Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 15, No. 3, 2024 

 

306 

Workshop Layout Optimization Based on SLP and 
Genetic Algorithm 

Mengjia Chen * 

Logistics Engineering and Management, Southwest University of Petroleum, Chengdu, Sichuan, 610599, China 

* Corresponding author: Mengjia Chen (Email: 1308327734@qq.com) 

 

Abstract: In response to the existing problems in the layout of Company B's production workshop,the System Layout Design 
(SLP) method is used to obtain the logistics intensity and non logistics intensity between operating units in the workshop.Based 

on comprehensive correlation analysis,a position correlation map is drawn to obtain the initial layout of the workshop.In order 

to eliminate the drawbacks caused by human factors,genetic algorithms were used to optimize the initial layout,forming a more 

scientific and reasonable layout plan.The feasibility of the optimized layout was verified by comparing the handling distance and 

logistics intensity. 

Keywords: Layout optimization, System layout design method, Genetic algorithm. 

 

1. Introduction 

As the main body of national economic development, the 

manufacturing industry plays an important role in promoting 

high-quality economic development and enhancing national 

competitiveness. Facility layout planning is an important part 

of enterprise development, and the quality of layout planning 

will have a significant impact on the production efficiency, 

material turnover efficiency, factory equipment utilization, 

and material processing costs of the enterprise. Therefore, a 

scientifically reasonable facility layout can reduce the overall 

management cost of material handling in the workshop by 10% 

to 30% [1]. If this cost can be significantly reduced through a 

scientifically reasonable facility layout, it is equivalent to 

creating profits for the enterprise and promoting its long-term 

development [2].At present, there are two mainstream 

methods for workshop equipment layout, System Layout 

Planning (SLP) and Genetic Algorithm. The SLP method 

proposed by American industrial engineer Charlie has been 

widely applied in the production research of automotive 

manufacturing and other manufacturing enterprises [3].Some 

foreign scholars have conducted extensive research on the job 

relationship diagrams and material handling diagrams 

generated during the application of SLP and Material 

Handling Analysis (SHA) methods [4-6].B Deng et al. 

Combined SLP and genetic algorithm to optimize the 

equipment layout model, which reduced material processing 

costs and improved the utilization rate of the production 

workshop [7].Ju Jingjing et al. developed a workshop layout 

optimization model based on ergonomics, using traditional 

SLP and genetic algorithm to optimize and solve the model, 

and verified it through examples [8].A large number of studies 

have shown that SLP is a relatively mature method for solving 

the layout of warehouse centers, but this method is subjective 

and easily influenced by the experience of researchers, so it 

needs to be combined with other optimization algorithms for 

quantitative analysis. This article combines the SLP method 

with genetic algorithm to optimize the workshop layout of 

Company B, achieving complementary advantages between 

the two and making the workshop equipment layout more 

practical. 

2. SLP Solution for Initial Layout of 
Production Workshop  

2.1. Overview of the Production Workshop of 
Company B 

B Company is a German brand manufacturer of electric 

tools and accessories. The products produced by the factory 

mainly include electric drills, electric hammers, electric 

pickaxes, charging tools, angle grinders, etc.. Factory B 

covers an area of approximately 30000 square meters, with a 

production workshop of 100m in length and 88.5m in width, 

covering an area of 8850 square meters. The current layout of 

the workshop is shown in the following figure. 

Chemical 
storage 
area

Inspection 
Area

Rotor injection 
molding area

Maintenance 
area

Clerical 
office area

Connection area 
of stator and 

rotor

Assembly area 
for stator and 

rotor

Finished product 
assembly area

Accessory 
assembly area

Storage area for 
stator and rotor

1

2

Semi-
finished 
product 
storage 
area

Raw material 
storage area

Finished product 
temporary storage area

 
Figure 1. Original layout of the workshop 

In the figure, it is evident that the workshop facilities are 

arranged in an unreasonable manner, such as the distance 

between the raw material storage area and the finished 

product assembly area being too far, the phenomenon of 

repeated handling in the inspection area, and the process of 

transferring the stator and rotor to the stator and rotor storage 

area and then to the finished product assembly area being not 

only too far away, but also with severe repeated handling.  



 

307 

2.2. Analysis of Logistics and Non-Logistics 
Relationships 

2.2.1. Analysis of Workshop Logistics Relationship 
The logistics intensity in the workshop is determined by the 

process route and logistics volume. According to the 

transportation frequency between homework units, the 

logistics relationship strength is divided into 5 levels. The 

higher the transportation frequency, the higher the level of 

logistics relationship, commonly represented by A, E, I, O, U, 

and their degree varies from strong to weak. Based on the 

logistics distance and logistics intensity between the 

operating units in the production workshop of Factory B, the 

logistics relationship between the operating units is obtained 

(see Figure 2). 

