Chance-constrained optimization is a research area that deals with decision-making under uncertainty by incorporating probabilistic constraints into optimization problems. In chance-constrained optimization, decision-makers aim to minimize or maximize a certain objective function while ensuring that the probability of violating a specified constraint remains below a certain threshold. This research area is commonly used in various fields such as finance, engineering, and supply chain management, where decision-makers need to make decisions in the presence of uncertain factors. Chance-constrained optimization provides a formal framework to model and solve these problems, taking into account the stochastic nature of the constraints. Overall, chance-constrained optimization is a valuable tool for tackling complex decision-making problems under uncertainty, providing a systematic approach to manage risk and make robust decisions.