=Paper=
{{Paper
|id=Vol-3641/paper1
|storemode=property
|title= Artificial Intelligence-Driven Cargo Optimization Service for Logistics
|pdfUrl=https://ceur-ws.org/Vol-3641/paper1.pdf
|volume=Vol-3641
|authors=Solomiia Fedushko,Yuriy Syerov,Michal Gregus
|dblpUrl=https://dblp.org/rec/conf/profitai/FedushkoSG23
}}
== Artificial Intelligence-Driven Cargo Optimization Service for Logistics==
https://ceur-ws.org/Vol-3641/paper1.pdf
Artificial Intelligence-Driven Cargo Optimization Service
for Logistics
Solomiia Fedushko1, Yuriy Syerov1,2 and Michal Gregus1
1 Department of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava,
Odbojรกrov 10, 820 05 Bratislava, Slovakia
2 Social Communication and Information Activity Department, Lviv Polytechnic National University, Bandera 12, 79000
Lviv, Ukraine
Abstract
The development of forwarding services is becoming more and more dynamic every day. Observing this
trend, we should look for solutions that will provide customers with comfort, speed, and quality of
service. This article introduced an intelligent forwarder model that runs as a web application. The
solution model and methods used to implement the system and the algorithm that uses elements of
artificial intelligence are presented. In logistics, a freight forwarder is a nonvessel operating common
carrier that helps coordinate shipments and ensure the smooth transportation of goods from producers
or manufacturers to end-users, markets, or final distribution points for corporations or individuals.
Software that is based on artificial intelligence can make the forwarder's work more effective. The
particle swarm optimization algorithm is a heuristic optimization method inspired by the collective
behavior of social agents observed in nature, such as bird flocking, bee swarming, and fish schooling. In
this paper, we propose a Cargo Optimizer service that is an expert system supporting determining the
most profitable sets of orders using AI - more precisely, it will be the particle swarm optimization
algorithm. In the scope of these tests, we did some actions on the level of the graphical user interface to
test the connectivity of the whole system. Tests prevent unexpected operations and make the system
safe and optimal. The comparison will consist of comparing the algorithms in terms of speed and quality
of the data obtained.
Keywords
Artificial intelligence, cargo optimization, logistics, particle swarm optimization, freight forwarding 1
1. Introduction
Logistics [1] is currently one of the fastest-growing industries. It involves planning and
implementing the flow of products from the point of production to the point of sale or
consumption. Freight forwarders coordinate the supply chain so that cargo is transported
optimally. They find employment in many areas of the economy, including trade, services, and
shipping. It is a very responsible job, with no room for error. In a situation where such a mistake
occurs, it can be very costly for the shipping company [2, 3]. Many transport companies are
regularly established around the world. They are entering any city where the squares are a typical
sight. However, it is not only the domain of cities because in the surrounding areas, and even in
many smaller towns, the view of various transport companies is seen, not to mention the
hundreds of trucks encountered on the roads daily.
Regarding shipping companies, the increase in their number results from the demand for
shipping services. If a given country's economy is developing at a fast pace, the consequence of
this is an increasing number of production plants and warehouses. It is connected with the need
to transport the goods they produce to various destinations, both within and outside of this
country [4]. The IT industry has a more substantial impact on shipping services every day.
ProfIT AI 2023: 3rd International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2023), November
20โ22, 2023, Waterloo, Canada
solomiia.s.fedushko@lpnu.ua (S. Fedushko); yurii.o.sierov@lpnu.ua (Yu. Syerov); michal.gregus@fm.uniba.sk
(M. Gregus)
0000-0001-7548-5856 (S. Fedushko); 0000-0002-5293-4791 (Yu. Syerov); 0000-0002-8156-8962 (M. Gregus)
ยฉ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Programmers create software [5] that allows you to replace the activities that the forwarder does
daily. Among other things, software based on artificial intelligence can make the forwarder's
work more effective. In computer science [6-8], artificial intelligence (AI) [9-11] represents a
significant area of exploration. Its focus lies in developing computer systems capable of emulating
human-like cognitive processes, including learning, reasoning, and self-correction.
