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Development of Decision Support Tool for Transport Forwarding Company Operating within Netherlands and Italy

This paper investigates the development of a decision support tool for a Latvian transportation company operating in the Dutch and Italian markets. Models and algorithms for optimizing freight routes using Excel and Python are included. Digitalization of logistics processes is recognized as a key to improve efficiency and reduce costs. Two methods for solving the traveling salesman problem were examined and compared: Excel with Solver and Python with the NetworkX library.The methodology involved collecting data from Google My Maps, creating Excel spreadsheets, and developing Python software to automate route optimization. The results showed that both methods improved route planning, reducing time and cost, as well as reducing carbon footprint.The study emphasizes the importance of integrating technologies such as machine learning and big data into logistics to increase flexibility and adaptability. Recommendations were offered to further improve and implement these technologies for sustainable business development and increased competitiveness in international markets.

Author: Anastasija Škaduna

Supervisor: Berdymyrat Ovezmyradov

Degree: Professional Bachelor

Year: 2024

Work Language: English

Estimating Generalised Transport Costs of Road Freight Transportation in the Baltic Sea Region

This problem of estimating road transport costs in the Baltic Sea region is important for optimising cargo delivery expenses in local transportation and manufacturing sectors. The study incorporates a wider range of economic and logistical factors beyond the usual metrics of physical distance and travel time by using Generalised Transport Costs (GTC). Important factors like geodesic and road distances, travel times, fuel consumption, labor costs, tolls, and other overheads are identified. We utilise a unique dataset that analyses trips between centroids within each NUTS-2 region.The study confirms the GTC model by comparing calculated costs with established database values. Regression analysis uncovers key factors affecting transport costs, such as road distance, travel time, and tolls.Network analysis is used to map the routes in the region, focusing on finding paths that are both cost-effective and time-efficient. The analysis shows how small changes in routes can have a big impact on costs and efficiency.In conclusion, this thesis contributes to the knowledge of road freight transport costs in the Baltic Sea region, offering valuable insights for policymakers and logistics companies.

Author: Angelīna Ņekļudova

Supervisor: Francesco Maria Turno

Degree: Professional Bachelor

Year: 2024

Work Language: English

Assessing the Viability of Natural Language Processing Applications within an Electronic Checklist
System for Freight Forwarders: Rule-based Information Extraction from Cargo Descriptions.

This study investigates the application of Natural Language Processing (NLP) within electronic checklist systems to enhance cargo description and securing practices for freight forwarders. The logistics industry faces significant challenges due to complex and varied legislation and the need for autonomous validation tools for cargo securing. This research aims to develop a rule-based Named Entity Recognition (NER) model to standardize and automate the extraction of entities from cargo descriptions. Key components of this study include the development of an entity extraction mechanism using regular expressions and standardized codes. The research demonstrates the potential of NLP solutions to generate precise, dynamic checklists from detailed cargo descriptions, ensuring that all pertinent tasks are covered. The developed NER model's effectiveness is evaluated through a series of experiments, showcasing high precision, recall, and F1 scores, thus highlighting its practical applicability in real-world logistics operations. The findings underscore the importance of standardizing cargo-related information to facilitate the broader adoption of automated NLP solutions in the logistics industry.

Author: Nikita Mickevičs

Supervisor: Dmitry Pavlyuk

Degree: Professional Bachelor

Year: 2024

Work Language: English

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