Time Varying Demand

Time-varying demand, characterized by fluctuating customer needs over time, presents a significant challenge across various sectors, demanding efficient resource allocation and optimized decision-making. Current research focuses on developing robust algorithms and models, including reinforcement learning, graph neural networks, and Bayesian methods, to predict and respond to these dynamic patterns in diverse applications like supply chain management, revenue optimization, and fleet scheduling. These advancements aim to improve operational efficiency, reduce costs, and enhance service levels by enabling proactive, data-driven strategies that adapt to unpredictable demand fluctuations.