https://doi.org/10.31449/inf.v47i5.4557 Informatica 47 (2023) 69–74 69 
Optimal Path Planning of Logistics Distribution of Urban and Rural 
Agricultural Products From the Perspective of Supply Chain 
Xiumei Wang
* 1
 and Xiaoli Song
2
 
1 
Business School, Nanfang College Guangzhou, Guangzhou, Guangdong 510970, China 
2 
School of Economics and Management, Beijing City University, Beijing 100083, China 
Email: 
1 
zuoxiu85467@126.com, 
2 
songxiaoli@bcu.edu.cn 
* 
Corresponding author 
Keywords: supply chain, agricultural product, logistics distribution, genetic algorithm 
Received: December 9, 2022 
Logistics distribution is a crucial part of the supply chain of agricultural products. This paper optimized 
the distribution path of agricultural products between distribution centers and customers using the 
adaptive genetic algorithm and conducted a simulation comparison with greedy and traditional genetic 
algorithms. The results showed that although the greedy algorithm obtained the path planning scheme 
faster, the planning path obtained by the genetic algorithm was better, and the adaptive genetic algorithm 
obtained a shorter distribution path, lower transportation cost, and less computation time than the 
traditional genetic algorithm. 
Povzetek: Raziskava je primerjala adaptivni genetski algoritem z drugimi algoritmi na problemu 
distribucije kmetijskih proizvodov med centri in strankami. 
 
1 Introduction 
Rapid economic development has improved people’s 
standard of living, which in turn has stimulated the desire 
to consume and driven economic growth [1]. At the same 
time, the improvement of the logistics level not only 
enables people to choose a wider variety of goods but also 
enables remote shopping, free from geographical 
restrictions on shopping. In addition to the improvement 
of shopping, the improvement of logistics is also 
beneficial to the development of the supply chain. The 
supply and demand sides of products in the supply chain 
are linked together through specific channels, and the 
supply side provides the related products according to the 
requirements of the demand side [2]. The supply side in 
the supply chain can have more than one demand side, and 
the demand side can have more than one supply side. The 
supply and demand sides in the supply chain together form 
a supply network. The supply side needs to plan a 
reasonable logistics distribution path when distributing 
products to multiple demand sides [3]. Related studies are 
as follows. Xiong et al. [4] proposed to optimize the 
logistics distribution path using the fish swarm algorithm 
and found through experiments that the algorithm quickly 
jumped out of the locally optimal solution and converged 
to the shortest path. Yu et al. [5] established a three-
dimensional constraint model for the actual demand in e-
commerce and used it to optimize the logistics distribution 
path algorithm. The experimental results showed that the 
optimized algorithm reduced the distribution mileage by 
more than 25% compared to the genetic algorithm, i.e., the 
comprehensive performance of the model was improved. 
Yang [6] established a hard time-window path  
 
 
optimization model for logistics distribution under a 
dynamic road network based on the distribution path  
 
optimization, considering the dynamic uncertainty of the 
urban road network and taking the minimum cost as the 
objective function. 
Table 1: A summary of this paper. 
The purpose 
of this study 
The objective of this paper is to optimize 
the logistics distribution path of 
agricultural products in the supply chain to 
improve the logistics distribution 
efficiency and reduce the distribution cost. 
The design 
of this study 
In this paper, after constructing the 
distribution model of agricultural logistics 
under the supply chain, the genetic 
algorithm was used to optimize the 
distribution path, and the fixed crossover 
and mutation probabilities in the genetic 
algorithm were improved to the adaptive 
probabilities. 
The results 
of this study 
The experimental results showed that the 
improved adaptive genetic algorithm 
obtained better logistics distribution paths 
and had higher planning efficiency than the 
traditional genetic algorithm and greedy 
algorithm. 
The 
contribution 
of this 
study 
This paper improved the crossover and 
mutation probabilities of the genetic 
algorithm to make adaptive adjustment 
according to the target in the process of 
optimization, so as to obtain a better 
distribution path. This work provides an 

70 Informatica 47 (2023) 69–74 X. Wang et al. 
effective reference for agricultural logistics 
distribution path planning. 
The 
limitation 
of this 
study 
The shortcoming of this paper is that the 
constraints of the model were simplified 
for the convenience of calculation when 
constructing the distribution model of 
agricultural logistics under the supply 
chain, which makes the planning scheme 
deviate to a certain extent. Therefore, the 
future research direction is to make more 
specific provisions for the constraints of 
the distribution model. 
 
