Titulo Estágio
An AI backend for the Nexus Haulier Capacity Matching Application
Áreas de especialidade
Engenharia de Software
Sistemas Inteligentes
Local do Estágio
DEI/CISUC
Enquadramento
The NEXUS Agenda consortium, led by the Port of Sines, comprises 35 partners sharing the common goal of developing innovative solutions to achieve a Green and Digital Transition Agenda. The consortium represents the entire value chain, including port authorities, maritime and terminal operators, railway operators, carriers, dry ports, logistics operators, technology suppliers, importers, exporters, as well as universities and research institutes. The expertise and skills of these partners play a crucial role in realizing this pioneering Agenda.
Within the NEXUS framework, a key objective is to harness the vast pool of high-quality data available for the development of Big Data Analytics and Artificial Intelligence Solutions. This internship opportunity is part of Work Package 2 (WP2), which focuses on creating specialized multimodal network and logistics applications to tackle this challenge. All resulting products and services will be designed and demonstrated in collaboration with end users, including terminal operators, road and rail operators, dry ports, and authorities within the multimodal logistics networks.
One specific task within WP2 aims to optimize the utilization of haulier resources, leading to cost savings and faster shipments. This task involves leveraging data collected from other sources to gain insights into the current state of logistic networks. By applying artificial intelligence methods, we aim to develop optimization algorithms to maximize resource utilization to their highest possible capacity. This endeavor encompasses various challenges such as developing accurate forecasting mechanisms, efficient data collection, problem characterization, modeling, and solving the optimization problem. The proposed activities stem from these challenges and seek to address them effectively.
Agentic AI refers to artificial intelligence systems designed to act as autonomous, goal-driven agents that can reason, plan, and interact with environments, tools, and other agents — often powered by large language models (LLMs). Unlike traditional AI systems that are static or task-specific, agentic AI exhibits more flexible and adaptive behavior. These agents can take high-level objectives, break them into steps, make decisions, and use tools (such as APIs, databases, or other software) to accomplish complex goals.
This internship is the continuation of previous work that resulted in an application capable of storing data regarding hauliers, containers, origins, and destinations for the transportations. This application has a frontend, written in ReactJS, and a backend developed for the AWS cloud. The application also includes an optimization component developed by another student. Continuation of the work will involve engaging with key stakeholders and understanding their needs within the logistics network, to define missing requirements. Examples of missing requirements that could be developed during this internship are a further refinement of details, such as separating the driver from the haulier, integration with existing logistics management systems, definition of a cost structure for the transportation services, the provision of a user-friendly interface for easy interaction, and the ability to generate insightful reports and analytics for decision-making purposes.
Objetivo
In addition to refining the requirements, the main purpose of this internship is to improve the existing implementation, by replacing the business layer (which is part of the backend) by LLM-based AI agents. The business layer should be able to explore the available data to reply to complex text-based queries, instead of being fixed.
Agentic AI can be used to transform the business logic layer of the haulier capacity matching application. Instead of relying on rigid, rule-based logic embedded in the backend, the system will be enhanced with LLM-powered autonomous agents capable of interpreting complex natural language queries and interacting dynamically with data sources and optimization components. These agents will function as intelligent intermediaries, capable of reasoning over historical and real-time logistics data, generating actionable insights, and invoking backend tools or APIs to execute tasks such as forecasting, matching hauliers to shipments, or estimating transportation costs. By replacing static logic with adaptive, agent-based reasoning, the backend gains the flexibility to support a wide range of use cases and user intents — enabling more intelligent, responsive, and scalable decision support across the multimodal logistics network.
The entire work should run according to the following sub-objectives:
1. Requirement Gathering: engage with stakeholders from various entities within the project, to fully understand the domain of the problem, their needs and current state of the art. Collect and document detailed requirements for the haulier capacity matching application, ensuring a comprehensive understanding of the desired functionalities and system behavior. These requirements should include at least the following:
- Refinement of data for the optimization problem.
- Defining a cost structure for the transportation services, possibly based on an approach similar to Uber's.
- Integration with existing logistics systems.
- Provide reports and analytics.
2. Architecture and Design: Based on the requirements, improve the architecture of the application, taking into account scalability, and security.
3. Frontend Development: Evolve the user interface (UI) for the haulier capacity matching application to cope with the new requirements. Develop interactive visualizations, data entry forms, and result displays, providing stakeholders with an intuitive and user-friendly experience.
4. Integration and Testing: Integrate the frontend with the backend, ensuring seamless communication and functionality across the application. Perform testing, including unit tests, integration tests, and user acceptance tests, to ensure the application meets the specified requirements and performs as expected.
5. Deployment on AWS: Deploy the application on the AWS cloud infrastructure, configuring necessary services, such as compute resources, storage, and networking components. Optimize the application's performance and scalability to handle varying demands and data volumes efficiently.
Plano de Trabalhos - Semestre 1
- Gain insight on the problem. (2 months)
- Elicit requirements. (1 month)
- Define the architecture of the application. (1 month)
- Write intermediate report (1 month).
Plano de Trabalhos - Semestre 2
- Do the implementation. (3 months)
- Test. (1 month)
- Write final report (1 month).
Condições
The work should take place at the Centre for Informatics and Systems of the University of Coimbra (CISUC) at the Department of Informatics Engineering of the University of Coimbra in the scope of the Nexus research project (https://nexuslab.pt).
Project "Agenda Mobilizadora Sines Nexus". ref. No. 7113, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation.
An 990,98 euros per month scholarship is foreseen for 3 to 6 months, depending on funding availability. The attribution of the scholarship is not guaranteed and is subject to a public application.
Observações
This work is the continuation of previous work and involves cooperation with other students working in the same application.
Orientador
Filipe Araújo e Prof. Luís Macedo
filipius@uc.pt 📩