• Mathematical Modeling Course - CO2011 - Ho Chi Minh City University of Technology
• Semester: 231
• Duration: 6 weeks
• Topic: Stochastic Programming and Its Applications

This repository is dedicated to exploring and understanding the fascinating field of stochastic programming, a mathematical optimization approach that tackles real-world problems characterized by uncertainty.

Stochastic programming goes beyond traditional optimization methods by incorporating randomness into the decision-making process. In practical terms, it addresses situations where outcomes are not deterministic and can vary due to uncertain factors. This field provides powerful tools to make optimal decisions that are robust across a range of possible scenarios.

These represent the choices we need to make to achieve our goals. Stochastic programming helps us determine the best decisions, considering the uncertainties involved.

Defines the goal we want to achieve. Stochastic programming aims to optimize this objective function under uncertain conditions.

Conditions that must be satisfied in the decision-making process. Stochastic programming ensures that decisions meet these requirements across different scenarios.

Encompass the various possible outcomes or states of the system. Stochastic programming analyzes these scenarios to find optimal solutions that perform well on average.

Stochastic programming finds applications in diverse fields such as finance, supply chain management, energy, and, importantly for us, in the realm of computer engineering. As technology becomes increasingly intertwined with uncertainty, understanding stochastic programming becomes pivotal for making informed and robust decisions.