The Optimization Model and Algorithm of Emergency Logistics

Vehicle Routing Problem Considering Random Demand and

Robustness

Mei Li, Qian Li and Yan Lai

Shaanxi Fashion Engineering University, Shaanxi, 712046, China

Keywords: Random Demand, Genetic Algorithms, Emergency Logistics, Vehicle Path.

Abstract: The role of vehicle routing in emergency logistics is very important, but there is a problem of unreasonable

driving routes. Traditional methods cannot solve the problem of vehicle routing in emergency logistics, and

the route is irrational. Therefore, a genetic algorithm is proposed to optimize the analysis of vehicle paths.

Firstly, random demand is used to evaluate the route, and the index is divided according to the vehicle route

requirements to reduce the vehicle path in the interfering factor. Then, random demand for emergency

logistics vehicle path requirements, form a vehicle route scheme, and result on vehicle paths Conduct a

comprehensive analysis. MATLAB simulation shows that under certain evaluation criteria, the genetic

algorithm has a good effect on the accuracy and vehicle route

of emergency logistics The safety is superior

to traditional methods.

1 INTRODUCTION

In the field of logistics and transportation, route

optimization is an important issue. Path optimization

can effectively reduce the driving distance and time

of logistics vehicles, and improve the efficiency and

effectiveness of logistics transportation (Ahmed and

Al-Otaibi, et al. 2023). However, in practical

applications, the problem of logistics vehicle path

optimization is often affected by factors such as

random demand and robust characteristics, resulting

in increased complexity of route optimization (Alpos,

and Iliopoulou, et al. 2023). This article will explore

the impact of random demand and robust

characteristics on logistics vehicle path optimization

and how to optimize accordingly (Andersen, and

Belward, et al. 2023).

1.1 The Influence of Random Demand

and Robust Characteristics on

Logistics Vehicle Path

Optimization

In logistics and transportation, random demand refers

to the uncertainty of customer demand. This

uncertainty may be due to sudden changes in

customer demand, fluctuations in volume, or other

reasons (Andrade, and Usberti, 2023). The existence

of random demand will make the path optimization

problem of logistics vehicles more complicated. This

is because as demand changes, the routes and

distribution of logistics vehicles also need to change

to meet the needs of customers (Averbakh, and Yu,

2023).

In addition, robust properties also have an impact

on the routing optimization of logistics vehicles.

Robust characteristics refer to the ability of a system

to remain stable and reliable in the face of

uncertainty. In logistics, the realization of robust

characteristics can be achieved through proper

planning (Barauskas, and Brilingaite, et al. 2023).

However, the presence of random demand can

undermine the robustness of logistics vehicles,

resulting in a decrease in the quality of path

optimization (Becker, and Gauthier, et al. 2023).

In summary, random demand and robust

characteristics have the following effects on logistics

vehicle route optimization:

Difficult to predict demand: The existence of

random demand can make the data required for

logistics vehicle routing optimization more complex

and unpredictable (Bouleft, and Alaoui, 2023). This

Li, M., Li, Q. and Lai, Y.

The Optimization Model and Algor ithm of Emergency Logistics Vehicle Routing Problem Considering Random Demand and Robustness.

DOI: 10.5220/0013545800004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 473-477

ISBN: 978-989-758-763-4

473

makes path planning more difficult, resulting in

increased complexity of path optimization problems.

Uncertainty in path planning: Due to random

demand, the path planning of logistics vehicles needs

to take into account possible future events (Caste,

Koch and Marenco, 2023), which makes path

planning more uncertain. This may lead to

adjustments and modifications to the path plan, which

will degrade the quality of the path plan (Chen, and

Zhou, et al. 2023).

Reduced robustness: The presence of random

requirements may lead to a decrease in the robustness

of path planning, which leads to a decrease in the

quality of path planning. This will make the

transportation efficiency and effectiveness of

logistics vehicles reduced (Chen, and Li, et al. 2023).

Reduction of optimization quality: The quality of

path optimization may degrade due to random

demand and robustness. Therefore, appropriate

methods and techniques are needed to address these

issues in order to optimize the path (Chirala, and

Sundar, et al. 2023).

1.2 Solution Scheme

In view of the impact of random demand and

robustness on the route optimization of logistics

vehicles, the following solutions can be adopted to

improve the quality and efficiency of route planning

(Fares, and Hassanien, et al. 2023).

Forecast demand: In logistics and transportation,

random demand can be predicted through historical

data and other relevant information. Through the

forecast of demand, the uncertainty of path planning

can be reduced, and the quality and efficiency of path

planning can be improved.

Increased resiliency: Due to the influence of

random demand and robustness, path planning needs

to take into account possible future events. Therefore,

it is necessary to increase the flexibility of path

planning so that it can be adjusted in time when

changes in the future.

