Analysis of Stochastic Integer Programming and Its Application in Real Life

Abstract

Uncertainty is inherent in real-world decision-making. Traditional deterministic models often fail to capture such variability, leading to sub optimal or infeasible results. Stochastic programming provides a robust mathematical framework for optimization under uncertainty. This study presents an overview of stochastic integer programming (SIP) and its applications. After introducing the fundamentals of stochastic programming, we review existing literature and highlight key methodologies such as two-stage and multi-stage models, LP-relaxation, and recourse strategies. Practical examples solved using AMPL and Mathematica are provided to demonstrate the applicability of SIP in real-life decision problems. The findings emphasize the significance of SIP as a versatile tool for optimization under uncertainty and as a foundation for future research.

Dhaka Univ. J. Sci. 74(2): 283–294, 2026 (July)