Approximate Dynamic Programming for Perishable Asset Management with Applications in Dynamic Pricing, Capacity Allocation and Revenue Management

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Many industries face the problem of managing multiple types of perishable physical assets in an environment where the demand for the assets is random and the different assets interact with each other. Examples are many. Fashion good retailers make stocking decisions for clothing items that act as substitutes. Airlines price millions of itinerary products that use the aircraft capacities over the same airline network. Health clinics allocate appointment and operating room slots to the patients with different priorities. Despite the recurrent nature of these asset management problems, the optimization models and algorithms for simultaneously managing the inventories of multiple perishable assets are largely limited to either deterministic approaches that ignore the uncertainty altogether or static approaches that ignore the evolution of the system over time. This grant provides funding for the development of sophisticated models and algorithms for large-scale perishable asset management problems that arise in a variety of settings.The results of this research will be particularly useful for pricing, capacity allocation, product assembly and revenue management problems. The goal is to develop optimization models and algorithms that (i) appropriately capture the randomness in the system, (ii) scale easily to handle realistic problem sizes, (iii) apply independent of the particular application setting, (iv) provide performance guarantees, and (v) make reasonable assumptions about data availability. The plan is to demonstrate that asset management problems share common features and one can develop a unified approach for them. The course offerings on decision-making under uncertainty will significantly benefit from this research. Management simulation games, which put the students in command of a realistic asset management scenario, will be programmed and incorporated into the courses. High school and undergraduate students will gain early research experience. Data sets for pricing and revenue management problems will be developed and made available to the research community.