An Application Programming Interface (API) [12] serves as a software intermediary that
facilitates communication between two applications. Traditionally, a freight forwarder [13-16]
acts as an intermediary connecting shippers or importers with carriers. In a broader context, a
freight forwarder, also known as a nonvessel operating common carrier, assists in coordinating
shipments and ensuring the smooth transportation of goods from producers or manufacturers to
end-users, markets, or final distribution points for corporations or individuals.
The Knapsack problem [17] involves optimizing the packing of a knapsack with integer
volume F using objects from K different classes to maximize profit. Objects from class k, where k
= 1, ..., K, consume integer units of knapsack volume and produce a profit rk. If the volume of the
knapsack F is an integer multiple of the volumes bk, k = 1, ..., K, a straightforward solution is to fill
the knapsack with objects from the class k with the highest profit-to-volume ratio rk/bk. Dynamic
programming can address cases where the knapsack volume ratio is not an integer multiple of
object volumes with a time complexity of O(FK).
The particle swarm optimization algorithm (PSO) [18] is a heuristic optimization method
inspired by the collective behavior of social agents observed in nature, such as bird flocking, bee
swarming, and fish schooling. The freight market [19] serves as the nexus where buyers and
sellers of shipping services converge to negotiate deals. Various categorizations can be applied to
this marketplace.
2. Related Work
The optimization of cargo logistics through artificial intelligence (AI) has drawn inspiration
from various swarm intelligence models and collective decision-making mechanisms observed in
natural systems. A diverse range of studies has explored the application of swarm-based
algorithms in different contexts, providing valuable insights into the potential enhancements
achievable through mimicking biological phenomena.
Okubo's seminal work [20] delves into the dynamic aspects of animal grouping, shedding light
on swarms, schools, flocks, and herds. Although focused on biological systems, the principles of
self-organization and coordinated behavior in groups offer conceptual foundations applicable to
artificial systems, especially in optimizing logistics operations.
Schumann [21] extends this perspective by exploring the transition from swarm simulations
to swarm intelligence. This transition highlights the relevance of understanding collective
behaviors and adaptive strategies exhibited by swarms, paving the way for informed design
choices in AI-driven cargo optimization services.
Karaboga et al. [22] present a comprehensive survey of the artificial bee colony (ABC)
algorithm, showcasing its potential applications. While the ABC algorithm is rooted in the
foraging behavior of honeybees, its adaptability and efficiency have inspired applications in
optimization problems, including those encountered in logistics.
Ventocilla's work [23] introduces a swarm-based approach to area exploration and coverage
inspired by pheromones and bird flocks. This research provides valuable insights into
decentralized decision-making mechanisms that can be harnessed for efficient logistics resource
allocation and route planning.
Pourpanah et al. [24] comprehensively review artificial fish swarm algorithms, emphasizing
recent advances and practical applications. The piscine-inspired algorithms offer novel
perspectives for optimizing cargo routing and resource allocation in logistics scenarios.
Bakar [25] explores the understanding of collective decision-making in natural swarm
systems, presenting applications and challenges. This work contributes to the theoretical
foundations of swarm intelligence, offering a deeper comprehension of decision-making
processes that can inform the design of AI-driven cargo optimization services.
The literature further includes contributions from Sadiku and Musa [26], Li and Clerc [27], and
Zhang et al. [28], offering insights into multiple intelligences, swarm intelligence handbooks, and
applications in various domains. These works provide a rich background for integrating swarm
intelligence principles into AI-driven cargo optimization.
Finally, Abidin et al. [29] introduce swarming robotics and discuss emerging trends in
application development. This work broadens the scope of swarm intelligence, suggesting
potential synergies between AI-driven cargo optimization and robotic swarm systems for
efficient logistics operations.
The analysis of these works contributes to a holistic understanding of swarm intelligence
models, offering a diverse array of inspirations and methodologies applicable to developing an
artificial intelligence-driven cargo optimization service for logistics.
3. Description of the use of swarm optimization algorithm in the
Cargo Optimizer
Cargo Optimizer would be an expert system supporting determining the most optimal trailer
loading configurations. It would fetch data from the Distance Matrix API from Google Maps and
data from the customer. Customers using our service (let us assume that it is some
expedition/transportation company) would send a request to the freight market containing
filters used to orders (max and min weight, max and min volume, max and min cost, location, and
search radius). After getting a list of orders, he can choose which to send to Cargo Optimizer.
