This paper addresses the efficiency issue for simulationbased optimization under uncertainty. In such a case, there are several design alternatives to simulate and each simulation has its own uncertainty to manage or reduce. We present a very efficient sampling approach to manage the overall uncertainty so that the total simulation time can be minimized. We also compare other allocation procedu...

From April 26 to April 30, the Dagstuhl Seminar 09181 Sampling-based Optimization in the Presence of Uncertainty was held in Schloss Dagstuhl Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of working group res...

There are typically uncertain parameters in power system models. This paper presents a computational method for evaluating the effect of parameter uncertainty on the dynamic behaviors of power systems. The effect of parameter uncertainty is represented by system trajectory or performance bounds around ccepted 23 April 2008 vailable online 5 June 2008 eywords: arameter uncertainty uccessive line...

In Part II of this two-part paper, we analyze the marginal prices derived in Part I of this two-part paper within a robust optimization framework. The load and generation are priced at Locational Marginal Price (LMP) while the uncertainty and generation reserve are priced at Uncertainty Marginal Price (UMP). The Financial Transmission Right (FTR) underfunding is demonstrated when there is trans...

In the real world, risk and uncertainty are two natural properties in the implementation of Mega projects. Most projects fail to achieve the pre-determined objectives due to uncertainty. A linear integer programming optimization model was used in this work to solve a problem in order to choose the most appropriate risk responses for the project risks. A mathematical model, in which work structu...

A methodology, based on the concept of Affinely Adjustable Robust Optimization, for optimizing daily operation of pumping stations is proposed, which takes into account the fact that a water distribution system in reality is unavoidably affected by uncertainties. For operation control, the main source of uncertainty is the uncertainty in the demand. Traditional methods for optimizing dynamical ...

The problem of scheduling under bounded uncertainty is addressed. We propose a novel robust optimization methodology, which when applied to mixed-integer linear programming (MILP) problems produces “robust” solutions which are in a sense immune against bounded uncertainty. Both the coefficients in the objective function, the left-hand-side parameters and the right-hand-side parameters of the in...

Autonomic management is aimed at adapting to uncertainty. Hence, it is devised as m-connected k-dominating set problem, resembled by dominator and dominate, such that dominators are resilient up to m-1 uncertainty among them and dominate are resilient up to k-1 uncertainty on their way to dominators. Therefore, an evolutionary algorithm GENESIS is proposed, which resolves uncertainty by evolvin...

In this paper a multi-agent based simulation and optimization framework was put forward to solve the aleatory and epistemic uncertainty in engineering design. Agent structures were designed to extract engineering knowledge from experiments and simulation and to communicate an internal message and domain engineering knowledge base. The framework can deal with complicated time-varying structure a...

We show two universal, Boolean, deterministic logic schemes based on binary noise timefunctions that can be realized without time-averaging units. The first scheme is based on a new bipolar random telegraph wave scheme and the second one makes use of the recent noise-based logic which is conjectured to be the brain’s method of logic operations [Physics Letters A 373 (2009) 2338-2342]. Error pro...