Journal of Robotics, Networking and Artificial Life
Vol. 8(1); June (2021), pp. 33–36
DOI: https://doi.org/10.2991/jrnal.k.210521.008; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Decision System for Plural Production Lines
Layout by Using GA
Hidehiko Yamamoto1,*, Masato Noda2, Hirohumi Tsuji3, Yasuhisa Terawa3, Yoshinori Nakamura3,
Masayuki Tsuchida3, Katsuaki Yamada4, Yukiyasu Kuriyama4
Department of Mechanical Engineering, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1194, JapanKIOXIA Corporation, Shibaura 3-1-21, Tokyo, Japan
3New Business Development Div., InfoFarm Co., Ltd, Yanaizu-cho Distribution Center 1-8-4, Gifu, Gifu 501-6123, Japan
4
Production Div., Kai Industries Co., Ltd, Oyana 1110, Seki 501-3992, Japan
12ARTICLE INFOABSTRACTArticle History

We develop the system to decide the efficient layout of assembly production line by using Genetic Algorithm (GA). We call thesystem as Decision System of Plural Production-lines-layout by GA (PPG). PPG decides the efficient layout of production lineby using GA, work-flow-line acquired by Chameleon code and the machine breakdown data. PPG evaluates the layout efficiency
by calculating the operator’s walking time to fix the machine breakdown occurred on the production line. By the evaluation, it
is ascertained that PPG was useful.

Received 23 October 2020
Accepted 08 April 2021
Keywords
Line layout
genetic algorithm
Chameleon-code
production line
walking time

© 2021 The Authors. Published by Atlantis Press B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
We need the efficient production line layout. In this study, Decision
System of Plural Production-lines-layout by Genetic Algorithm(PPG). PPG determines the layout of plural production lines wheresome operators work. The determination is carried out by using
Genetic Algorithm (GA) and the operators’ walking routes obtained
from Chameleon-code. PPG is applied to the razor assembly line toverify whether PPD is effective in improving work efficiency. The
efficiency means which kind of layout that plural production lines
are working just one operator can manage.

2. CHAMELEON CODEChameleon code is the new color barcode whose function has ahigh speed and precision recognitions as shown in Figure 1. This
study puts Chameleon-code on the top of an operator’s hat andobtains the operator’s walking routes data by many cameras that are
put on the factory ceiling. The acquired operators’ walking routes
data includes the name of the operator, the camera names, the dates
and times, and the x- and y-coordinates of the chameleon code.

3. PPG CHARACTERISTICS
Plural production-lines-layout by genetic algorithm is the system
to determine the layout of plural production lines using GA. The
Corresponding author. Email: yam-h@gifu-u.ac.jp*determination is based on the operators’ walking routes data which
are acquired from the chameleon-code and the machine breakdown data. Specifically, PPG calculates the walking time that anoperator moves to fix machine breakdowns and determines thebetter layout where one operator can manage plural productionlines. As shown in Figure 2, PPG consists of the condition moduleand the GA module. Condition module includes the acquiredoperators’ walking routes data and various parameters for GA. GA
module determines the better production line layout by using GA.

4. INDIVIDUAL REPRESENTATION
The individual representation of our GA used in PPG adopts thestructural gene, as shown in Figure 3. We express the x-coordinates,
the y-coordinates and the angles indicating the position of one production line as the three consecutive loci. When representing plural
production lines, these three loci are connected in series. Figure 3
shows the individuals in the case of three production lines. The xcoordinates, y-coordinates and angles are shown in Figure 4. To secure
the moving paths of the operator, we adopt the constraint that thewidth between each production line is an arbitrary value Z mm. The
Z means the width that an operator can walk through. Individuals
that do not satisfy the constraints are deleted as lethal genes.

5. HOW TO EVALUATE FITNESSThe GA fitness of PPG adopts the walking time of an operator. If
the walking time of individual i is ti and the slowest walking timeH. Yamamoto et al. / Journal of Robotics, Networking and Artificial Life 8(1) 33–36

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Figure 1 | Chameleon-code.

