Price Optimization

Price optimization aims to determine the most profitable prices for goods or services, maximizing revenue while considering various constraints. Current research focuses on developing data-driven methods, employing machine learning techniques like gradient descent, reinforcement learning, and neural ordinary differential equations, to optimize pricing strategies across diverse sectors including e-commerce, insurance, and energy markets. These advancements address challenges such as incorporating fairness criteria, handling complex data structures (e.g., multi-dimensional capacity in air cargo), and improving the interpretability of pricing models for practical implementation. The resulting improvements in revenue generation and resource allocation have significant implications for businesses and resource management across various industries.