Journal of Robotics, Networking and Artificial Life, Vol. 1, No. 4 (March 2015), 295-298

Production effects by form changes of autonomous decentralized FMSs with mind
Hidehiko Yamamoto
Mechanical Engineering, Gifu University, 1-1, Yanagido, Gifu Japan
Kakeru Yokoi
Human Information Systems Engineering, Gifu University, 1-1, Yanagido, Gifu Japan
Takayoshi Yamada
Mechanical Engineering, Gifu University, 1-1, Yanagido, Gifu Japan
E-mail: yam-h@gihu-u.ac.jp, yokoi1205@gmail.com, yamt@gihu-u.ac.jp

Abstract
In autonomous decentralized FMS, the number of agents and constant installation position has been studied inprevious studies. Therefore, in this study, we are considering to change the position of the warehouse and thenumber of AGVs to redesign the factory form and evaluate the changes in the production of autonomous
decentralized FMS. By being inspired by human mind, we are proposing Minimum Unit of Mind (MUM), a new
method of controlling the behavior of AGVs to avoid the unexpected collision in autonomous decentralized FMS.
Keywords: Autonomous decentralized FMS, Mind, Route interference, AGV,

1. Introduction

evaluate the changes on the production of autonomous
decentralized FMS.

We need to avoid collision among AGVs when we use
autonomous decentralized FMS. There are some rules toavoid collisions, but these rules are not good enough tocontrol the process of autonomous decentralized FMS
because all situations can not be foreseen and rules are
not described. Hence, the purpose of this study is tobring forward human mind to control the behavior ofAGVs. In fact there are some limitations to control
autonomous decentralized FMS in order to avoid theAGVs collisions, but in this study, we are trying toimprove the FMS operations to keep a high efficiency.
To improve the efficiency and control the collision,
we propose the development of behavioral control toavoid the collision by relying on the fact that simulating
the human mind will help machines to make better
decisions. In this study, we are considering to change
the position of the product warehouse and change thenumber of AGVs to redesign the factory form and2. Overview of autonomous decentralized FMS
Fig.1 illustrates an autonomous decentralized FMS
factory sketch. As Fig. 1 shows, the factory floor is
divided into grid patterns, and AGVs are moving along
these lines to carry parts to the warehouse and machine
centers which is representing the system of autonomous
factory to carry out the production. The autonomous
decentralized FMS does not have a management
mechanism to integrate the entire system, machining
centers, AGV, product warehouse (PRW), parts
warehouse (PAW). Each agent that configures thesystem autonomously determines the act by recognizing
the purpose of the system by cooperating andnegotiating to other agent.1

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PRW

PAW

Machining
CenterPath interference

A１AGV
Stay away from thedestination‐X

A２Fig. 1. Autonomous decentralized FMS

Fig. 2. Model of Mind

We describe the behavior of the AGV control ofautonomous decentralized FMS factory. In autonomous
decentralized FMS, each AGV determines the behaviorautonomously. If any of the AGVs runs on the same line
at the same time, they overlap and AGV pathinterference occurs. It is necessary to avoid suchinterference between AGVs and therefore, we havedeveloped control rules for the system. If the number ofAGVs increases, more unexpected events can occur
because the rules become more complicated. Thus, it is
impossible to generate rules that can be adapted to every
situation in an autonomous decentralized FMS factory.
This study focuses on the human mind, which can deal
with unexpected events. We propose a mind model for
the AGVs and develop a method to control their
behavior by using a mind. The mind model considers
two types of mind: an arrogant mind and a modestmind. AGV with arrogant mind takes the action
approaching the destination forcibly. AGV with modest
mind takes the action to give way to other AGV.
Fig.2 is a diagram about mind model. In this study,
we call it Minimum Unit of Mind (MUM). Further, A1and A2 is unit, X is load, and arrow is a stimulation
vector. The threshold is determined bythe unit.
If the internal value reaches the threshold, we call
excited and if it does not reach, normal.
When a signal is sent to the unit, it sends a signal tothe direction of the arrow when excited, and it is notsent when normal.
Load has the function to change the internal value ofthe unit. When the signal is sent to the load, the value of
unit is decreased by the value of X.
Stimulation vector is a line connecting the load andthe unit. It gives a signal to the load or unit when thesignal comes, when A1 keeps exited, we call modest
mind, and when normal, we call arrogant mind.

Next, we describe the internal functions of MUM.
When the arrogant AGV has path interference, A1 isincreased by 1. Keeping the situation of the interference
and being increased by 1, the A1 value becomes thethreshold value, and the AGV is changed to a modest
one.
When AGVs with a modest mind keep giving a way,the value of A2 is increased by 1. When the situation isrepeated an optional time and A2 becomes exited, asignal is sent to a load. The received load decreases thevalues of units A1 and A2 by optional values. Becauseof this, A1 and A2 are returned to normal and AGV
with a modest mind is changed to one with an arrogant
mind. In this way, when an arrogant mind isincorporated in the AGV, it is forceful in its motion. On
the other hand, when a modest mind is incorporated inthe AGV, it gives way to other AGVs.
3. Impact on production conditions and FMS layout
In this section we will investigate parameters such as
capacity utilization, route interference and production
outputs by applying following conditions:# Changing the position of the products warehouse
The product and part warehouses were immobilized
in the previous studies and to prove that moveable
warehouse method is effective, we decided to design
five new factory layouts in this study.
# Changing the number of machining centers and AGVs
In the previous studies, the number of AGV wasallocated to five AGVs and the number of themachining centers was done with 24 units. We willchange the AGV numbers from 2 to 9, and we adopt 24
units of machining center and 16 units in this study.

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Fig. 3.