Figure 4 | How to judge lethal chromosome.

Step 2: Calculate the machine breakdown coordinates for each production line.
Step 3: Calculate the walking time of the operator.
Step 4: Calculate the fitness.
Step 5: Judge whether the end condition is satisfied or not. If satisfied, go to Step 8. If not, go to Step 6.
Step 6: Carry out selection, crossover and mutation operations andgenerate new individuals of the next generation.
Step 7: Carry out Step 2 on the new generation’s individuals.
Step 8: The production line layout of the individual with the shortest walking time is adopted as the satisfying layout and isoutput as a solution.
Figure 2 | PPG outline.

Figure 3 | Individuals expression.

among the population of a generation is tw, the fitness fi of individual i is expressed by the following equation.	   fi = (t w - t i + 1)2(i = 1, 2, 3,¼) (1)If the time difference between each individual and the slowest individual is adopted as the fitness, the difference between an excellent
individual fitness and another individual one becomes small. To
make large difference, we give the difference a square [1].

6. GA ALGORITHM
Our GA module function is to improve the efficiency of production line layout by GA. The algorithm of our GA is shown below.
Step 1: Generate the initial population.

We describe the details of the above algorithm as below.
Step 1: Generate the initial population.
The individuals of the initial population are randomly selectedfrom among the spaces coordinates that lines can be placed in thefactory. The information of the selected individual has the x- andy-coordinates and angles of the production line. In generating theindividuals, there is a possibility that the production line layouts
may overlap or be too close. In this case, the individual corresponding to these kinds of layouts becomes a lethal gene. Not to generate
the lethal genes in generating individuals, the following operations
are carried out.
(1) Whenever generating individuals, calculate the distance
between the contour of a production line and the contours ofother still acquired production lines as shown in Figure 4. (2)If the calculated distance is less than Z mm, the individuals aredeleted as a lethal gene. (3) Return to (1) and regenerate the individual again.
Step 2: Calculate the machine breakdown coordinates for eachproduction line.
The coordinates whose machine gets breakdown in the factory
layout change according to the coordinates of the production line
layout. That is, since the coordinates of a machine breakdown
occurrence for every generated individual are different, it is necessary to calculate each walking time. PPG’s coordinates of machine
breakdown occurrences of all individuals are calculated by thefollowing operations.

H. Yamamoto et al. / Journal of Robotics, Networking and Artificial Life 8(1) 33–36(1) Find the work flow line data corresponding to the machine
breakdown occurrence time from the machine breakdown data
and the coordinates of the machine breakdown. (2) By using theacquired coordinates, calculate the machine breakdown occurrence
coordinates for other all individuals. For example, if the coordinates
and angles of one production line A of an individual generated in
Step 1 are (xa, ya, θa) and the machine breakdown coordinates foundin (1) are (x_e, y_e), the machine breakdown coordinates (Xe, Ye)of the production line A are expressed as the following equations.	 
Xe = xa + xe * sinq a(e = 1, 2, 3,¼) (2)Ye = ya + ye * cosq a	 (e = 1, 2, 3,¼) (3)35

Figure 5 | Production line model.

Step 3: Calculate the walking time of the operator.
When a machine breakdown occurs, the operator moves from theoperator’s standing place to the place where the machine breakdown occurs to fix it. PPG calculates the total walking time to getthere and back.
Step 4: Calculate fitness by using Equation (1).
Step 5: Judge whether the end condition is satisfied or not. If satisfied, go to Step 8. If not, go to Step 6.
After the walking time of the operator is updated, if no better individual is acquired after certain generations, GA is finished.

Figure 6 | Parallel layout and PPG layout.
Table 1 | Machine breakdown example
Timex-Coordinate

411
565
890
1156

3513
3858
4128
2651

y-Coordinate Breakdown name A

Step 6: Carry out selection, crossover and mutation operations.

