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New approaches for integrating revenue and supply chain management
(Massachusetts Institute of Technology, 2014)
First, we describe a general framework called online customer selection that describes natural settings where suppliers must actively select which customer requests to serve. Unlike traditional revenue management models ...
Statistical learning for decision making : interpretability, uncertainty, and inference
(Massachusetts Institute of Technology, 2015)
Data and predictive modeling are an increasingly important part of decision making. Here we present advances in several areas of statistical learning that are important for gaining insight from large amounts of data, and ...
Data-driven optimization and analytics for operations management applications
(Massachusetts Institute of Technology, 2013)
In this thesis, we study data-driven decision making in operation management contexts, with a focus on both theoretical and practical aspects. The first part of the thesis analyzes the well-known newsvendor model but under ...
Data-driven decision making in online and offline retail/
(Massachusetts Institute of Technology, 2020)
.Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make personalized ...
Dynamic optimization in the age of big data
(Massachusetts Institute of Technology, 2020)
This thesis revisits a fundamental class of dynamic optimization problems introduced by Dantzig (1955). These decision problems remain widely studied in many applications domains (e.g., inventory management, finance, energy ...
Practical applications of large-scale stochastic control for learning and optimization
(Massachusetts Institute of Technology, 2018)
This thesis explores a variety of techniques for large-scale stochastic control. These range from simple heuristics that are motivated by the problem structure and are amenable to analysis, to more general deep reinforcement ...
Data, models and decisions for large-scale stochastic optimization problems
(Massachusetts Institute of Technology, 2016)
Modern business decisions exceed human decision making ability: often, they are of a large scale, their outcomes are uncertain, and they are made in multiple stages. At the same time, firms have increasing access to data ...
Dynamic, data-driven decision-making in revenue management
(Massachusetts Institute of Technology, 2018)
Motivated by applications in Revenue Management (RM), this thesis studies various problems in sequential decision-making and demand learning. In the first module, we consider a personalized RM setting, where items with ...
Investigations in applied probability and high-dimensional statistics
(Massachusetts Institute of Technology, 2020)
This thesis makes contributions to the areas of applied probability and high-dimensional statistics. We introduce the Attracting Random Walks model, which is a Markov chain model on a graph. In the Attracting Random Walks ...
Interpretable machine learning methods with applications to health care
(Massachusetts Institute of Technology, 2020)
With data becoming increasingly available in recent years, black-box algorithms like boosting methods or neural networks play more important roles in the real world. However, interpretability is a severe need for several ...