 
Figure 1: Basic structure of agricultural supply chain. 
2 Agricultural products supply 
chain 
A supply chain is a customer or market demand-oriented 
channel relationship between supply and requisitioning 
parties. The demand side finds the supply side based on 
the demand for the product, and the supply side delivers 
the product to the location of the demand side through 
logistics. Agricultural products have higher time limit 
requirements than other types of products [7]. Users can 
get the agricultural products they need faster through the 
supply chain. The concept of customer demand as the 
business direction in the supply chain can, in turn, guide 
the production direction of agricultural products and 
reduce unnecessary losses for farmers. 
Figure 1 shows the basic structure of the agricultural 
products supply chain. Firstly, agricultural products 
customers will communicate their demand information for 
agricultural products to the agricultural products 
distribution center, and the agricultural products 
distribution center will summarize the demand 
information from different customers and then 
communicate the corresponding demand information to 
the suppliers of the agricultural products [8]. After 
receiving the demand information, the suppliers of the 
agricultural products deliver the corresponding 
agricultural products to the distribution center, and finally, 
the distribution center carries out the logistics distribution 
of agricultural products according to the customer’s 
demand information. 
The agricultural product supply chain structure 
diagram in Figure 1 is a simplified sequence diagram. In 
the actual supply chain, there are plural suppliers, 
distribution centers, and customers, but the distribution 
centers play the role of aggregation and distribution of 
agricultural products, so the number of distribution centers 
is smaller; therefore, the actual structure of the supply 
chain is more like a net than a chain. In addition, Figure 1 
also shows that the types and number of agricultural 
products to be distributed by the distribution center 
depend on the information provided by the customer, and 
the types and number of agricultural products sent by the 
supplier to the distribution center depend on the 
information provided by the distribution center. It is 
further seen that in the supply chain model, agricultural 
product sales are guided by the market demand and are not 
decided by the suppliers, which promotes market 
development [9]. 
3 Logistics distribution path 
optimization from a supply chain 
perspective 
3.1 Logistics distribution path model 
This paper optimizes the efficiency and cost of supply 
chain operation by optimizing the logistics distribution 
path in the supply chain. Since the distribution center 
initiates the demand for agricultural products to the 
supplier, and then the supplier delivers the agricultural 
products to the distribution center, the path between the 
supplier and the distribution center is fixed and does not 
need optimization, so this paper only considers the path 
solution between the distribution center and every user 
when considering the logistics distribution path 
optimization. In the actual supply chain, every distribution 
center needs to distribute agricultural products to different 
customers, and every distribution center needs to take 
itself as the starting point to design the distribution path. 
Planning the distribution path between distribution centers 
and customers is considered a multi-point logistics 
distribution path planning problem [10]. 
Realistic distribution path planning is subject to many 
influencing factors and involves more complicated 
calculations as multiple vehicles are involved in 
distribution within a distribution center. Therefore, some 
assumptions are often made to facilitate computation 
when constructing the multi-point logistics distribution 
path model. The assumptions made in this paper are: (1) 
the quantity of agricultural products in the distribution 
center can meet the demand of all customers who have 
contact with the distribution center; (2) there are enough 
vehicles in the distribution center for agricultural products 
distribution, and the specification of every vehicle is 
consistent; (3) when one vehicle is responsible for the 
demand of one customer, other vehicles are not 
responsible for that customer; (4) vehicles in the 
distribution process ignore the impact of loading and 
unloading at the distribution point on the transportation 
time, and because the vehicle specification and 
transportation speed are consistent, the transportation cost 
of every distribution vehicle is only related to the path 
length; (5) additional assignments or additions of products 
will be allowed while the vehicle is following the planned 
route for delivery; (6) the geographical relationship 
between the distribution center and the customer is known, 
and the customer’s demand for agricultural products is 
also known. 

