Stochastic Profit Optimization in Agile Supply Chains Using Monte Carlo Simulations and Markov Decision Processes

Our paper presents a stochastic multi-objective optimization model {minimize cost, maximize profits} designed to manage supply and demand uncertainties in a two-tier supply chain. The model optimizes supplier selection and order allocation by incorporating key parameters such as defective rates, buyback agreements, and quantity discounts. Through Monte Carlo simulations and sensitivity analysis, the model evaluates the impacts of these parameters on supply chain performance, offering a resilient framework for making informed decisions. The research provides actionable strategies for enhancing operational efficiency, profitability, and customer satisfaction amidst supply chain uncertainties.