Learning Algorithms for Dynamic Inventory and Pricing Optimization Problems

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Learning algorithms aim to solve dynamic optimization problems in which the decision maker has limited or no prior information about either a part of or the entire system structure. Indeed, in many applications, the system is so complex that it may not be possible to lay out an exact theoretical model with all system parameters known in advance. In these settings, the decision maker needs to learn such information during the decision making process, e.g., by extracting information from the collected data, to design algorithms for improved system performance. This view of optimization as a dynamic learning process has become prominent in recent years and has led to some promising results. This research will develop efficient data-driven learning algorithms for dynamic operations optimization problems in supply chain management. It will be accomplished by incorporating and extending ideas and techniques from machine learning and stochastic optimization, and the effectiveness of the algorithms will be measured by regret, defined as its average loss (increment) in profit (cost) per unit time compared with a clairvoyant who has complete information about the underlying system structure. This project involves several disciplines such as manufacturing, computing, operations research, and business analytics, and the multidisciplinary approach will encourage participation from under-represented groups and positively impact graduate and undergraduate education. Efficient data-driven algorithms will be developed for several classes of dynamic operations optimization problems, including multi-product dynamic inventory control with stockout substitutions, multi-product pricing and inventory control under customer choice models, inventory and pricing optimization under changing and seasonal environments, dynamic optimization in competitive environments, dynamic inventory control and pricing with reference point effect, and dynamic joint operations and marketing decision making. The research integrates cutting-edge knowledge and ideas from various areas, such as statistics, game theory, machine learning, operations research, and behavioral sciences, and it will lead to efficient learning algorithms that perform well both theoretically and empirically. With the increasing availability of data in companies, the research from this project will help them better utilize data for intelligent pricing and inventory decisions, and increase revenue and minimize cost.