Angelos Georghiou

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Linear stochastic programming provides a flexible toolbox for analyzing reallife decision situations, but it can become computationally cumbersome when recourse decisions are involved. The latter are usually modeled as decision rules, i.e., functions of the uncertain problem data. It has recently been argued that stochastic programs can quite generally be(More)
Stochastic programming provides a versatile framework for decision-making under uncertainty, but the resulting optimization problems can be computationally demanding. It has recently been shown that primal and dual linear decision rule approximations can yield tractable upper and lower bounds on the optimal value of a stochastic program. Unfortunately,(More)
In recent years, decision rules have been established as the preferred solution method for addressing computationally demanding, multistage adaptive optimization problems. Despite their success, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling adaptive binary decisions. To address(More)
Decision rules provide a flexible toolbox for solving the computationally demanding, multistage adaptive optimization problems. There is a plethora of real-valued decision rules that are highly scalable and achieve good quality solutions. On the other hand, existing binary decision rule structures tend to produce good quality solutions at the expense of(More)
Robust control design for constrained uncertain systems is a well-studied topic. Given a known uncertainty set, the objective is to find a control policy that minimizes a given cost and satisfies the system’s constraints for all possible uncertainty realizations. In this paper, we extend the classical robust control setup by treating the uncertainty sets as(More)
Decision-making under uncertainty has a long and distinguished history in operations research. However, most of the existing solution techniques suffer from the curse of dimensionality, which restricts their application to small and medium-sized problems, or they rely on simplifying modelling assumptions (e.g. absence of recourse actions). Recently, a new(More)
Building energy management is an active field of research since the potential in energy savings can be substantial. Nevertheless, the opportunities for large savings within individual buildings can be limited by the flexibility of the installed climate control devices and the individual construction characteristics. The energy hub concept allows one to(More)
Abstract— Approximate Dynamic Programming offers benefits for large-scale systems compared to other synthesis and control methodologies. A common technique to approximate the Dynamic Program, is through the solution of the corresponding Linear Program. The major drawback of this approach is that the online performance is very sensitive to the choice of(More)
The cooperative energy management of aggregated buildings has recently received a great deal of interest due to substantial potential energy savings. These gains are mainly obtained in two ways: (i) Exploiting the load shifting capabilities of the cooperative buildings; (ii) Utilizing the expensive but energy efficient equipment that is commonly shared by(More)