Abstract

This blog post addresses the design of a sustainable logistic network for GreenForwarder—a multinational freight company—by balancing profitability, environmental emissions, and regional reach. A multi-objective network optimization problem is formulated over a graph that represents cities and potential transport connections. Using a modified Minimum Spanning Tree (MST) algorithm that incorporates capacity constraints and investment costs, the approach has been inspired by the Steiner Tree Problem and evaluates candidate edges dynamically by considering cost, emission factors, and a bonus for expanding regional reach. POC and LIVE differ significantly in their approaches, particularly in their starting points. POC begins by simplifying the “full” graph and focusing on optimizing its objective function, whereas LIVE prioritizes satisfying its unique constraint from the outset. For the MVP, two methods are employed to determine which approach would yield a better outcome, as the significance of profit’s contribution was unknown during the project’s initial stages. Detailed experimental results, analysis, and comparisons are provided in later sections.

Abstract

In this blog post, I rigorously analyze the forecasting performance of commodity price inflation models. I compare a conventional univariate autoregressive (AR) model with two mixed frequency models—the unrestricted MiDAS model and the HAR-MiDAS model. By integrating daily stock returns with monthly inflation data, I aim to capture market dynamics that standard techniques overlook. I evaluate model performance using robust statistical tests, RMSE comparisons, and the Diebold-Mariano test, and I explore the benefits of a rolling forecast origin. Throughout the analysis, I include detailed mathematical formulations, estimation results, tables, and graphical visualizations.

Abstract

In this blog post, I tackle the complex problem of shipping optimization for SABIC by developing and implementing a genetic algorithm (GA). The primary aim is to minimize operational costs by optimally scheduling vessel routes while considering constraints such as vessel capacity, draft limitations, fuel consumption, and cargo compatibility. I outline the complete mathematical formulation of the cost function, detail the GA’s operators (selection, crossover, mutation), and present comprehensive results—including cost savings, vessel utilization improvements, and on-time delivery rates. This work is supported by extensive numerical results and detailed cost tables.

Abstract

This blog post presents an innovative and comprehensive analysis of Portugal’s macroeconomic performance over the period 1970–2005. By integrating classical economic theory with advanced econometric modeling—including Vector Autoregression (VAR) and ARIMA approaches—we examine the intricate interplay between GDP growth, unemployment, and inflation. Our study revisits the traditional Phillips Curve framework and proposes an accelerated variant to better capture the nuances of Portuguese economic dynamics. The analysis not only identifies key recession periods but also offers robust policy implications for economic stabilization and investment strategy formulation.

Introduction

The idea for this blog originated on an 1 hour long boarding process on my last trip. The process of boarding an airplane appears deceptively simple; yet, it exhibits surprisingly high delays compared to boarding other forms of transportation such as buses or trains. This document aims to provide a mathematically rigorous explanation of why the boarding process is so inefficient. We build a model that captures both stochastic delays (due to bag stowing, seating interference, etc.) and systematic constraints imposed by the boarding procedure.

The ubiquitous nature of digital communication demands robust security measures to protect sensitive information across an increasingly interconnected world. This blog post provides an extensive analysis of Internet security, tracing its evolution from classical encryption techniques to modern cryptographic protocols and blockchain technologies. The work explores foundational principles, advanced cryptographic methodologies, and emerging trends such as quantum-resistant algorithms and zero-knowledge proofs. In addition, practical applications—including secure wireless protocols, HTTPS, and the development of a custom cryptocurrency—are critically examined. The study concludes with recommendations for future research and the implementation of next-generation security systems.

In this post, I outline a series of strategies I use to study more efficiently—strategies that allow me to maintain balance between academic work, personal development, entrepreneurship, and other commitments.

Complete Assigned Readings Before Class

Perhaps the most crucial practice is to read the assigned material prior to attending lectures. This habit transforms the lecture into a reinforcement and clarification session, rather than your first point of contact with the content.

Introduction

Bootstrapping is a resampling method that enables the estimation of the sampling distribution of a statistic by drawing repeated samples with replacement from the observed data. In simple terms, imagine you have a jar filled with cookies. Instead of baking a new jar of cookies to see how the average size might vary, you repeatedly pick a handful of cookies from the same jar (with replacement), calculate the average size for each handful, and then study how these averages vary. This variation gives you an insight into the reliability and variability of your original measurement.

To cultivate genuine critical thinking, one must first confront an uncomfortable but essential truth:

From the moment we are born, we are not taught how to think — we are taught what to think.

From childhood onward, we are immersed in a web of inherited ideas — shaped by parents, teachers, media, games, institutions, and cultural norms. We are not blank slates, but rather vessels gradually filled with beliefs we did not choose and assumptions we rarely question. The philosopher Michel Foucault might call this our epistemic conditioning: the invisible framework of thought shaped by power, culture, and history.

In this post, I present an in‐depth analysis of a project that investigates optimal location choices for competing firms using concepts from graph theory and game theory. The project models a set of cities as nodes in a graph and employs two primary methods: the iterated elimination of strictly dominated strategies and local search dynamics. I provide a detailed exposition of the mathematical foundations, algorithms, and extensions implemented to reflect more realistic scenarios. This is my answer to a university assignement.