Optimal Path Planning of Logistics Distribution of Urban and Rural… Informatica 47 (2023) 69–74 71 
 
The expression of the logistics distribution path model 
[11] is: 
j i x d C Z
R
i
R
j
N
k
ijk ijk k
   =
  
= = = 0 0 1
) ( min
. (1) 
The constraints are: 

















=



=
=
=
 



= =



=


 

=
=
= =
=
1
, , 3 , 2 , 1 1
0
) (
0
i point  passes k  le when vehic 1
0
j point   to i point  from drives k  le when vehic 1
1
0
1
0 1
1
R
i
k i
N
k
ik
R
i
N
k
i ik k
R
i
ijk ik
ijk
x
R i
i N
y
q y Q
else
x y
else
x

, (2) 
 
where
 
Z is the total transportation cost of the 
distribution route, k is the distribution vehicle number 
(there are N
 
vehicles), i and j
 
are the customer node 
number (the number of the distribution center is 0, and the 
customer node number is 1, 2, 3 ......R), 
ijk
x is the 
decision variable (its value is 1 when distribution vehicle 
k drives from node i to node j and 0 else), 
ik
y
 is the 
decision variable (its value is 1 if the demand of node i is 
completed by distribution vehicle k and 0 else), 



=
=
=

=
R i
i N
y
N
k
ik
, , 3 , 2 , 1 1
0
1

 represents other vehicles 
cannot be responsible for meeting the demand of a 
customer after one of them has been responsible for that 
customer, 
1
1
0
=

=
R
i
k i
x
 represents that the distribution 
vehicle will return to the distribution center after 
completing the distribution task, 
k
Q represents the 
maximum load of the distribution vehicle [12], 
i
q 
represents the demand of customer node i for agricultural 
products, 
k
C is the transportation cost per unit distance of 
the distribution vehicle, and 
ijk
d is the transportation 
distance between nodes i and j . 
3.2 Distribution path optimization based 
on the adaptive genetic algorithm 
A genetic algorithm is used to optimize the logistics 
distribution path from a supply chain perspective. The 
basic principle of the genetic algorithm for route 
optimization is to consider nodes as genes in a 
chromosome, i.e., the sequence of genes as the distribution 
path of vehicles. 
 
 
Figure 2: Distribution path optimization process based on 
the adaptive genetic algorithm. 
When using the genetic algorithm to optimize the 
logistics distribution path, the initial population is first 
randomly generated according to specific steps, and then 
the transportation cost is calculated for the distribution 
path scheme represented by every chromosome in the 
population using the formula of the distribution path 
model. The transportation cost is the adaptive value of the 
genetic algorithm. If the algorithm does not reach the 
termination condition, the iteration is repeated according 
to the preset crossover and mutation probabilities until the 
termination condition is satisfied. 
The traditional genetic algorithm keeps the good gene 
fragments under the guidance of the adaptive value, 
integrates them by crossover operation [13], and generates 
new gene fragments by mutation operation; eventually, 
the adaptive value of the population is converged. The key 
lies in the crossover and mutation operations in the genetic 
operation. The probabilities of the two operations will 
directly affect the convergence speed and quality of the 
algorithm, but the crossover and mutation probabilities in 
the traditional genetic algorithm are usually fixed 
parameters set empirically. Large probabilities may lead 
to difficulty in convergence or make the solution fall into 
locally optimal in the late iteration. Therefore, the genetic 
algorithm was optimized by adaptive crossover and 
mutation probabilities [14]. The optimized path 
optimization flow is shown in Figure 2. 
① A customer node is randomly selected from the set 
of customer nodes that have not been assigned a 
distribution vehicle. 
② A distribution vehicle is selected randomly from 
the set of distribution vehicles. 
③ If the remaining load of the selected distribution 
vehicle can meet the demand quantity of the selected 
customer node, then the customer node is assigned to that 
distribution vehicle, and if it cannot meet, then it returns 
to step ②. 
④ Whether all the customer nodes are allocated is 
determined. If not, it returns to step ①; if they are, the 
allocation scheme of customer nodes is encoded to 
generate chromosomes. The structure is shown in Figure 
3. The large gene fragment in the chromosome is the 
distribution path scheme of a distribution vehicle. The 
number of distribution centers is at the beginning and the 
end, and the allocation order of customer nodes in the 
middle is the distribution order. 
 