The user has to add the maximum weight and volume of the trailer to the request with the order
list to Cargo Optimizer [30]. That way, we will obtain two lists of the most profitable orders (best
income/cost ratio) that can be done in one go. This service's logic would collect data from clients
and external services to generate the most profitable sets of orders using AI. More precisely, it
will be the particle swarm optimization algorithm. The PSO algorithm can be used to optimize the
function of many variables, and in this case, it will be used to find a set of goods that will fill the
cargo space most cost-effectively.
Figure 1: UML Component Diagram for Cargo Optimizer
Figure 1 shows the dependencies between the components. There is a Graphical User Interface
mainly on the outside (on the customer side). It directly connects to the
FreightMarketDataFetcher and our main component, Cargo Optimizer. The latter has a
connection to the RoutingDataFetcher. The PSO algorithm adapted to the needs of the cargo
optimizer will retrieve data on available orders at the input. Each particle will contain a drawn
set of goods with attributes such as cost, weight, cubic capacity, distance in meters, and duration
in minutes between depot and destination. The study contains the fitness value returned by the
target function for this set of goods. By target function, we mean a function that will take the list
of goods as an input parameter and then convert the profitability value based on their attributes
and return it as fitness. Apart from the attributes listed above, each particle will have the best set
of goods remembered and the related fitness. Furthermore, the best set of goods will be
remembered for the entire swarm, which will be used in the optimization process to update the
speed and position of the particles. The particles will be updated according to the assumptions of
the original PSO, and the condition for the algorithm to end is a specified number of iterations,
after which two of the most profitable lists of orders will be returned.
A trailer with a maximum weight of W, a maximum volume of V, and a set of N elements {x1,
xi, ..., xN}, each element having a specific value ci, weight wi, and volume vi.
๐๐๐ฅ๐๐๐๐ง๐ โ๐
๐ = 1 ๐๐ ๐ฅ๐ , where xi = 0, 1 and i = 1, ..., n (1)
๐ ๐ (2)
๐ค๐๐กโ ๐๐๐๐ ๐ก๐๐๐๐๐ก๐ : โ ๐ค๐ ๐ฅ๐ โค ๐ ๐๐๐ โ ๐ฃ๐ ๐ฅ๐ โค ๐
๐=1 ๐=1
The function which is responsible for determining the value of an order has been determined as
follows:
๐๐๐ ๐ก (3)
๐ฃ๐๐๐ข๐ =
๐ค๐๐๐โ๐ก + ๐ฃ๐๐๐ข๐๐ + ๐๐๐ ๐ก๐๐๐๐ + ๐๐ข๐๐๐ก๐๐๐
The cost, weight, volume, distance, and duration at the beginning of the program are scaled to
a range of [0,1], so each has the same meaning. Therefore, in this case, the optimization task is
based on finding a list with a maximum value, which is the sum of the values of all elements in
that list. To provide more freedom for the user, to return the list with the second highest value so
that he can finally decide which list of orders suits him better.
3.1. PSO algorithm adapted to the Cargo Optimizer's diagram and pseudocode
Present a workflow diagram better to illustrate the logic operation in the Cargo Optimizer.
Furthermore, it was decided to present the logic operation differently. The pseudocode that
describes the contents of Figure 3.
Figure 2: Pseudocode for Cargo Optimizer
Figure 3: Workflow diagram for Cargo Optimizer
The pseudocode presented in Figure 3. contains instructions on how to use the PSO algorithm
implemented for Cargo Optimizer. This is how we tried to explain its operation step by step. What
is worth noting is the fact that the attributes of the order and the way in which the value of a given
order differs from the original backpacking problem. Returning the two best-order lists is also
our bonus.
3.2. Automatic Tests
The cargo optimizer, as an expert system, should be reliable. It is essential to prove how reliable,
fault-proof and optimized it is. To achieve this, the system will be tested in many ways, both
manually and automatically.
