Two Essays on Dynamic Pricing and Cooperative Game in Supply Chain Management

Abstract

Dynamic pricing and inventory management are critical research areas in Operations Management. In this thesis, we explore cyclic pricing in the emerging business model of live-streaming selling and address inventory optimization problems. The thesis is divided into two independent parts.

In the first essay, we consider continuous-time dynamic pricing in the presence of waiting customers for live-streaming selling with herding behaviors. Customers arrive at a constant rate over time with heterogeneous valuations. Customers purchase if the current price is less than their corresponding valuations. The remaining customers may leave or wait for a future live-streaming sale. The waiting customers exhibit a variety of herding behaviors when making waiting decisions, where these herding behaviors are characterized by the population-contingent leaving probability. Leveraging the waiting customers’ behaviors, the seller makes the pricing decision with a finite menu of prices each time and has the option to offer a lowest price to attract all waiting customers through a live-streaming sale. We build a continuous-time model by exploiting impulse control. We show that the optimal dynamic pricing policy can be either a pump and dump or generalized pump and dump cyclic pricing strategy, determined by the effective marginal population-contingent leaving rate. Moreover, the seller has no incentive to offer a regular price which is smaller than the myopic optimal price. If customers exhibit reverse-, non-, or weak-herding behavior for waiting, a pump and dump cyclic pricing strategy is optimal. On the other hand, if they have the strong-herding behavior, a generalized pump and dump cyclic pricing strategy becomes optimal. We provide analytical solutions for the cycle length and the optimal pricing path. Our findings provide guidelines on how to carry out dynamic pricing for live-stream selling. Numerically, we show that ignoring cyclic pricing can lead to a significant revenue loss.

In classical inventory centralization games, retailers collaborate to meet demand characterized by stochastic uncertainty in joint distributions. In the second essay, we further investigate these games under ambiguity in the joint demand distributions. Specifically, we assess the worst-case joint demand distributions within a distributionally robust optimization framework. Utilizing cooperative game theory, we propose core allocation schemes for these games, using popular ambiguity sets from the distributionally robust optimization literature based on (i) the lifted ambiguity sets and (ii) the Wasserstein distance to an empirical reference distribution. Furthermore, we demonstrate that the core allocation scheme under classical stochastic demand can be implemented in a decentralized manner, showing that the cost allocation scheme corresponds to a market equilibrium.

Date of Award	26 Sept 2024
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Yimin YU (Supervisor)