TSI logo 25 years
LV EN
Home > About Us > Structure and Government > Abstracts

DEGREE

PROGRAMME

FACULTY

YEAR

LANGUAGE

KEYWORDS

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

Optimizing Product Cost in Supply Chain

Optimizing product cost through simulation modeling offers a powerful approach to enhancing cost efficiency and decision-making in product development and manufacturing. Simulation modeling allows businesses to create detailed virtual representations of their processes, enabling them to experiment with different scenarios and strategies without the risks associated with physical trials. Simulation modeling supports iterative testing and optimization, allowing for the refinement of product designs and manufacturing processes. Ultimately, this approach enhances the ability to make informed, data-driven decisions, leading to more effective cost management and improved profitability. This abstract highlights the value of simulation modeling in optimizing product cost, emphasizing its role in providing actionable insights and fostering strategic improvements.

Author: Sofiya Andryuk

Supervisor: Mihails Savrasovs

Degree: Professional Bachelor

Year: 2024

Work Language: English

Table View
Text View