Advancements in Models and Algorithms for Management Science
type: CreativeWork
description: Management science is an interdisciplinary field that leverages a variety of analytical techniques to inform effective decision-making within businesses and organizations. It is a dynamic field that is continuously innovating as data becomes increasingly available and businesses leverage new digital technologies. As a result, there is a constant need to develop models and algorithms to address unique decision-making settings. This thesis is composed of three independent chapters, each of which proposes novel modeling insights and algorithmic solutions for real-world problems. Chapter 2 studies a mathematical model in online resource allocation where a decision-maker must efficiently allocate a scarce resource to patient and impatient customers. This study is motivated by recent advancements in on-demand online platforms (such as Uber and Instacart) where customers who are patient (e.g., can wait a few minutes for a ride) are offered a discounted price. Under this model, we develop a simple resource allocation policy that has provable theoretical guarantees under a competitive ratio analysis and is also easy to use in practice. Our work supports the managerial intuition that offering discounts for patient customers leads to more robust and efficient resource allocation. Chapter 3 addresses the challenge of organizing a large corpus of documents into an expert-defined labeling scheme without manual annotation or labeled training examples. This work is motivated by a collaboration with a major pharmaceutical company to streamline root cause analysis of deviations in the manufacturing process. In investigating a new deviation, quickly finding related historical deviations is crucial, but such deviation reports are not organized in a way to facilitate this task. This chapter proposes an innovative methodology called Document Classification with Reference Information (DCRI), which crucially leverages the existence of reference information, documents which describe the tax…
abstract: Management science is an interdisciplinary field that leverages a variety of analytical techniques to inform effective decision-making within businesses and organizations. It is a dynamic field that is continuously innovating as data becomes increasingly available and businesses leverage new digital technologies. As a result, there is a constant need to develop models and algorithms to address unique decision-making settings. This thesis is composed of three independent chapters, each of which proposes novel modeling insights and algorithmic solutions for real-world problems. Chapter 2 studies a mathematical model in online resource allocation where a decision-maker must efficiently allocate a scarce resource to patient and impatient customers. This study is motivated by recent advancements in on-demand online platforms (such as Uber and Instacart) where customers who are patient (e.g., can wait a few minutes for a ride) are offered a discounted price. Under this model, we develop a simple resource allocation policy that has provable theoretical guarantees under a competitive ratio analysis and is also easy to use in practice. Our work supports the managerial intuition that offering discounts for patient customers leads to more robust and efficient resource allocation. Chapter 3 addresses the challenge of organizing a large corpus of documents into an expert-defined labeling scheme without manual annotation or labeled training examples. This work is motivated by a collaboration with a major pharmaceutical company to streamline root cause analysis of deviations in the manufacturing process. In investigating a new deviation, quickly finding related historical deviations is crucial, but such deviation reports are not organized in a way to facilitate this task. This chapter proposes an innovative methodology called Document Classification with Reference Information (DCRI), which crucially leverages the existence of reference information, documents which describe the tax…
author: Yao, Yuanfan (Evan)
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2024-09
date modified: 2026-07-22T22:21:18Z
provider: MIT
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1721.1/157094