72 Informatica 47 (2023) 69–74 X. Wang et al. 
 
Figure 3: The coding structure of the distribution scheme. 
⑤ The chromosomes are stored in the population to 
judge whether the number of chromosomes in the 
population reaches the preset value. If not, it returns to step 
①; if it does, it goes to the next step. 
⑥ The formula of the logistics distribution path 
model is used to calculate the transportation cost of the 
distribution path scheme corresponding to every 
chromosome, i.e., the adaptive value of the chromosome. 
⑦ Whether the genetic algorithm reaches the 
termination condition is determined. If it does, the optimal 
chromosome distribution path scheme is output; if not, the 
crossover and mutation probabilities are adjusted 
according to the adaptive value of the chromosome. The 
adaptive adjustment formula is: 
 
















  
  
−
  −  −
−
=





  
  
−
  −  −
−
=
avg m
avg
avg
m m
m
m
avg c
avg
avg
c c
c
c
Z Z P
Z Z
Z Z
Z Z P P
P
P
Z Z P
Z Z
Z Z
Z Z P P
P
P
1
m in
m in 2 1
1
1
m in
m in 2 1
1
) )( (
) )( (
, (3) 
 
where 
c
P
 
and 
m
P
 
are the crossover and variation 
probabilities after adaptive adjustment, 
1 c
P and 
1 c
P are 
the initial crossover probabilities, 
1 m
P and 
2 m
P are the 
initial mutation probabilities, Z ′ is the adaptive value of 
an individual, 
min
′ Z is the smallest adaptive value in the 
current population, Z ′ ′ is the smaller adaptive value of an 
individual where crossover or mutation occurs, and 
avg
Z ′ 
is the average adaptive value of the current population. 
⑧ Genetic operations are performed on the 
population chromosomes, including selection, crossover, 
and mutation. The selection operation is to keep the 10% 
of chromosomes with the lowest transportation cost in the 
population directly as the offspring chromosomes. The 
crossover operation is to exchange all distribution point 
genes within the daughter chromosomes with the same 
vehicle number in two chromosomes, delete duplicate 
genes, and supplement the missing distribution point 
genes to ensure complete and non-duplicate distribution 
points in the whole chromosome. The mutation operation 
is to exchange the distribution point gene to be mutated in 
the daughter chromosome with the distribution point gene 
at the same gene position in another daughter chromosome. 
A population composed of offspring chromosomes is 
obtained after selection, crossover, and mutation 
operations. Then, it returns to step ⑥. 
 
Table 2: Demand for agricultural products at nodes and 
the distance between nodes in the simulation experiment. 
Node number Demand for 
agricultural 
products/t 
Coordinates/km 
0 / 
) 5 . 3 , 0 . 3 ( 
1 0.414 
) 5 . 5 , 0 . 2 ( 
2 0.325 
) 0 . 7 , 0 . 1 ( 
3 0.201 
) 5 . 6 , 0 . 6 ( 
4 0.311 
) 5 . 5 , 0 . 4 ( 
5 0.213 
) 0 . 4 , 5 . 5 ( 
6 0.123 
) 0 . 3 , 5 . 6 ( 
7 0.347 
) 5 . 2 , 5 . 1 ( 
8 0.435 
) 0 . 2 , 0 . 5 ( 
9 0.256 
) 0 . 1 , 0 . 2 ( 
10 0.414 
) 0 . 1 , 0 . 6 ( 
 
