined variables, like target distance, traveling time, waiting time, etc.

This work proposes a rule using fuzzy logic and three variables for the dispatching task. The vehicle initiated is also considered.

2 Proposed Fuzzy System

The main purpose of the system or the approach is to increase the productivity, i.e., to look for a rule that can increase the produced part quantity in a limited time or reduce the time to produce a defmed part quantity.

The proposed fuzzy system is based on the Mamdani model and has the main elements or functions described as the following:

Input variables d e f ~ t i o n ;

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Fuzzy sets and membership functions (inputs

and outputs);

Rules Base;

Inference;

Defuzzification.

Input variable mapping or fuzzyfrcation;

2.1 Input Variables

The variables for the proposed system are:

0 Distance between the AGV and the machine witch is requesting a transportation 0;

Node number or quantity between the AGV and the machine witch is requesting a transportation 0;

Remaining space output buffer of the defmed machine (RS).

The DT variable is found in many work proposes and intends to minimize the travel time, but there are some problems described when only this variable is used.

The NN variable used is intended to avoid or to penalize a task where there are too many nodes in the path, because it's possible that the AGV has to stop in a node waiting for a free path (Figure 1).

Nod.

Figure 1 -Path, zones and nodes [19].

The RS variable is used to prevent a deadlock or even to prevent a situation where there is an idle machine and AGV, but it's not possible to use the machine because the buffer is full.

2.2 Fuzzy Sets

For the specified variables the fuzzy sets and the membership functions are described in Figure 2.

Priority;

Distance;

NodeNumher;

Remaining Space Output Buffer.

0 X Y

Membership Function of Distance

0 X Y

Membenhip Function of Node Number

1

+Big -++Medium

0 o X Y

Membership Function of Remnining Space Output Buffer

0 X Y Membership Function of Priority

Figure 2: Fuzzy Sets and the Membership Functions.

2.3 Rules Base

Considering the input variable, a rules base was consmlct.

The rules are of following types:

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if DT = Short and NN = Few and RS = Big then PE = High Figure 3.

one load station and one unload station as shown in

This n i l e base is shown on Table 1.

Table 1: Rules Base.

I Rule I DT I h" I RS I Priority I

3 Applying the Fuzzy System

In order to validate the proposed approach a case study was carried out aiming to accomplish all the delivery dates, obtain a high productivity and lead time reduction.

A. The FMS modeled

The flexible manufacturing system (FMS) considered in &e case-study was built in a simulation software. The FMS is composed by six manufacturing machines with a limited output buffer each, one AGV,

,... ,/e

,..I &*' Figure 3: FMS layout

At the factory, five different product types are manufactured. T!x production routes and processing time (in minutes) for each product is known and is presented in Figure 4.

PRODUCT A

PRODUCT C m- .er

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PRODUCT D

Mcb 4 Mch5 P l .3") P . 5 " )

Mch 3 rr-1.1 min)

PRODUCT E

Mch 2 (r4.95 mm)

Figure 4: Product's production routes and times.

Considering the product A, its production route is machine 1 or machine 2, machine 3 and machine 5 and your proceessing time in each machine is, in minutes, 1.5 or 1.2, 1.3 and 1.7, repectively.

For the simulation rehearsals, the following conditions where considered

There is only one operation at a time on a

machine.

Each part type has only one route processing.

The capacities of buffer machines are limited.

The AGV travel distance between stations is

known and AGV speed is constant.

Each part type requires a set of tools.

Setup times are constants.

B. Tests and Validation

In order to test and validate the proposed approach, the shop floor was tested using the fuzzy system and also a FIFO rule to be compared.

The total products were 375 and the pre-defmd sequence was products type C, after B, after D, after E and A (Table 2).

Table 2: Sequence and quantity

Product Quantitys

E 25 1.

A 25 .. . . . . C 25

D 25

A 25

B 25

D 2s E 25

Totalof 375parts

In the Table 3 the results considering the processing time are shown, using the fuzzy system and the FIFO rule.

Table 3: Results of Processing Time

Processing Production Parts Time @ours)

FIFO FUZZY

Another results refer to the inventory. Every pre- defmed time the total parts quantity was checked, presenting the results showed at Table 4.

For every single checked time, the parts quantity is lower when using the fuzzy system.

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Table 4: Results of Processing Time

Processing Inventory Time (hours)

FIFO FUZZY

4 Conclusions The proposed approach considers the current status

of the shop floor in order to decide witch vehicle will attend a transportation task. The variables considered (distance, node number and remaining space output buffer) were used in a fuzzy decision system.

Some experts were consulted to help to define the variables to be considered, the membership functions and rules for the fuzzy system and also to test a prototype in order to set adjustments.

A system was implemented, verified and validated considering a flexible manufachuing system model.

The main results of this work is a definition of a new dispatching rule for AGV, that can be used in real time on a reactive way, once the plant status is considered to the decision.

A system was implemented and it has presented better results than systems with decision making based on FIFO rule.

The proposed approach for vehicle dispatching task in a manufacturing system considers fuzzy logic and the actnal plant status. This allows a reaction to event occurrences, and also considering the designed factory performance.

It’s also possible to use the system to an automated system, with AGV, or even a non-automated one.

Looking for improvements at the obtained results, automatic techniques for the rules generation and membership functions setting can be used in the system.

References [ I ] Artiba, A.; Elmaghraby, S.E.. The Planning and Scheduling of Production Systems - Methodologies and Applications. Chapman & Hall, 1997.

[2] Bartholdi, J. J.; III; Platzman. 1989. Decentralized control of automated guided vehicles on a simple loop. IIE Transactions, 21, 76-81.

