ORIE/SCAN Seminar, 2013-10-30 - Martijn Mes (Twente): Tactical Planning in Healthcare Processes Using Approximate Dynamic Programming with Bayesian Exploration - Edited
From E. Cornelius on April 26th, 2018
Wednesday, October 30, 2013 at 1:25pm Upson Hall, 111 Special ORIE/SCAN Seminar: Martijn Mes (Twente) - Tactical Planning in Healthcare Processes Using Approximate Dynamic Programming with Bayesian Exploration Abstract: Tactical planning of resources in hospitals concerns elective patient admission planning and the intermediate term allocation of resource capacities. Its main objectives are to achieve equitable access for patients, to serve the strategically agreed number of patients, and to use resources efficiently. We propose a method to develop a tactical resource allocation and patient admission plan that takes stochastic elements into consideration, thereby providing robust plans. We use Approximate Dynamic Programming (ADP) combined with a Bayesian belief structure to express our uncertainty about the value functions. This methodology enables us to systematically explore the state and action space quickly. Joint work with Ilya O. Ryzhov, Gerald A. van den Berg, and Warren B. Powell. Bio: Martijn Mes is an assistant professor within the department Industrial Engineering and Business Information Systems at the University of Twente (Enschede). He holds a master’s degree in Applied Mathematics (2002) and did his PhD at the School of Management and Governance, University of Twente (2008). The PhD dissertation (and a summary of it) can be found at the bottom of this page. Martijn’s main activities are (i) giving lectures for master and bachelor students, (ii) assisting graduate students, (iii) doing research and project work. Martijn provides the following courses: Simulation, Warehousing, Management of Technology, Supply Chain and Transportation Management, Stochastic Models for Operations Management, Project Process Control and Production Management, and Project Production and Logistics Management. His research involves multi-agent systems (MAS), pricing and auctions in freight transport, behavioral issues in freight transport, dynamic vehicle routing problems (VRP & DVRP), AGV routing, ranking and selection problems (R&S), optimal learning, approximate dynamic programming (ADP), simulation optimization, discrete-event simulation, and simulation of logistic systems.