This paper focuses on the methods for determining how uncertainties affect people in everyday life. Since almost nothing is deterministic, anything might become impractical in real life. We will encounter many difficulties if we lack anticipatory ideas. The business organizations are facing uncertainties in demand, supply, cost of raw materials, prices of finished products etc. in everyday life. To address these uncertainties, this study will analyse stochastic Linear Programming Problems (SLPs) aiming to bridge the gap between theoretical concepts and practical applications. This paper specifically explores the effects of assuming stochastic pricing and demand in the Uncapacitated Facility Location Problem (UCFLP), shedding light on the variations in profit. We will develop a stochastic UFLP for a company of Bangladesh known as Unilever Company of Bangladesh and it may use for any company in the world. For this, we will collect and analyse the data from the company in stochastic atmosphere. The model will help the company to be able to aware of how uncertainties of demand, supply and other factors can affect the profit and loss of the company. This approach underscores the importance of considering various factors, including demand patterns, cost-effectiveness, and regional dynamics. In the model, we will consider the data from five different regions of Bangladesh.

Accurate option pricing is critical for the effective functioning of financial markets, providing traders and investors with the means to hedge risks and capitalize on market movements. Traditional models such as the Black-Scholes, Binomial Tree, Trinomial Tree, Monte Carlo Simulation, and the Garman-Kohlhagen model have long been the standard for option pricing. However, these models often face limitations in capturing market complexities and extreme events. We propose here a hybrid approach that combines Genetic Algorithm (GA) optimization with Backpropagation (BP) neural networks to enhance the precision of option pricing. It uses HS300 index stock data from 2013 to 2022, including stock prices, volumes, and price changes. The hybrid GA-BP model is tested for its ability to make more accurate price predictions. The model helps investors make better decisions by improving pricing strategies and managing risks effectively. The Hybrid GA-BP neural network model leverages the global search capabilities of GA to optimize the initial weights and biases of the BP neural network, thereby avoiding local minima and improving convergence rates. This integrated model is trained and tested on historical market data, with its performance benchmarked against traditional models. Empirical results demonstrate that the Hybrid GA-BP neural network model significantly outperforms traditional models in terms of pricing accuracy. The model shows superior precision when comparing actual market prices with predicted prices, reducing errors and increasing reliability. This enhancement in pricing precision can lead to more informed trading decisions and better risk management strategies. The findings of this research contribute to the growing body of knowledge in financial engineering by showcasing the potential of hybrid machine learning approaches in financial modeling. The Hybrid GA-BP neural network model presents a promising tool for practitioners and researchers aiming to improve option pricing methodologies in increasingly complex financial markets. 

This paper explores the use of stochastic optimization techniques to address the aircraft allocation problem under uncertain passenger demand. The proposed stochastic allocation model successfully meets the study’s objectives by demonstrating how uncertainty in passenger demand can be effectively incorporated into aircraft assignment decisions through a two-stage stochastic programming framework. Simulation results across multiple demand scenarios show that the model provides stable and adaptive allocations that minimize total cost while maintaining service quality, even under high variability. Incorporating the simple recourse approach enables post-decision flexibility, reducing penalties for unmet demand, and the use of Geometric Brownian Motion (GBM) offers a realistic representation of continuous demand fluctuations over time. These outcomes confirm the model’s practical value in bridging deterministic planning and real-time decision environments. While future research will focus on extending the model to a Markov Decision Process (MDP) framework and integrating real-time data streams, the current results establish a solid foundation by quantifying how uncertainty directly impacts fleet utilization, cost efficiency, and service reliability.