Students

This document presents CAPPM (Conversational Agents Platform with Pluggable Models), a platform designed to drive the development and testing of AI applications with total abstraction of the underlying models. CAPPM allows for the easy creation of intelligent agents that can be configured from custom documents, utilizing Retrieval-Augmented Generation (RAG) techniques to answer questions and solve tasks within specific information domains. The solution stands out for its flexibility in adjusting agent behavior and relies on an elastic and scalable cloud architecture that ensures maximum efficiency and adaptability in multi-user environments. In addition to detailing the architecture and providing a user guide, the document includes a system evaluation in a real-world environment. The analysis gathers user feedback, evaluates the models, identifies limitations, and outlines lessons learned along with proposals for future improvements.

The Cloud Computing paradigm and, in particular, serverless architecture, offers substantial advantages such as scale-to-zero and a strict pay-as-you-go model. However, most current vector databases rely on always-on instances, which guarantee low latency but drastically increase operational costs and reduce resource efficiency. This Bachelor’s Thesis aims to design and implement a fully serverless and dynamic vector database capable of supporting continuous data ingestion while remaining highly competitive with state-of-the-art solutions. To achieve this goal, the project builds upon a static baseline architecture provided by the CloudLab research group (URV). From this starting point, an iterative development methodology was applied where, through empirical analysis of an initial implementation, an optimized architecture was designed based on asynchronous state compaction and an innovative hybrid query model. The results demonstrate that the proposed architecture achieves superior performance, both in execution time and operational costs, compared to other commercial market options when subjected to massive analytical workloads (ingestion operations and grouped semantic queries or batch queries). Finally, it is concluded that, despite the intrinsic orchestration overhead in low-frequency use cases or isolated requests, the system represents a structurally robust and affordable solution to democratize large-scale vector storage.

This Final Degree Project presents the design and development of an automated auditing solution focused on identifying underutilized resources in hybrid environments based on VMware vCenter infrastructures and Amazon Web Services (AWS). The main objective of the project is to simplify the detection of inefficient resource allocation and optimization opportunities while reducing the manual effort required for infrastructure audits. The solution uses Ansible for orchestration and Python for data processing, integrating with VMware and AWS APIs to collect information from virtual machines, ESXi hosts, datastores, EC2 instances, EBS volumes and related resources. Several analysis rules are applied to detect oversized virtual machines, long-term inactive resources, duplicated IP addresses, workload imbalances and underutilized cloud services. The generated results are automatically consolidated into detailed reports and executive summaries delivered through Microsoft Teams. The validation process confirmed the correct operation of the solution and revealed multiple optimization opportunities across both VMware and AWS environments. The project required significant design, development, integration and testing efforts, combining automation, virtualization and cloud technologies into a centralized infrastructure auditing solution.

This Master’s Thesis presents a rigorous comparative analysis of three representative automotive production philosophies United States, Europe, and Japan using Discrete Event Simulation (DES) combined with a multi-objective optimization approach. The study is based on the development of equivalent simulation models that share the same product, process sequence, and input conditions, allowing the impact of production philosophy on system performance to be isolated. In a first stage, the baseline behavior of each model is analyzed without optimization, using key performance indicators such as throughput, work-in-process (WIP), lead time, and rework rate. Subsequently, saturation and breakdown scenarios are introduced to evaluate the structural robustness of each system under operational disturbances. In a final stage, a multi-objective optimization process is carried out through realistic structural modifications, mainly by adjusting parallelism at critical stations, while keeping processing times and arrival conditions unchanged. The results show that no universal optimal configuration exists; instead, each system exhibits inherent trade-offs between throughput, WIP, and lead time that strongly depend on the underlying production philosophy. Pareto front analysis is used to visualize these trade-offs and to assess how different philosophies respond to equivalent optimization actions. The thesis concludes that the integration of discrete event simulation with multi-objective optimization is a powerful framework for analyzing complex production systems and for deriving insights applicable to real industrial decision-making contexts.