Optimization Under Uncertainty Using the Generalized Inverse Distribution Function

A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define the objectives of the optimization process, the inverse cumulative distribution function allows for the use of all the possible information available in the probabilistic domain. Furthermore, the use of a quantile based approach leads naturally to a multi-objective methodology which allows an a-posteriori selection of the candidate design based on risk/opportunity criteria defined by the designer. Finally, the error on the estimation of the objectives due to the resolution of the GIDF will be proven to be quantifiableComment: 20 pages, 25 figure

Customer Concerns about Uncertainty and Willingness to Pay in Leasing Solar Power Systems

Although solar power systems are considered as one of the most promising renewable energy sources, some uncertain factors as well as the high cost could be barriers which create customer resistance. Leasing instead of purchase, as one type of product service system, could be an option to reduce consumer concern on such issues. This study focuses on consumer concerns about uncertainty and willingness to pay for leasing solar power systems. Conjoint analysis method is used to find part worth utilities and estimate gaps of willingness to pay between attribute levels, including various leasing time lengths. The results show the part worth utilities an d relative importance of four major attributes, including leasing time. Among concerns about uncertainties, government subsidy, electricity price, reliability, and rise of new generation solar power systems were found to be significantly related to the additional willingness-to-pay for a shorter leasing time. Cluster analysis is used to identify two groups standing for high and low concerns about uncertainty. People with more concerns tend to pay more for a shorter lease time

Measuring output gap uncertainty

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap. In our application, we show that data revisions alter substantially our probabilistic assessments of the output gap using a variety of output gap measures derived from univariate detrending filters. The resulting ensemble produces well-calibrated forecast densities for US inflation in real time, in contrast to those from simple univariate autoregressions which ignore the contribution of the output gap. Combining evidence from both linear trends and more flexible univariate detrending filters induces strong multi-modality in the predictive densities for the unobserved output gap. The peaks associated with these two detrending methodologies indicate output gaps of opposite sign for some observations, reflecting the pervasive nature of model uncertainty in our US data

Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters

Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates from empirical observations of a system; their values are not known precisely. Different types of MDPs with uncertain, imprecise or bounded transition rates or probabilities and rewards exist in the literature. Commonly, analysis of models with uncertainties amounts to searching for the most robust policy which means that the goal is to generate a policy with the greatest lower bound on performance (or, symmetrically, the lowest upper bound on costs). However, hedging against an unlikely worst case may lead to losses in other situations. In general, one is interested in policies that behave well in all situations which results in a multi-objective view on decision making. In this paper, we consider policies for the expected discounted reward measure of MDPs with uncertain parameters. In particular, the approach is defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best and average case performances of a policy are analyzed simultaneously, which yields a multi-scenario multi-objective optimization problem. The paper presents and evaluates approaches to compute the pure Pareto optimal policies in the value vector space.Comment: 9 pages, 5 figures, preprint for VALUETOOLS 201

Photovoltaics as a terrestrial energy source. Volume 2: System value

Assumptions and techniques employed by the electric utility industry and other electricity planners to make estimates of the future value of photovoltaic (PV) systems interconnected with U.S. electric utilities were examined. Existing estimates of PV value and their interpretation and limitations are discussed. PV value is defined as the marginal private savings accruing to potential PV owners. For utility-owned PV systems, these values are shown to be the after-tax savings in conventional fuel and capacity displaced by the PV output. For non-utility-owned (distributed) systems, the utility's savings in fuel and capacity must first be translated through the electric rate structure (prices) to the potential PV system owner. Base-case estimates of the average value of PV systems to U.S. utilities are presented. The relationship of these results to the PV Program price goals and current energy policy is discussed; the usefulness of PV output quantity goals is also reviewed

Evaluation of the applicability of investment appraisal techniques for assessing the business value of IS services.

There is a consensus among academics and practitioners that ICT investments should be carefully justified, measured and controlled. This is not different for the development of a service architecture or the development of particular services as such. In practice, the traditional capital investment appraisal techniques (CIAT’s) such as payback period or net present value are by far the most used techniques for assessing the feasibility of ICT investments. Nevertheless, serious doubts about the fitness of these techniques in a service based value net environment arise. Value nets have special characteristics such as high flexibility and agility, re-use of services,… that makes the use of these techniques very difficult and the reliability of the outcome most uncertain. Efforts are made to find more appropriate techniques. In the past, CIAT’s have been adjusted so that these techniques become more reliable in an ICT environment and new justification methods and techniques have been developed. However neither these adjusted techniques nor the new techniques are frequently used. This might be explained by the fact that the outcome of these techniques is difficult to interpret and to use and the fact that some significant problems (like the estimation of hidden costs) remain unsolved. Moreover, most of the new techniques are still in the conceptual phase. In this paper we evaluate these adjusted and new techniques in the light of service oriented architectures. We will argue that non of the techniques offers a good solution for assessing the business value of IS services. Despite the existence of a wealth of literature, the IS community appears to be no nearer to a solution to many problems associated with ICT appraisal. This is potentially problematic when dealing with investments in emerging technology such as IS services or service architectures. Since all techniques presented in the article have their drawbacks, it is safe to say that reliance on a sole technique may lead to sub-optimalisation or even failure. Therefore it makes sense to use a mixture of techniques, eliminating or diminishing the weaknesses of each of the techniques used. We strongly recommend a multi-layer evaluation process, or an evaluation process derived from the balanced scorecard, for the appraisal of investments in services or service architectures.

On two-echelon inventory systems with Poisson demand and lost sales

We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u