Online Stochastic Combinatorial Optimization
In an increasingly dynamic and connected world, organizations often need to make operational decisions under time constraints and uncertainty. Such decisions arise, for instance, when containing failures in power grids, when delivering quality of service in networks, when dispatching vehicles in transportation systems, and when replenishing inventories dynamically. Automating such decisions raises novel challenges and opportunities, moving decision-support systems from deterministic, a priori optimization to online stochastic combinatorial optimization (OSCO). This talk presents a class of anticipatory algorithms for OSCO applications, studies their theoretical properties, and demonstrates their performance on a variety of complex problems. The talk also illustrates the synergies between algorithmic, optimization, and machine-learning techniques for approaching these challenging applications and identify promising research directions.
Pascal Van Hentenryck is professor of computer science at Brown University. He has written 5 books, all published by the MIT Press, and developed a number of influential optimization systems, including CHIP, Numerica, OPL, and Comet, several of which are available commercially. Professor Van Hentenryck has received a 1993 NSF young investigator award, the 2002 INFORMS ICS prize for research excellence at the intersection of computer science and operations research, the 2006 ACP Award for research excellence in Constraint Programming, a 2004 IBM faculty award, and several best paper awards.