MIF Summer Series Presents: Research Talks on Optimizing Nonprofit Organizations and Electric Vehicle Fleet Charging Operations
- Nonprofit organizations rely on a combination of volunteers and staff for their operations. Determining the optimal policy for sharing work schedules among volunteers and staff is not trivial. Self-scheduling platforms, like online scheduling, can be used to balance volunteer preferences with organizational needs. Strategically assigning staff to the schedule can help to improve supply/demand balance while keeping volunteers engaged. In this talk, we explore shared-scheduling strategies to help volunteer organizations close the supply-demand gap. We propose organizational and volunteer satisfaction metrics to evaluate the performance of the strategies. We present a numerical analysis to compare the policies under different scenarios of supply, demand, and coverage.
Speaker 1: Mariana Escallon Barrios
Bio: Mariana Escallon-Barrios is a doctoral candidate in Industrial Engineering and Management Science at Northwestern University. She works on modeling and solution approaches for logistic problems in nonprofit settings. Her research interests include scheduling policies for paid and volunteer labor in context with supply and demand imbalances. Prior to her doctorate program, Mariana received her BS and Master in Industrial Engineer from Los Andes University in Bogota, Colombia.
2nd Presentation: Sequential Decision-Making Analytics, with a focus on Managing Electric Vehicle Fleet Charging
- Charging-as-a-Service (CaaS) is an emerging industry within the electric vehicle domain that offers comprehensive charging solutions, from designing charging depots to managing charging process, for fleet operators such as school districts and postal carriers. The CaaS providers' profitability relies significantly on the effectiveness of their charging management, given that they receive a predetermined contracted fee. As CaaS providers need to make charging decisions in a sequential manner to minimize charging costs, the problem at hand is a Sequential Decision-Making problem. In this context, we showcase how the combination of Sequential Decision-Making Analytics techniques and Machine Learning tools enables us to effectively address this challenge. By leveraging these methodologies, we can manage charging services to minimize costs, promote a sustainable power grid, and facilitate a smooth transition to electrification.
Speaker 2: Ehsan Mahyari
Bio: Ehsan Mahyari is a Ph.D. candidate in Operations Management at the Culverhouse College of Business, University of Alabama. His research is centered around Sequential Decision-Making Analytics and its application in Electric Vehicle Fleet (EVF) Charging Management. He utilizes various techniques such as Online Optimization, Markov Decision Process, Approximate Dynamic Programming, and Optimal Stopping to address the challenges encountered by EVF charging service providers. Before embarking on his Ph.D., Ehsan completed his bachelor's degree in Industrial Engineering and obtained a master's degree in Logistics and Supply Chain Management.