Automatic tests are vital to developing and deploying any modern software project. It was
created using particular testing frameworks, usually written in the same programming language
as the program. These testing frameworks allow us to make assertions regarding parts of code,
which usually define some particular logic, e.g., computing average value or realization of sorting
algorithm. We can compare the characteristics returned values by function and check them
against our expectations. It is also possible to mock-test objects and monitor their internal
behavior, such as the order of called functions or used arguments [31].
3.2.1. Unit Test
Unit testing is the most basic technique of all testing approaches. It tests tiny fragments of code,
primarily single functions or methods that fulfill one purpose. Outside dependencies, such as web
services [32, 33] or database connections, are avoided. If some outside dependencies are mixed
with logic, they are replaced with mocked connections returning some fixed values [34].
RoutingDataFetcher
Figure 4: UML component diagram - between the components and the RoutingDataFetcher
component marked with red color
Figure 4 shows the dependencies between the components and the RoutingDataFetcher
component marked in red. We posted it to illustrate which component is currently being tested.
In the scope of unit tests for RoutingDataFetcher, we tested internal functionalities of this
component, such as:
โข Parsing location-related data obtained from an external API,
โข parsing responses from external API,
โข computation of distance in a straight line between two points on the surface of the earth,
โข construction of addresses for requests to external API.
FreightMarketDataFetcher
Figure 5: Example figure caption - Dependencies between the components and the
FreightMarketDataFetcher component marked with red color
Figure 5 shows the dependencies between the components and the FreightMarketDataFetcher
component marked in red. We posted it to illustrate which component is currently being tested.
In the scope of unit tests for RoutingDataFetcher, we tested internal functionalities of this
component, such as:
โข the randomness of generated lists of orders,
โข removal of diacritics,
โข parsing contents of files with customer/location-related data.
CargoOptimizer
Figure 6: UML component diagram - dependencies between the components and the
CargoOptimizer component marked in red.
Figure 6 shows the dependencies between the components and the CargoOptimizer component
marked in red. We posted it to illustrate which component is currently being tested.
In the scope of unit tests for RoutingDataFetcher, we tested internal functionalities of this
component, such as:
โข realization of the PSO algorithm,
โข Normalization of the input data.
3.2.2. Integration tests
The test goal is to execute the system to verify its behavior and reveal possible failures. The
integration testing phase is performed to find errors in the unit interfaces [35] and systematically
build up the entire software system structure [36 -38]. For example, in our system, one of the
components is Cargo Optimizer, whose purpose is to implement a PSO algorithm to compute a
list of the most profitable order sets.
In the case of this component, we will check whether it behaves as expected on the level of the
enabled interface, but we will not check what exact processes are going on while producing
results.
RoutingDataFetcher and Distance Matrix API
Figure 7: UML component diagram โ dependencies between the components. The
RoutingDataFetcher and Distance Matrix API are marked in red.
In Figure 7, we can see the dependencies between the components. RoutingDataFetcher and
Distance Matrix API are marked red to show what part of the project was tested.
In the scope of integration tests for RoutingDataFetcher, we tested the following:
โข connectivity between the internal service of our system (RoutingDataFetcher) and the
external Distance Matrix API.
CargoOptimizer and RoutingDataFetcher
Figure 8: UML component diagram โ dependencies between the components. The cargo
optimizer and the routingDataFetcher are marked red.
In Figure 8, we can see the dependencies between the components. CargoOptimizer and
RoutingDataFetcher are marked red to show what part of the project was tested.
In the scope of integration tests for RoutingDataFetcher, we tested the following:
โข connectivity between internal services: CargoOptimizer and RoutingDataFetcher.
3.2.3. Performance tests
Performance tests are necessary when a system is based on performance-dependent
functionalities. Successful products should not force customers to wait long for desired results or
exceed some designated limit. In our system, it is strictly bound to the execution time of the PSO
algorithm.
For different sets of inputs, we will require that results be produced within some designated
time limit [39].
Figure 9: UML component diagram - Dependencies between the components and the
CargoOptimizer component marked in red color
Figure 9 shows the dependencies between the components and the CargoOptimizer component
marked in red. We posted it to illustrate which component is currently being tested.
In the scope of unit performance for RoutingDataFetcher, we tested the following:
โข speed and scalability of the realization of the PSO algorithm for different input sizes,
โข stability of the provided realization of the PSO algorithm.