U
U

U

U
U
I
O

U
U

U

U
U
0
U
I
U

U
U

U
U

U

U
U
U
U
0
A
E
E

U
U

U
U

U
U

U

O
U
0
0
O
U
U
U
U
U

U
U

U
U

U
U

U
I

I

U
U
I
U
E
U
U
U

U
U

U

U

U

U

U

0

O

U

U

U

O

U

U
Storage area for stator and 

rotor13

Semi-finished product 
storage area12

Maintenance area11

Accessory assembly area10

Finished product assembly 
area9

Clerical office area8

Assembly area for stator 
and rotor7

Connection area of stator 
and rotor6

Rotor injection molding 
area5

Chemical storage area4

Inspection Area3

Finished product temporary 
storage area2

1 Raw material storage area

 
Figure 2. Logistics intensity relationship chart 

2.2.2. Analysis of Workshop Logistics Relationship 
When planning the layout of workshop facilities, not only 

should the impact of logistics relationships be considered, but 

also the impact of non-logistics relationships on production, 

mainly including the continuity of the operation process, the 

weight of material handling, convenient handling, and 

management, in order to determine the non-logistics 

relationships of the operating unit (see Figure 3). 

U
U

U

U
U
O
O

U
U

U

U
U
0
U
O
U

U
U

U
U

U

U
U
U
U
0
E
A
I

U
U

U
U

U
U

U

U
U
U
U
U
U
U
U
U
U

U
U

U
U

U
U

U
I

E

U
U
I
U
E
U
U
U

U
U

U

U

U

U

U

0

I

U

U

U

U

U

U
Storage area for stator and 

rotor13

Semi-finished product 
storage area12

Maintenance area11

Accessory assembly area10

Finished product assembly 
area9

Clerical office area8

Assembly area for stator 
and rotor7

Connection area of stator 

and rotor
6

Rotor injection molding 
area5

Chemical storage area4

Inspection Area3

Finished product temporary 
storage area2

1 Raw material storage area

 
Figure 3. Non logistics relationship diagram 

2.3. Comprehensive Relationship and Initial 
Workshop Layout 

In the vast majority of factories, there are both logistics and 

non-logistics connections between the operating units. 

Therefore, in SLP, it is necessary to assign points based on the 

logistics and non-logistics relationships between the 

operating units, weighted and summed according to a certain 

proportion, and graded according to the proportion described 

in the table. Finally, the composite interrelationship, which is 

the comprehensive interrelationship, is calculated. This article 

sets the weight ratio between logistics and non-logistics 

relationships as 1:1. The classification of comprehensive 

interrelationships is shown in the table. The comprehensive 

interrelationships between all 36 job pairs between job units 

1 and 13 were calculated using weights. Finally, obtain a 

comprehensive interrelationship diagram and preliminary 

layout diagram of the homework unit (see Figure 4 and Figure 

5). 

U
U

U

U
U
I
O

U
U

U

U
U
0
U
I
U

U
U

U
U

U
U
U
U
0
A
A
E

U
U

U
U

U
U

U

O
U
O
0
0
U
U
U
U
U

U
U

U
U

U
U

U
I

E

U
U
I
U
E
U
U
U

U
U

U

U

U

U

U

0

I

U

U

U

0

U

U
Storage area for stator and 

rotor13

Semi-finished product 
storage area12

Maintenance area11

Accessory assembly area10

Finished product assembly 
area9

Clerical office area8

Assembly area for stator 
and rotor7

Connection area of stator 
and rotor6

Rotor injection molding 
area5

Chemical storage area4

Inspection Area3

Finished product temporary 
storage area2

1 Raw material storage area

U

 
Figure 4. Comprehensive Mutual Relationship Diagram 

 

S
e
m
i
-

f
i
n
i
s
h
e
d
 

p
r
o
d
u
c
t
 

s
t
o
r
a
g
e
 

a
r
e
a

Finished product 

assembly area

Accessory 

assembly area

Storage area for 
stator and rotor

Clerical 

office area

Rotor injection 

molding area

Connection area of 

stator and rotor

Assembly area 
for stator and 

rotor

1

2

Raw material storage 

area

Finished product 

temporary storage area
Chemical 
storage 

area

Inspection Area

Maintenance 

area

 
Figure 5. Production workshop layout diagram 

3. Workshop Layout Optimization 
Based on SLP and Genetic 
Algorithm 

The most commonly used algorithm at present is still the 

Qualitative Processing System Layout Planning (SLP) 

algorithm, which cannot plan and design the specific location 

of facilities, and the solution is difficult to determine due to 

the subjective will of decision-makers. 