Location of product warehouse (0, 50) layout

Fig.3 is the layout of the production floor when thecoordinates of the product warehouse are (0, 50).

Fig. 4. Rout interference and its number of AGVs
in every 30 minutes

4. Simulation results and discussions
Figure 4, Table 1 and Table 2 are the results of thesimulation of production when the number of machining
centers and AGVs are being changed. As the result
shown, the more number of AGVs, the moreproductions will be produced. However, we have gotten
a result that shows a little difference when the numberof machining centers are 24 units and 16 units. Inaddition, in the first 30 minutes of the starting thesimulation there is a high route interference number asshown in the Fig. 4. It is because, right after the starting
the simulation, there is no part in the machine centersand all AGVs should go and pick the parts from the part
warehouse simultaneously.
Table 1. Production outputs of MC number 16
AGV

2

3456

78

9

data1

92

129

160

184

209

233

254

274

data2

89

129

156

184

212

233

256

273

data3

91

132

161

184

209

230

255

277

Table 2. Production outputs of 24 MC number 24
AGV

2

3

45

67

8

data1

84

122

151

184

214

235

257

9274

data2

85

120

155

185

212

233

252

277

data3

84

118

155

185

210

232

252

280

Next, we show the simulation results of autonomous
decentralized FMS when the position of the productwarehouse is changed to 5 different positions. Table 3
and 4 illustrate the utilization rate of MC when thenumber of AGVs are 8 and 5 and the number of MCs
are set on 16.

Table 3. Utilization rate when the AGV number is 5
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)Capacity utilization
36.52237.18736.40136.15
35.88235.248

Table 4. Utilization rate when the AGV number is 8Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)Capacity utilization
49.86450.13650.30650.29350.07549.235

When the AGVs number is 5 and the position of
product warehouse is (0, 25) then the utilization rate of
MC gives us a better result. Table 3 and 4 are shown theresults of this simulation. However, when the number of
AGVs is changed to 8 and the coordinates of PW arechanged to (0, 50), the utilization rate gets a little bit
better than the previous condition. On the other hand,when the AGVs number is 5 or 8 and PW coordinatesare (60, 50), the utilization rate will be the lowest rate.
Table 5 and 6 are shown the production outputs whenthe AGVs number is 5 and 8.
According to Table 5 and 6, PW with position (0,50) has become the maximum production outputs whenthe AGVs number is 5 and 8. In the case of 5 AGVs, theproduction outputs are increased. To compare with 8AGVs, the ratio of increasing the products is not
significant. The lowest result of production outputs
which we have got in the simulation is the time when
the PW position is (60, 50) with both 5 and 8 AGVs.

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Table 7 and 8 illustrate the route interference number incase of 5 and 8 AGVs.
Next, we show the simulation results when theposition of the product warehouse is changed to 5
different positions. Table 3 and 4 illustrate theutilization rate of MC when the number of AGVs are 8and 5 and the number of MCs are set on 16.
Table 5. Production number of AGV number 5
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)The production number182
180
191
181
180
175

Table 6. Production number of AGV number 8
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)The production number250
263263261
253
247

Table 7. Route interference number of AGV number 5
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)

Route interference number997
912
729
833
850
731

Table 8. Route interference number of AGV number 8
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)

Route interference number
2668
2286
2134
2111
2113
1923

According to Table 7 and 8, the route interference
number is the lowest result in case of 5 and 8 AGVs andin the PW position (60, 50). As a result, the new
production floors have a better result to compare withthe previous production floors. Hence, autonomous
distributed system that was newly created by thissimulation, and the results we have got through thissimulation show that the some layouts will be more
efficient to use in the production floor.

There are different rules and techniques whichhave been studied by other researchers, the number ofAGVs are limited so more number of AGVs cause moredifficulties and collisions in the production floor in theirstudy. In our research we have tried to improve thelimitations and change the rules to be able to increase
the number of AGVs as more as possible. To prove that
our study is effective, we proposed MUM in this study.
In this section, we discuss the results of different
layouts of the production floor, 5 and 8 AGVs, thenumber of parts, route interference number and MC
utilization rate. According to mentioned Tables andFigures, the previous study with a PW position (x, y) =(0, 10) the route interference rate was high. In our study
we change the layout of autonomous distributed system
PW positions to (0, 25), (0, 50), (60, 0), (60, 25), (60,50) and AGV route interference was reduced after
applying changes. However the utilization rate of AGV
is increased and interference number of AGV isreduced, but sometimes the production outputs will belower. Sometimes due to a long distance between AGV
and PW position, the AGV takes a lot of time to move
between the two points, which is time consuming.
In our study, the result of simulation shows that
production floor with PW position (0, 50) with 5 and 8
AGVs will have a better performance and will get abetter result; we expect the production efficiency byfollowing this pattern. There are several ways to design
the layout and change the number of AGVs and MCs,
however we have got a good result which seems
efficient, but we suspect that there can be other ways tomake optimum. It has also been found that it isnecessary to perform the simulation, depending on thedifferent conditions.
5. Conclusions
In conclusion, changing the number of MC and AGVs
affected the production rate and capacity utilization.
Applying the new conditions and layout designs has
improved the production efficiency. Finding new
optimal agent conditions by applying to autonomous
decentralized FMS in order to improve the production
efficiency should be done in a future work.
Reference
1.Hidehiko Yamamoto, Jaber Abu Qudeiri and M.A. Jamali,
Real-time Control of Decentralized Autonomous Flexible
Manufacturing Systems by using Memory and Oblivion,
Lecture Notes of Computer Science , Springer, pp.252-259,
ISBN 978-3-540-46535-5, Nov., 2006.

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