Walking time (s)PPG layout
Parallel layout

Step 7: Carry out Step 2 on the new generation’s individuals.
Step 8: The production line layout of the individual with the shortest walking time is adopted as the satisfying layout and PPG
outputs it as a solution.
The coordinate data of the solution individual is output and thebest layout is represented by using the acquired coordinates.

7. APPLICATION EXAMPLES
Plural production-lines-layout by genetic algorithm was applied tothe razor assembly line of Kai Industries Co., Ltd. Since the shapeof the razor assembly line is extremely complicated, we simplified
it to the graphic model as shown in Figure 5 and applied it to PPG.
In this case, we acquired the operator walking routes data for 8 h
per one shift. The routes data correspond to 3 days. The machine
breakdown data of the same time (Table 1) are also acquired. We
adopted the GA parameters as follows. The mutation rate is 10%,the population size is 200 individuals and one individual is preserved as an elite. Selection and crossover methods were carriedout by the fitness proportional method and two-point crossover.
We also adopted b = 500 mm and Z = 800 mm.
The simulation results are shown below. The simulations were carried out 20 times. The acquired best layout is shown in Figure 6.
The layout corresponds to the individual with the minimum

Whether the blade is abnormal
Whether the blade is abnormal
On standby
Abnormal presence or absenceof frame

Table 2 | Comparison of simulation results

Using the fitness calculated in Step 4 and using the fitness proportional method, select a set of the individuals as a crossover target.
As the crossover, we adopt the two-point crossover. Mutations
occur with a certain probability.
Repeat Steps 2–6 for the next generation population.

4046
2046
881
3468

2405
6164

walking time. As a comparison, the walking time of the current layout
adopted in Kai Industries Co., Ltd. Its current layout is arranged inparallel. The current layout walking time was also calculated. The
layout determined by PPG shortened the operator walking time byabout 63 min compared to the parallel layout (Table 2). The layout
determined by PPG was effective in improving work efficiency.

8. CONCLUSION
After application simulations, it is ascertained that PPG using thechameleon code of InfoFarm Co., Ltd. is effective for the decision
to find better layouts of plural production lines where one operator
works. We will need the more research to find the refined layout ofplural production lines because the acquired layout is not beautiful.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

REFERENCE[1]

K. Honda, H. Yamamoto, T. Yamada, Design system of cell typeassembly machine with dual arms robot by GA, Artif. Life Robot.
23 (2018), 170–173.

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AUTHORS INTRODUCTION
Dr. Hidehiko Yamamoto
He received his B.S, M.S., and PhD fromNagoya Institute of Technology. After joining Toyota Industrial Corporation andWakayama University, he works for Gifu
University. He is currently a Professor andthe fellow of the Japan Society of MechanicalEngineers.

Mr. Masato Noda

Mr. Yoshinori Nakamura
He is General Manager of New Business
Development Division, InfoFarm Co., Ltd.

Mr. Masayuki TsuchidaHe received his Master’s degree fromDepartment of Intelligence Science andEngineering, Gifu University. He is currently working for Xioxia Corporation.

Mr. Hirohumi Tsuji

He is Expert of New Business Development
Division, InfoFarm Co., Ltd.

Mr. Katsuaki Yamada
He is President and Representative
Director of InfoFarm Co., Ltd.

Mr. Yasuhisa Terawa
He is Senior Expert of Corporate Planning
Department, InfoFarm Co., Ltd.

He received his B.S. from the Departmentof Mechanical Engineering, Nagoya
Institute of Technology, 1978. Currently,
he is a Managing Director of Production
Div., Kai Industries Co., Ltd.

Mr. Yukiyasu Kuriyama
He received his B.S. from the Departmentof Mechanical Engineering, Nippon
University, 1986. Currently, he is a ChiefManager Technical Def., Prod. Div. KaiIndustries Co., Ltd.