4 Simulation experiments 
4.1 Experimental environment 
MATLAB software simulated the logistics distribution 
path optimization algorithm [15]. The experiment was 
conducted on a server in a lab. 
4.2 Experimental setup 
To facilitate calculation, the agricultural products were 
considered as one kind, and the total weight was used as 
the demand of customer nodes. Among the node numbers, 
0 was the distribution center, and 1~10 was the customer 
node numbers. The demand of the customer nodes for 
agricultural products and the distance between different 
nodes are shown in Table 2. In addition, the distribution 
center had four trucks with the same specifications, the 
maximum weight of the trucks was 1.5 tons, and the 
transportation cost was 15 yuan/km. 
In order to verify the effectiveness of the adaptive 
genetic algorithm, this paper also conducted simulation 
experiments on the traditional genetic and greedy 
algorithms for comparison. When the greedy algorithm 
planned the distribution path, it first scanned 
counterclockwise with the distribution center as the origin 
and assigned the customer nodes to the distribution 
vehicles from near to far. The vehicle was changed by 
another one when the remaining load of the vehicle was 
not able to bear the demand. After the customer nodes 
were assigned, every vehicle selected the nearest 
distribution node as the next node. 
The parameters of the traditional genetic algorithm for 
planning the distribution path are as follows. The 
population size was 30,
c
P was 0.07, 
m
P was 0.01, and 
the maximum number of iterations was 1000. 

Optimal Path Planning of Logistics Distribution of Urban and Rural… Informatica 47 (2023) 69–74 73 
The relevant parameters of the adaptive genetic 
algorithm for planning the distribution path are as follows. 
The population scale was 30, 
1 c
P was 0.07, 
2 c
P was 0.05, 
1 m
P was 0.01, 
2 m
P was 0.005, and the maximum number 
of iterations was 1000. 
4.3 Experimental results 
Figure 4 shows the logistics distribution paths obtained 
using the greedy, traditional genetic, and adaptive genetic 
algorithms, respectively. Every color in Figure 4 
represents the distribution path of one distribution vehicle. 
Since the specifications of the distribution vehicles were 
consistent, no additional path attribution was marked. It 
was seen from Figure 4 that the path scheme obtained by 
every algorithm required three distribution vehicles for 
distributing agricultural products. The distribution paths 
under the greedy algorithm were 0-7-1-2-4-0, 0-5-6-8-10-
9-0, and 0-3-0; the distribution paths under the traditional 
genetic algorithm were 0-4-1-2-3-5-0, 0-7-9-10-8-0, and 
0-6-0; the distribution paths under the adaptive genetic 
algorithm were 0-1-2-3-4-0, 0-5-6-10-8-0, and 0-7-9-0. 
 
 
Figure 4: Logistics distribution paths under three 
distribution path planning algorithms. 
Table 3: The total length and transportation cost of the 
distribution paths under the three distribution path 
planning algorithms and the corresponding calculation 
time. 
 Path 
length/km 
Transportat
ion 
cost/yuan 
Calculation 
time 
consumed 
for the path 
scheme/s 
The greedy 
algorithm 
34.57 518.55 13.56 
The 
traditional 
33.93 508.95 25.67 
genetic 
algorithm 
The 
adaptive 
genetic 
algorithm 
29.54 443.10 19.65 
 
Table 3 shows the total transportation length and cost 
of the distribution paths under the three-logistics 
distribution path planning algorithms and the 
corresponding time consumed for calculating the three 
path schemes shown in Figure 4. The total length of the 
distribution path obtained by the greedy algorithm was 
34.57 km, the total transportation cost was 518.55, and the 
time taken to calculate this path scheme was 13.56 s; the 
total length of the distribution path obtained by the 
traditional genetic algorithm was 33.93 km, the total 
transportation cost was 508.95, and the time taken to 
calculate this path scheme was 25.67 s; the distribution 
path obtained by the adaptive genetic algorithm was 29.54 
km, the total transportation cost was 443.10, and the time 
taken to calculate this path scheme was 19.65 s. 
The comparison of the data in Table 2 showed that the 
greedy algorithm obtained the longest distribution path 
and the highest transportation cost, the traditional genetic 
algorithm obtained the second longest distribution path 
and the second lowest transportation cost, and the adaptive 
genetic algorithm obtained the shortest distribution path 
and the lowest transportation cost. 
The greedy algorithm spent the shortest time 
calculating the path scheme, while the traditional genetic 
algorithm spent the longest time. 
5 Discussion 
The supply chain is a customer or market demand-oriented 
channel relationship between supply and demand. The 
demand side seeks out the supply side according to the 
demand for products, and the supply side delivers the 
products to the demand side through logistics according to 
the demand side’s product demand. With the supply chain, 
customers can get the goods they need more quickly. 
Especially for agricultural products with a short shelf life, 
the faster the transportation and delivery, the higher the 
sale value can be retained. In the process of logistics 
distribution of agricultural products supply chain, the 
planning of the distribution route is an important part. 
After constructing the logistics distribution model of 
agricultural products under the supply chain, this paper 
used the genetic algorithm to optimize the logistics 
distribution path plan. In order to improve the 
performance of the genetic algorithm, this paper improved 
the fixed crossover and mutation probabilities of the 
traditional genetic algorithm into probabilities that can be 
adjusted according to the adaptive value of the distribution 
plan. Finally, the improved genetic algorithm was 
compared with the traditional genetic algorithm and 
greedy algorithm in the simulation experiment. The final 
experimental results are shown in the previous section. 
The experimental results show that the proposed adaptive 
genetic algorithm yielded the best distribution path 