3.2.4. End-to-end tests
End-to-end tests are used to check the functioning of the whole system, checking logic enabled
for customers. In this case, all dependencies are included in the testing process: the system's
internal components, read/write operations on files, connections to third-party data providers,
and whatever is needed for the provided system. Put bluntly, this kind of testing checks whether
a provided product fulfills its purpose [40].
Figure 10: UML component diagram โ dependencies between the components
Figure 10 shows the dependencies between the components. We mark the components tested
during the E2E test with a red rectangle. In the scope of this test, we use a client in the form of a
program that uses a testing framework instead of the graphical user interface to simplify the
testing process.
In the scope of unit performance for RoutingDataFetcher, we tested the following.
โข overall connectivity and correctness of the propagated data between system components.
4. Manual tests
These tests are conducted without using remarkable testing and frameworks, automating testing
and making test cases repeatable. Usually, manual tests are performed by company or beta
testers, which are used to simulate the behaviors of actual customers. It means that what is tested
is the enabled user interface (mainly graphical user interface), its logic, reliability, and fault-
proofness. An important part of manual testing is the evaluation of user experience [41] โ User
Experience is an area focused on maximizing the comfortability of interfaces enabled for end
users [42].
Figure 11: UML component diagram - manual tests with a red rectangle
Figure 11 shows the dependencies between the components. We marked the components being
tested during manual tests with a red rectangle.
In the scope of these tests, we performed some actions on the level of the graphical user
interface to test the connectivity of the entire system and enabled the elements of the graphical
interface.
Figure 12 shows a manual system test to check if a user can proceed to the next step without
filling in the required data. Missing data will appear on the screen, so the test was successful.
Figure 12: The screenshot shows a manual test.
Figure 13: The screenshot shows the test result.
Figure 13 shows a screenshot of a manual test to retrieve information on the optimal order lists
generated based on previously entered data.
The test was successful, and two best-order lists were displayed on the screen.
4.1. Queuing model
In this report, we also decided to present a queueing model. For analysis, we have selected the
main component of our service, Cargo Optimizer, because it is the most important to our
customers.
To calculate the queuing model factors that allow us to evaluate the operation of our service,
we decided to simulate the operation and estimate the input data. M/M/c was selected as the
queueing model. As for the arrival rate ฮป, we chose a value of 5 requests per second, the service
rate ฮผ equals 3, and the number of servers m of 2.
The calculation of coefficients is as follows [43]:
โข utilization of the server (4):
๐ (4)
๐= = 0,833
๐ โ ๐
โข profitability of an empty system (5):
๐โ1 (5)
(๐๐)๐ (๐๐)๐
๐0 = 1/ [โ + ] = 0,091
๐! ๐! โ (1 โ ๐)2
๐=0
โข mean number of customers in the queue (6):
๐
๐) (6)
๐0 โ (๐ โ ๐
๐ฟ๐ = 2
= 3,788
๐! โ (1 โ ๐)
โข mean wait in the queue (7):
๐ฟ๐ (7)
๐๐ = = 0,758
๐
โข mean wait in the system (8):
1 (8)
๐ = ๐๐ + = 1,091
๐
โข mean number of customers in the system (9):
(9)
๐ฟ = ๐ โ ๐ = 5,455
5. Conclusion
The intelligent forwarder system is created according to previously planned assumptions. The
system communicates with external API's and processes data received from them.
Communication occurs between the components that store data, those that process it, and those
responsible for displaying it. An essential element of the system is an algorithm based on artificial
intelligence. It is a particle swarm optimization algorithm whose task is to return the optimal list
of orders based on the data entered by the user.
The system implementation also includes many tests. Tests help to prevent unexpected
operations and make the system safe and optimal.
The Cargo Optimizer module, which uses elements of artificial intelligence and is the essential
point of the system, also uses a queueing model. This is to improve the system performance for
multiple users.
The system can be improved in the future by using a different algorithm for the cargo
optimizer. A possible algorithm to use is a genetic algorithm, which operates similarly to the
current algorithm. The two algorithms can then be compared based on their speed and the quality
of data obtained.
Acknowledgments
This study is funded by EU NextGenerationEU through the Recovery and Resilience Plan for
Slovakia under project No. 09I03-03-V01-000153.
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