Essays in Revenue Management

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May 2023

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Abstract

This dissertation consists of three main chapters that develop new techniques for revenue management and also demonstrate novel practical applications. These chapters focus on developing pricing policies when prices of products are restricted to finite price points, analyzing the performance of state-independent policies in network revenue management, and optimizing pricing and capacity decisions in railways. In Chapter 2, we consider a stochastic multiproduct dynamic pricing problem in which the prices of the products are restricted to discrete and finite sets. The focus of this work is to analyze the deterministic variant of this problem (wherein customer-arrival rates are deterministic), which is a key subproblem whose solution can be used to build attractive policies for the stochastic variant. We obtain efficient and effective solutions to the deterministic problem, and prove worst-case optimality gaps for our solutions. Chapter 3 studies the canonical network revenue management problems introduced in (Gallego and Van Ryzin, 1997) and (Liu and Van Ryzin, 2008). For all the state-independent policies developed in the literature for these problems, we show that the optimality gaps scale proportionally to ?k, where k is the scale of demand and supply. In Chapter 4, we study revenue management in railways which distinguishes itself from that in traditional sectors such as airline, hotel, and fashion retail. In railways, capacity is substantially more flexible leading to a genuine necessity for the joint optimization of prices and capacity. Discreteness in capacity and passengers traveling by standing in unreserved coaches are other features unique to railways. Motivated by our work with a major railway company in Japan, we analyze the problem of jointly optimizing pricing and capacity, and develop four asymptotically optimal policies. We demonstrate the attractive performance of our policies on test-suites of instances based on real-world operations of the high-speed “Shinkansen” trains in Japan, and also develop rich insights based on our numerical results.

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Business Administration, Management

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