TITLE: High-level modeling and solution of combinatorial stochastic programs


Stochastic programming is concerned with decision making under uncertainty, seeking an optimal policy with respect to a set of possible future scenarios. While the value of Stochastic Programming is obvious to many practitioners, in reality, uncertainty in decision making is oftentimes neglected. For deterministic optimisation problems, a coherent chain of modeling and solving exists. Employing standard modeling languages and solvers for stochastic programs is however difficult. First, they have (with exceptions) no native support to formulate Stochastic Programs. Secondly solving stochastic programs with standard solvers (e.g. MIP solvers) is often computationally intractable.


I will talk about my research that aims to make Stochastic Programming more accessible. First, I will discuss modeling deterministic and stochastic programs in the Constraint Programming language MiniZinc - a modeling paradigm that retains the structure of a problem much more strongly than MIP formulations. Second, I will talk about decomposition algorithms I have been working on to solve combinatorial Stochastic Programs. 


BIO: David is a PhD student at Monash University in Melbourne. Before joining Monash, he completed a Bachelors degree in Systems Engineering in Switzerland, studied Robotics at University of Bristol and worked in various engineering roles. As part of his Phd, David is working on a solver system for stochastic combinatorial optimisation problems that are modeled in in the Constraint Programming language MiniZinc. In future, he would like to combine his passion for automation with the research he is doing in operations research.