Miguel F. Anjos   (Polytechnique Montreal, Canada)Talk title: Smart Grids and Optimization: A Winning Combination
Abstract: A smart grid is the combination of a traditional electrical power system with information and energy both flowing back and forth between suppliers and consumers. This new paradigm introduces major challenges such as the integration of intermittent generation and storage, and the need for electricity consumers to play an active role in the operations of the system. We will illustrate the importance of optimization in meeting these challenges, and the opportunities provided by smart grid to the optimization community.
Michael C. Ferris   (University of Wisconsin, USA)Talk title: Cows, Fish, Fields of Fuel and Optimization
Abstract: We describe several applications of optimization modeling to address environmental constraints. At the core of these models are complex interacting physical, biological, social and economic systems. We show how spatial visualizations of the underlying decision spaces can expose critical features of the problem to the domain experts in ways that facilitate greater understanding of optimization tradeoffs. We will detail specific optimization models underlying three applications, including a nutrient managagement system (Anmods), a fish barrier removal project (Fishwerks), and a bio-energy game (Fields of fuel).
Michal Kocvara   (University of Birmingham, UK)Talk title: On Iterative Methods in Large Scale Optimization
Abstract: I will discuss the advantages and disadvantages of iterative methods used for the solution of linear system in the framework of convex optimization. I will first present a case study demonstrating that a properly chosen and tuned iterative method can lead to a very efficient solution of a complex large-scale engineering problem. Then I will discuss the case of general problems and the use of iterative methods within the general solver PENNON.
Daniel Ralph   (University of Cambridge, UK)Talk title: Keeping Options Open When Just One Can Be Exercised: When To Select the Winner From an R&D Portfolio?
Abstract: We examine at what point in time parallel investments, for example in an R&D portfolio, should be discontinued in favour of investment in the winner. Simultaneous investment in multiple options increases the probability of success of at least one project, but is also costly. At each point in time, the decision maker must decide whether one option is so far ahead of the others that all remaining time and money should be devoted exclusively to it. Furthermore, the decision maker can selectively drop underperforming options over time. We assume that the performance of each project is governed by a general Ito process, while opportunities to drop underperforming options (or select the winner) are generated by an independent Poisson process. We provide a new, constructive method, whereby the option value can be found as (the limit of) an increasing sequence of lower bounds. The multidimensional theory developed here underlies many complex real-world stopping decisions that are often solved sub-optimally.
Joint work with Dr Rutger-Jan Lange, Erasmus University Rotterdam.