Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Response-surface-model-based system sizing for nearly/net zero energy buildings under uncertainty

Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty

Chance Constrained Optimization for Targeted Internet Advertising

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation

Supplier selection under disaster uncertainty with joint procurement

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringJessica L. Heier StammHealth care organizations must have enough supplies and equipment on hand to adequately respond to events such as terrorist attacks, infectious disease outbreaks, and natural disasters. This is achieved through a robust supply chain system. Nationwide, states are assessing their current supply chains to identify gaps that may present issues during disaster preparedness and response. During an assessment of the Kansas health care supply chain, a number of vulnerabilities were identified, one of which being supplier consolidation. Through mergers and acquisitions, the number of suppliers within the health care field has been decreasing over the years. This can pose problems during disaster response when there is a surge in demand and multiple organizations are relying on the same suppliers to provide equipment and supplies. This thesis explores the potential for joint procurement agreements to encourage supplier diversity by splitting purchasing among multiple suppliers. In joint procurement, two or more customers combine their purchases into one large order so that they can receive quantity discounts from a supplier. This research makes three important contributions to supplier selection under disaster uncertainty. The first of these is the development of a scenario-based supplier selection model under uncertainty with joint procurement. This optimization model can be used to observe customer purchasing decisions in various scenarios while considering the probability of disaster occurrence. Second, the model is applied to a set of experiments to analyze the results when supplier diversity is increased and when joint procurement is introduced. This leads to the third and final contribution: a set of recommendations for health care organization decision makers regarding ways to increase supplier diversity and decrease the risk of disruption associated with disaster occurrence

Equilibrium bidding in the Eurosystem's open market operations

Open market operations play a key role in allocating central bank funds to the banking system and thereby to steer short-term interest rates in line with the stance of monetary policy. This note presents some elements of a theory of bidding in central bank tenders in a framework such as the one of the Eurosystem. The ECB has so far used fixed rate tenders and a variant of the variable rate tender, which may be similar to a fixed rate tender depending on market circumstances. In doing so, it faced consecutively an 'under-' and an 'overbidding' issue. The tools developed in this note to understand the bidding behavior of banks in these operations allow revisiting these phenomena and the more general question of the optimal tender procedure and allotment policy. JEL Classification: D84, E43, E52central bank liquidity management, Open market operations, tender procedures

A displacement-based design method for medium rise reinforced concrete walls [Un método de diseño basado en desplazamientos para muros de hormigon reforzado de mediana altura]

In displacementbased design methods some demand considerations, starting from the first mode of vibration, are usually made. However, some authors have called the attention on the importance of taking the higher modes into account, due to their influence in the distribution and demand of both, moments and shears along the vertical elements, with significant effects. In this work, the method presented allows to consider, in a practical way, the effect of the higher modes on the seismic response of a structure. The proposal to achieve it, is a simplified model of Three Degree of Fredom developed from a mass concentration of four points equally distant along the building height. This method corresponds to an iterative process, in which the analysis and design procedures are carried out simultaneously, thus, avoiding considerations or suppositions on resistance and ductility values. This method has been applied to the structural walls of a 15storey building. The results obtained show the efficiency of the method in terms of the proposed objectives achievement and the fast converging of the iterative process involved. The effect of the higher modes is extremely noticeable in the distribution of shear stresses and the use of an initial pre-dimensioning involving the reinforcement, allows consistency between the analysis and the structural design