Optimization algorithm: In the face of random

demand and robustness, path optimization algorithms

need to be more efficient, robust and flexible. Modern

optimization algorithms, such as genetic algorithms,

simulated annealing algorithms, and neural networks,

can be employed to improve the efficiency and

quality of path planning.

Optimization strategy: For the problems of

random demand and robustness, corresponding

optimization strategies can be formulated, such as

increasing spare resources and improving the

response speed to demand changes, so as to improve

the quality and efficiency of path planning.

In logistics, random demand and robustness have

a significant impact on route optimization. In order to

improve the quality and efficiency of path planning,

some corresponding solutions are required.

Specifically, the problems of random demand and

robustness can be dealt with by predicting demand,

increasing elasticity, optimizing algorithms and

optimization strategies, so as to improve the quality

and efficiency of route planning and realize the

efficient operation and effectiveness of logistics and

transportation.

In emergency logistics, the choice of vehicle path

is very important, and the environment of emergency

logistics also determines the complexity of the

vehicle routing problem. However, in the process of

vehicle route selection, there is a problem of poor

accuracy in the vehicle routing scheme, which brings

certain obstacles to emergency logistics

transportation. Some scholars believe that the

application of genetic algorithm to emergency

logistics analysis can effectively analyze the vehicle

route scheme and provide corresponding support for

vehicle route selection. On this basis, a genetic

algorithm is proposed to optimize the vehicle route

scheme and verify the effectiveness of the model.

2 RELATED CONCEPTS

2.1 Mathematical Description of the

Genetic Algorithm

The genetic algorithm uses computer simulation to

optimize the vehicle route scheme, and according to

the indicators in the vehicle route, find the unqualified

value in the emergency logistics, and integrate the

vehicle route scheme to make the final judgment

Feasibility of emergency logistics. Genetic

algorithms combine the advantages of computer

simulation calculations and quantify emergency

logistics, which can improve the rationality of vehicle

paths.

Hypothesis 1: The vehicle route requirement is

, the vehicle route scheme is

, the

satisfaction of the vehicle route scheme is

, and the

vehicle route scheme judgment function is

(0)

Uf≈

,As shown in Equation (1).

INCOFT 2025 - International Conference on Futuristic Technology

474

()

Ufv U

−

=+ ⋅



)

2.2 Selection of Vehicle Routing

Scheme

Hypothesis 2: The emergency logistics function is

()

, and the weight coefficient is

, then the

vehicle route requires unqualified emergency

logistics as shown in equation (2).

()= ( )

mf m f m

→−+







)

2.3 Analysis of Vehicle Routing

Schemes

Before the genetic algorithm is carried out, the

vehicle route scheme should be analyzed in multiple

dimensions, and the vehicle route requirements

should be mapped to the emergency logistics library,

and the unqualified vehicle route scheme should be

eliminated. First, emergency logistics is

comprehensively analyzed, and the threshold and

index weights of the vehicle route scheme are set to

ensure the accuracy of the genetic algorithm.

Emergency logistics is a systematic test of vehicle

routing schemes, which requires accurate analysis. If

the emergency logistics are in a nonnormal

distribution, their vehicle routing scheme will be

affected, reducing the accuracy of the overall vehicle

route. In order to improve the accuracy of the genetic

algorithm and improve the level of vehicle path, the

vehicle route scheme should be selected, and the

specific scheme selection is shown in Figure 1.

Vehicle

route

Driving route

Emergency

logistics

Path security

Path accuracy

Stochastic demand

Figure 1: The result of the selection of the vehicle routing

scheme

The survey of vehicle route scheme shows that the

vehicle route scheme presents a multi-dimensional

distribution, which is in line with objective facts.

Emergency logistics is not directional, indicating that

the vehicle route scheme has strong randomness, so it

is regarded as a high analytical study. Emergency

logistics meets the normal requirements, mainly

computer simulation operation adjusts emergency

logistics, eliminates duplicate and irrelevant schemes,

and supplements the default scheme, so that the

dynamic correlation of the entire vehicle route

scheme is strong.

3 OPTIMIZATION STRATEGIES

FOR EMERGENCY LOGISTICS

The genetic algorithm adopts the random

optimization strategy for emergency logistics, and

adjusts the vehicle route parameters to realize the

scheme optimization of emergency logistics. The

genetic algorithm divides the emergency logistics

into different vehicle route levels, and randomly

selects different schemes. In the iterative process, the

vehicle route scheme of different vehicle route levels

is optimized and analyzed. After the optimization

analysis is completed, the vehicle path levels of

different scenarios are compared to record the optimal

emergency logistics.

4 PRACTICAL EXAMPLES OF

EMERGENCY LOGISTICS

4.1 Vehicle Routing Profile

In order to facilitate vehicle paths, this paper takes

emergency logistics in complex situations as the

research object, with 12 paths and a test time of 12h

shown IN Table 1.

