Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...
This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its reve ...
Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...
This thesis develops models for three problems of liquidity under asymmetric information.
In the chapter "Disclosures, Rollover Risk, and Debt Runs" I build a model of dynamic debt
runs without perfect information in order to understand the impact of asset ...
We present a general method for imputing missing information in the Worldwide Patent Statistical Database (PATSTAT) and make the resulting datasets publicly available. The PATSTAT database is the de facto standard for academic research using patent data. C ...
Research on Web credibility assessment can significantly benefit from new models that are better suited for evaluation and study of adversary strategies. Currently employed models lack several important aspects, such as the explicit modeling of Web content ...
From the moment we wake up in the morning to the day's ebb when we settle in to sleep, we are bound to the task of decision-making. Some of these decisions barely register in our consciousness, if at all, while others, less shy, take a more prominent place ...
Policymaking is a complex process that has been studied using policy process theories almost exclusively. These theories have been built using a large number of qualitative cases. Such methods are useful for theory building but remain limited for theory ex ...
In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical ...
This paper proposes a scheme for generating optimal process plans for multi jobs in a networked based manufacturing system. Networked manufacturing offers several advantages in the current competitive atmosphere such as reducing short manufacturing cycle t ...
