Supply Demand

Supply-demand balancing focuses on optimizing resource allocation to meet fluctuating demands efficiently and equitably. Current research emphasizes developing sophisticated algorithms, including primal-dual optimization, deep learning models (like Deep Black-Litterman and reinforcement learning agents), and nonlinear dynamics, to address this challenge across diverse applications such as revenue management, supply chain optimization, and ride-sharing platforms. These advancements aim to improve resource utilization, enhance market efficiency, and personalize service delivery, impacting fields from transportation and energy to online marketplaces. The ultimate goal is to create more robust and responsive systems capable of handling complex, real-world scenarios with varying levels of uncertainty and strategic behavior from involved agents.