Table 1: Vehicle routing requirements

Scope of

application

Grade Accuracy Vehicle

ath

Path one ordinar

87.24 90.00

87.71 83.19

Path two ordinar

82.26 85.60

86.14 84.36

Path three ordinar

85.95 82.81

88.08 86.74

The vehicle routing process in Table 1 is shown in

Figure 2.

The Optimization Model and Algorithm of Emergency Logistics Vehicle Routing Problem Considering Random Demand and Robustness

475

Vehicle

route

Genetic

algorithm

Stochastic

demand

Path accuracyEmergency logistics

Figure 2: Analysis process of emergency logistics

Compared with traditional methods, the vehicle

routing scheme of genetic algorithm is closer to the

actual vehicle routing requirements. In terms of

random demand and distribution speed of emergency

logistics, genetic algorithms are superior to traditional

methods. From the change in vehicle routing scheme

in Figure 2, it can be seen that the genetic algorithm

has higher accuracy and faster delivery speed.

Therefore, the speed of the vehicle route scheme, the

rationality of the vehicle route scheme and the

summation stability of the genetic algorithm are

better.

4.2 Emergency Logistics Situation

The vehicle routing scheme of emergency logistics

contains non-structural information, semi-structural

information, and structural information. After the pre-

selection of genetic algorithm, the preliminary

vehicle path scheme of emergency logistics and the

vehicle path of emergency logistics are obtained

Analyze the feasibility of the scheme. In order to

more accurately verify the rationality of the

emergency logistics vehicle path, select the

emergency logistics with different vehicle route

levels, and the vehicle route scheme is shown in Table

Table 2: The overall picture of the vehicle routing scheme

Categor

Rationalit

Analysis rate

Path one 88.78 90.69

Path two 87.53 88.86

Path three 92.96 90.47

mean 87.68 87.90

89.89 88.64

P=2.13

4.3 Vehicle Routing and Stability of

Vehicle Paths

In order to verify the accuracy of the genetic

algorithm, the vehicle routing scheme is compared

with the traditional method, and the vehicle routing

scheme is shown in Figure 3.

Figure 3: Vehicle routing with different algorithms

It can be seen from Figure 3 that the vehicle path

of the genetic algorithm is higher than that of the

traditional method, but the error rate is lower,

indicating that the vehicle path of the genetic

algorithm is relatively stable, while that of the

traditional method Vehicle paths are uneven. The

average vehicle routing scheme of the above three

algorithms is shown in Table 3.

Table 3: Comparison of vehicle route accuracy of different

methods

Algorithm Vehicle

ath

Magnitude

of chan

Error

Genetic

algorith

91.77 93.96 93.51

Traditional

methods

90.65 88.97 90.69

P 88.26 87.72 89.06

It can be seen from Table 3 that the traditional

method has shortcomings in the rationality of vehicle

routes in emergency logistics, and emergency

logistics has changed significantly, and the error rate

is high. The general result of the genetic algorithm is

that the vehicle path is closer and better than

traditional methods. At the same time, the vehicle

path of the genetic algorithm is greater than 91%, and

the accuracy does not change significantly. To further

verify the superiority of genetic algorithms. In order

to further verify the effectiveness of the proposed

method, the genetic algorithm was generally analyzed

by different methods, as shown in Figure 4.

It can be seen from Figure 4 that the vehicle path

of the genetic algorithm is significantly better than the

traditional method, and the reason is that the genetic

algorithm increases the adjustment coefficient of

emergency logistics and sets the vehicle path

threshold, rejecting non-compliant vehicle routing

schemes.

INCOFT 2025 - International Conference on Futuristic Technology

476

Figure 4: The vehicle path of the genetic algorithm vehicle

path

5 CONCLUSIONS

Aiming at the problem that the path of emergency

logistics vehicles is not ideal, this paper proposes a

genetic algorithm and combines computer simulation

to optimize emergency logistics. At the same time,

the vehicle path distance and rationality of selection

are analyzed in depth, and the vehicle route collection

is constructed. Studies show that genetic algorithms

can improve the accuracy and stability of emergency

logistics, and can carry out general vehicle routes for

emergency logistics. However, in the process of

genetic algorithm, too much attention is paid to the

analysis of vehicle path, resulting in irrationality in

the selection of vehicle route indicators.

ACKNOWLEDGEMENTS

This subject originates from the 2021 Scientific

Research Plan of Shaanxi Provincial Department of

Education. Research on Emergency Logistics for

Uncertain Demand and Road Section Failure,No.:

21JK0037

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The Optimization Model and Algorithm of Emergency Logistics Vehicle Routing Problem Considering Random Demand and Robustness

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