New revenue management problems for online platforms

In this thesis, we study new applications of revenue management in three different online platforms and develop models and techniques in these settings. In the first study, we consider an omnichannel platform selling its products through online and physical outlets. We design a robust inventory-sharing policy to allocate inventory between online and offline demands and coordinate fulfillment and transshipment decisions to minimize the retailer’s total expected cost. We compare our policy with another standard inventory-sharing policy using real data from a well-known retailer. In the second study, we investigate an online marketplace (OM) platform that operates as intermediary connecting sellers to consumers. An OM typically determines the assortment of sellers displayed on its platform and charges commission fees on the sales of selected sellers to maximize its expected revenue. Each selected seller, to maximize its profit, then decides the price of its product in response to other sellers’ behavior on the platform. To model the interactions between the OM and the sellers, we develop a Stackelberg game and consider two types of competition among the sellers: Bertrand and Cournot. We provide characterizations and insights of the OM’s optimal assortments and commission fee decisions for both types of competitions. We also introduce a monopoly model, in which the OM makes pricing decisions while guaranteeing that the selected sellers’ profits are no less than their target profits. We study how the proposed oligopoly and monopoly models affect the OM’s profit and total market share. In the third study, we incorporate requests’ choice behaviors when facing different pricing and promised service options with a dynamic routing problem allowing for deadline decisions in a unified framework for an online ride-sharing problem. This framework considers a ride-sharing platform that dynamically makes routing, pricing, and assortment decisions to offer vehicle options to different requests over a finite horizon time in a rural area. At most one request stochastically enters the platform at each period, and the request then selects one option or leaves the platform with no selection. The platform’s decisions will impact both the present and future requests’ decisions in the current period. To this end, we develop an approximation dynamic programming algorithm and derive a policy that is able to make all the decisions in real-time. In a comprehensive numerical study, we compare our policy with four benchmark policies and show that it outperforms the benchmark policies significantly.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Arian, Ebrahim

Contributors dc:contributor

• Chen, Xin
• Seshadri, Sridhar
• Wang, Qiong
• Etesami, Rasoul

• omnichannel retailing
• online marketplace
• ride-sharing platform
• assortment optimization
• pricing

Statement dc:rights

• Copyright 2022 Ebrahim Arian

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115551

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115551