[3] Berkan, R.C.; Trubatch, S.L.. Fuzzy Systems Design Principles - Building Fuzzy IF-Then Rules Bases. IEEE Press, 1997.

[4] CO, C. G.; Tanchoco, J. M. A.. 1991. A review of research on AGVS vehicle management. Engineering Costs and Production Economics, 21,3542.

[SI Egbelu, P. J.; Tanchoco, J.M.A.. 1984. Characterization of Automatic Guided Vehicle Dispatching Rules. Intemational Journal of Production Research, v01.22.

[6] e Opera&q Pioneira, S b Paulo, 2001.

171 Grasso, V.. 1990. AGV-based assembly lines: influence of sequencing and launch policies on system performance. Intemational Journal of Production Research, 28, 1385-1399.

[8] Groover, Mikell P. 1987. Automation, Production System, and Computer Integrated Manufacturing, Prentice-Hall, Inc., USA.

[9] Grossman, David D.. 1988. Trafic Control of Multiple Robot Vehicle. EEE Journal of Robotics and Automation, vo1.4, n.5, October.

[lo] Gyampah, K. “A comparative study of FMS tool allocation and part type selection approacbes for varying part type mix”. The Intemational Joumal of F M , vo1.6, pp.179-207, 1994.

[ I l l Hammond, Gary. 1986. AGVS at Fork; IFS (publications) Ltd., 1986.

[I21 Hwang, S.S.; Shogan, W.. “Modeling and solving an FMS part selection problem”. Intemational Joumol of Production Research, vo1.27,n0.8, pp.1349-1366, 1989.

[I31 Kim, C. W.; Tanchoco, J. M. A.; Koa, P. H.. 1999. AGV dispatching based on workload balancing. Intemacional Joumal of Production Research, vo1.37, no.17, pp.40534066.

Gaither, N.; Frazier, G.. Administracrio da Produpio

4379

[14] Kim, Y. D.; Lee, D. H.; Yoon, C. M.. “Two-Stage Heuristic Algorithms for Part Input Sequencing in Flexible Manufacturing Systems”. European Joumal of Operocional Research, vo1.133,no.3, pp.624-634,2001,

[15] Klir, G. J.; Yuan, B.. 1995. Fuzzy Sets and Fuzzy Logic. Theory and Applications. Upper Saddle River, New Jersey: Prentice Hall.

[I61 Klir, G. J.; Yuan, B.. F u q Sets and Furry Logic - Theoy andApplicafions. Prentice Hall, 1995.

[17] Mamdani, E. H. Application of Fuzzy Logic to Approximate Reasoning Using Linguist Synthesis. IEEE Transactions on Computer Vol. 26, no. 12, pp. 1182- 1191, 1977.

[IS] Mendel, J. M.. 1995. Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, Vol. 83, N.“ 3, pp. 345-377, Maqo.

[ 191 Morandin Junior, Orides. Automated Monufacturing Systems modeling methodologv using Virtual Pehi Net. Doctoral Thesis (in Portuguese); Universidade de S l o Paula, August, 139 p., 1999.

[20] Pedrjcz, W.; Gomide, F.. An Infroduction to Fuzzy Sets: Analysis andDesign, MIT Press, 1998.

[21] Politano, P.R.; Camargo, H.A.; Kato, E.R.R.; Morandin Junior., 0. “A reactive programming procedure for flexible manufacturing systems”. SMC2000-IEEE International Conference on Systems, Man and Cybernetics, pp.2156-2161, October 200, Nashville, Tennessee, USA.

[22] Stecke, K.E.; Kim, I.. “A study of FMS part type selection approaches for short-term production planning”. Intemational Joumal of Flexible Manufacturing System, vol.1, no.1, pp.7-29, 1988.

[23] Stecke, K.E.; Kim, I.. “A flexible approach to part type selection in flexible flow systems using part mix ratios”. International Joumal of Production Research, vo1.29,no.l,pp.53-57, 1991.

[24] Slack, N.; Chambers, S.; Harland, C.; Harrison, A.; Johnston, R.. Adminishapio do Produ@o, Atlas, Slo Paulo, 1997.

[25] Slack, N.. Vantagem Competitiva em Manufatura: Atingindo Competifividade nos Operacbes Indusfriais - EdigBo Compacta, Atlas, Slo Paula, 1999.

[26] Taghaboni, F.. 1989. Scheduling and confrol of manufacturing system with critical material handling. PhD thesis, Purdue University, West Lafayette, Indiana.

[27] Transactions on Computer, vo1.26, no.12, pp.1182- 1191,1977.

[28] Tubino, D.F.. Manual de Planejamento e Controle do Produqfio, Atlas, Slo Paula, 2000.

[29] Yim, D.; Linn, R. J.. 1993. Push ondpull rules for dispatching automated guided vehicles in a flexible manufacfuring system. Intemational Jonmal of Production Research, vo1.31, no.1, pp. 43-57.

[30] Zadeh L. A.. 1965. Fwry Sets. Information and Control, vol. 8, pp. 338-353.

[31] ZadehL. A.. 1988. F u q Logic. IEEE Computer.

[32] Zadeh L.A.. 1996. Fuzzy Logic = Computing Tith Words. IEEE Transactions On Fuzzy Systems, Vol. 4, No 2, Maio, pp. 103 - 11 1.

[33] Zhao, Z.; S a m , R. D. Fuzzy rule leaming during simulation of manufacturing resources. F u q Sets and System. vol.l22,2001, pp.469-485.

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