The SLP method focuses on the continuity of the process 

and the rationality of the workshop layout. The judgment of 

the superiority or inferiority of the workshop equipment 

layout relies too much on the experience of designers, and 

lacks quantitative analysis of the workshop, resulting in the 

inability to intuitively and scientifically determine whether 

the layout is optimal. Therefore, to establish a mathematical 

model for the processing workshop, with the goal of 

minimizing logistics costs and maximizing non logistics 

relationships, a genetic algorithm is designed and solved 

through MATLAB programming. 



 

308 

3.1. A Mathematical Model for The Production 
Workshop Problem in Factory B 

3.1.1. Model Assumptions 
This article aims to optimize the layout of Company B's 

production workshop to minimize material handling costs. To 

achieve this goal, a layout optimization model was 

established based on facility layout theory and objectives. The 

model assumes that: 

(1) The boundaries of all workshop units are parallel to the 

X or Y axis. The X-axis positive half axis serves as the long 

side direction of the factory, and the Y-axis positive half axis 

serves as the wide side direction of the factory; 

(2) The center point of each unit serves as its entry and exit 

points; 

(3) The unit transportation cost between different work 

units is roughly equal. 

4) The distance between two workshops is calculated as the 

Manhattan distance, which is 𝑑𝑖𝑗 = |𝑥𝑖 − 𝑥𝑗| + |𝑦𝑖 − 𝑦𝑗|.  

The layout coordinates of Company B's production 

workshop are shown in Figure 6. 

i

j

L

H

O

Y

X

 
Figure 6. Coordinate map of homework unit 

3.1.2. Model Assumptions 
Based on the description of the above layout problem, a 

single objective function is established to minimize the 

material flow rate under a certain workshop area, following 

the principles of shortest transportation path and optimizing 

workshop layout, in order to achieve the goal of reducing 

material flow rate. Key considerations should be given in 

production layout. Let i and j be the work units in the layout 

plan, d be the distance between two work units, f be the 

handling volume between two work units, and C be the unit 

logistics cost. Then, the expression for the material handling 

cost function: 

=
m m

j

ijijij
dfcC

i

                 (1) 

3.1.3. Constraint condition 
(1) When planning the workshop layout, consider the 

transportation route, set the distance between each unit, and 

ensure that any two workshops in the factory area do not 

overlap in the workshop area. 

iji
dL

2
x +

+
−

ji

j

LL
x               (2) 

ij

j
dH

HH
+

+


2
y-y

i

ji
            (3) 

(2) Ensure that the edge of the workshop does not exceed 

the boundary of the factory area. 

2

L
x-x

ji

ji

+
+

L
L                (4) 

2
y-y

j

ji

HH
H

i
+

+               (5) 

3.1.4. Initialize the Group 
Before starting the calculation, the genetic algorithm needs 

to generate a certain number of populations to facilitate 

subsequent operations. In population intelligence algorithms, 

having too many populations can lead to increased 

computation time, unstable convergence, and reduced 

efficiency; A small population can lead to a lack of diversity 

within the population, resulting in suboptimal outcomes. To 

prevent this situation; The initial layout planning scheme 

obtained by the SLP method can be combined with randomly 

generated individuals to manually adjust the initial population 

and create a diverse optimal initial population. 

3.1.5. Fitness Function 
The fitness function, also known as the evaluation function, 

has a higher fitness value, which increases the chance of an 

individual being selected as a parent, surviving, and passing 

on their advantageous traits to offspring. Individuals with 

lower fitness values are more likely to be eliminated. This 

process ensures that ideal traits are continuously inherited, 

ultimately forming the best individual solution. In the process 

of facility layout in this case, several constraints and objective 

functions were encountered. Based on the mathematical 

model, a fitness function was formed using the reciprocal 

method. This article studies the minimum value problem and 

adopts the first processing method, so the objective function 

is: 

）（
）（

xf1

1
x

++
=

G
F  0xf0 + ）（，GG  (6) 

Wherein, 

'xf C=）（                (7) 

1000=G                 (8) 

3.1.6. Cross and Variation 
Genetic operators are an important component of genetic 

algorithms, which mimic natural selection to select the most 

suitable individuals. It consists of four basic operations: 

selection, crossover, mutation, and mutation. This article uses 

real number encoding to select partial crossover and single 

point mutation. Partial crossing refers to the selection of a 

subset of genes at a fork point for mutual exchange; Single 

point variation refers to the variation of each gene in an 

individual's gene sequence, replacing the original gene on the 

individual's gene pair with a small probability. 