74 Informatica 47 (2023) 69–74 X. Wang et al. 
solution and took the least amount of time to compute the 
distribution solution. The reason is as follows. The greedy 
algorithm only considered the nearest node, i.e., the 
locally shortest path, when selecting the next node, but the 
combination of the local shortest path might not be the 
globally shortest. Moreover, the assignment results of the 
scanning method adopted by the greedy algorithm were 
contingent, and the customer nodes assigned to the 
distribution vehicles also affected the subsequent path 
planning. The genetic algorithm randomly generated the 
chromosomes representing the path scheme, reducing the 
contingency of node assignment as much as possible. The 
adaptive values afterward guided the crossover and 
mutation from the global perspective; thus, its planning 
result was better than the greedy algorithm. In terms of 
computing time, the greedy algorithm selected the next 
node according to the principle of being closest to the 
current node, i.e., it only compared the path length. The 
genetic algorithm iterated the population repeatedly until 
the adaptive population value converged and stabilized, 
and the adaptive value of all chromosomes was calculated 
in every iteration. Therefore, the traditional genetic and 
adaptive genetic algorithms both took more time than the 
greedy algorithm. The adaptive genetic algorithm 
autonomously adjusted the crossover and mutation 
probabilities so that it did not fluctuate up and down 
around the optimal adaptive value after iteration due to 
excessively large probability but stably converged to the 
optimal solution, so it consumed the shortest time. 
6 Conclusion 
This paper briefly introduced the basic structure of the 
agricultural product supply chain, used the adaptive 
genetic algorithm to optimize the logistics distribution 
path in the agricultural product supply chain, and finally 
compared it with greedy and traditional genetic algorithms 
in the simulation experiment. The obtained results are as 
follows. (1) The path schemes obtained by the three 
algorithms all needed three vehicles; the scheme of the 
greedy algorithm was 0-7-1-2-4-0, 0-5-6-8-10-9-0, and 0-
3-0, the scheme of the traditional genetic algorithm was 0-
4-1-2-3-5-0, 0-7-9-10-8-0, and 0-6-0, and the scheme of 
the adaptive genetic algorithm was
 
0-1-2-3-4-0, 0-5-6-10-
8-0, and 0-7-9-0. (2) The total length and transportation 
cost of the distribution path obtained by the greedy 
algorithm were 34.57 km and 518.55 yuan, those of the 
traditional genetic algorithm were 33.93 km and 508.95 
yuan, and those of the adaptive genetic algorithm were 
29.54 km and 443.10 yuan. (3) The time consumed by 
greedy, traditional genetic, and adaptive genetic 
algorithms to calculate the path scheme was 13.56 s, 25.67 
s, and 19.65 s, respectively. 
References 
[1] Yao C (2020). Research on logistics distribution path 
analysis based on artificial intelligence algorithms. 
International Journal of Biometrics, 12, pp. 100-108. 
https://doi.org/10.1504/IJBM.2020.105625 
[2] Zong L (2021). Smart Logistics and Distribution System 
Based on Laser and Vision Fused SLAM Algorithm. 
Modern Economics & Management Forum, 2, pp. 227-238. 
https://doi.org/10.32629/memf.v2i6.539 
[3] Liu G, Hu J, Yang Y, Xia S, Lim MK (2020). Vehicle 
routing problem in cold Chain logistics: A joint 
distribution model with carbon trading mechanisms. 
Resources Conservation and Recycling, 156, pp. 104715. 
https://doi.org/10.1016/j.resconrec.2020.104715 
[4] Xiong C, Xu Y (2021). Research on Logistics Distribution 
Path Planning Based on Fish Swarm Algorithm. Journal of 
Physics: Conference Series, 1883, pp. 1-6. 
https://doi.org/10.1088/1742-6596/1883/1/012040 
[5] Yu F, Zhou Y (2021). Optimization of E-Commerce 
Logistics Distribution Path Algorithm under the 
Background of Big Data. Journal of Physics: Conference 
Series, 1744, pp. 1-7. https://doi.org/10.1088/1742-
6596/1744/4/042214 
[6] Yang S (2020). Optimization of Urban Logistics 
Distribution path under dynamic Traffic Network. 
International Core Journal of Engineering, 6, pp. 243-248. 
https://doi.org/P20190813001-202001-201912170001-
201912170001-243-248 
[7] Zou Y, Si W (2021). Research on Logistics Distribution in 
E-commerce Environment Based on Particle Swarm 
Optimization Algorithm. Journal of Physics: Conference 
Series, 1881, pp. 1-10. https://doi.org/10.1088/1742-
6596/1881/4/042059 
[8] Fu M, Wang D (2020). Research on Optimization of cold 
chain logistics distribution path of fresh products based on 
Hybrid Genetic Algorithm. IOP Conference Series: Earth 
and Environmental Science, 585, pp. 1-5. 
https://doi.org/10.1088/1755-1315/585/1/012131 
[9] Lu J C, Nie X Y (2019). Logistics distribution path 
optimization research based on adaptive chaotic 
disturbance flies optimization algorithm. IOP Conference 
Series Earth and Environmental Science, 237, pp. 1-8. 
https://doi.org/10.1088/1755-1315/237/5/052068 
[10] Lu S, He T, Zhou Q, Wen J, Liu Y, Zhang M (2020). 
Research on a Distribution-Outlier Detection Algorithm 
Based on Logistics Distribution Data. Journal of Physics: 
Conference Series, 1624, pp. 1-6. 
https://doi.org/10.1088/1742-6596/1624/4/042002 
[11] Chen J, Xu S, Chen H, Zhao C, Xue K (2020). Research 
on Optimization of Food Cold Chain Logistics 
Distribution Route Based on Internet of Things. Journal of 
Physics Conference Series, 1544, pp. 1-8. 
https://doi.org/10.1088/1742-6596/1544/1/012086 
[12] Wang C L, Wang Y, Zeng Z Y, Lin CY, Yu QL (2021). 
Research on Logistics Distribution Vehicle Scheduling 
Based on Heuristic Genetic Algorithm. Complexity, 2021, 
pp. 1-8. https://doi.org/10.1155/2021/8275714 
[13] Wang Y, Zhou H, Wang Y (2017). Research and 
application of genetic algorithm in path planning of 
logistics distribution vehicle. AIP Conference Proceedings, 
1864, pp. 1-5. https://doi.org/10.1063/1.4992864 
[14] Simic D, Kovaevi I, Svirevi V, Simic S (2015). Hybrid 
Firefly Model in Routing Heterogeneous Fleet of Vehicles 
in Logistics Distribution. Logic Journal of IGPL, 23, pp. 
521-532. https://doi.org/10.1093/jigpal/jzv011 
[15] Gu W, Liu Y, Wei L, Dong B (2015). A Hybrid 
Optimization Algorithm for Travelling Salesman Problem 
Based on Geographical Information System for Logistics 
Distribution. LISS, 8, pp. 1641-1646. 
https://doi.org/10.14257/ijgdc.2015.8.3.33