3.2. Model Algorithm Solving 

Write and run a genetic algorithm program using MATLAB, 

with som the running results shown in Figure 7 and Figure 8. 



 

309 

 
Figure 7. Particle iteration process   

 

 
Figure 8. Optimized facility layout diagram 

Based on the actual situation of the factory and the optimal 

unit coordinates and area requirements, draw an optimized 

layout diagram (as shown in Figure 10).    

Semi-
finished 
product 
storage 
area

F
i
n
i
s
h
e
d
 
p
r
o
d
u
c
t
 

a
s
s
e
m
b
l
y
 
a
r
e
a

Accessory 
assembly area

Storage area for 
stator and rotor

Clerical office 
area

Rotor injection 
molding area

Connection area 
of stator and 

rotor

Assembly area 
for stator and 

rotor

Raw material 
storage area

1

2

Finished product 
temporary storage 

area

Chemical 
storage 
area

Inspection Area

Maintenance area

 
Figure 9. Optimized workshop layout diagram 

4. Result Analysis  

To test the effectiveness of the optimized facility layout, the 

distance and transportation volume of work units before and 

after the optimization of the workshop layout in Company B 

are shown in Table 1. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Three Scheme comparing 

Related units 
Distance before 

optimization/m 

Optimized 

distance/m 

logistics 

volume/t 

9-2 68 28 4.8 

1-9 62 30 2.3 

1-10 57 26 1.5 

7-13 47 38 1.2 

10-12 26 19 1.5 

13-9 21 20 1.2 

12-9 17 42 1.4 

1-7 23 54 0.8 

1-5 28 25 0.5 

6-7 15 21 0.5 

5-6 14 17 0.5 

1-6 21 52 0.2 

9-3 65 43 0.05 

 

According to Table 1, the total logistics distance before 

optimization is 464m, and the total logistics distance after 

optimization is 415m. The total flow rate product before 

optimization is 7596, while the total flow rate product after 

optimization is 4905. The total logistics distance of the 

optimized plan has been reduced by 49 meters, with an 

optimization ratio of about 11%; The total flow rate has 

decreased by 2691 compared to the product sum, with an 

optimization ratio of approximately 35%. 

5. Conclusion 

This article takes the production workshop of Company B 

as the research object, establishes a preliminary improvement 

plan for workshop layout through SLP method, and optimizes 

the initial workshop layout plan using genetic algorithm. The 

results indicate that the new plan can reduce logistics and 

transportation costs and effectively help Company B improve 

the efficiency of the production workshop. 

References 

[1] Zhou Ji. Intelligent Manufacturing - Main Attack Direction of 
"Made in China 2025" [J]. China Mechanical Engineering, 
2015, 26 (17): 2273-2284. 

[2] Ma Hanwu, Gong Wenwei, Chen Jun. Facility Planning and 
Logistics System Design [M]. Beijing: Higher Education Press, 
2005. 

[3] Charlie Muser. System Layout Design [M]. Beijing: 
Mechanical Industry Press, 1988: 10-22. 

[4] PATIL S B, KUBER S S. Productivity improvement in plant 
by using systematic layout planning ( SLP)-A case study of 
medium scale industry [J]. International Journal of Research in 
Engineering and Technology, 2014, 3 ( 4): 770-775. 

[5] ARYA V. Increased productivity and planning by improved 
plant layout using systematic layout planning at NCRM 
division, Bhushan [J]. International Journal of Innovations in 
Engineering and Technology, 2013, 2(2): 297-304. 

[6] WIYARATN W, WATANAPA A. Improvement plant layout 
using systematic layout planning (SLP) for increased 
productivity [J]. International Journal of Industrial and 
Manufacturing Engineering, 2010, 4( 12): 1382-1386. 

[7] Deng Bing, Lin Guangchun. Optimization of Workshop 
Equipment Layout by Combining Improved SLP and Genetic 
Algorithm [J]. Combination Machine Tool and Automation 
Processing Technology, 2017 (8): 148-151+156. 



 

310 

[8] Ju Jingjing, Lin Jingguo. Research on improved GA-SLP 
production workshop layout planning [J]. Modern 
Manufacturing Engineering, 2017 (11): 120-124. 

[9] Wang Yunrui. Research on Optimization of Production 
Workshop Layout in a Steel Structure Company Based on SLP 
[J]. Modern Manufacturing Engineering, 2019 (3): 31-36.