Probability Distribution Of Cost Research Articles

Design-to-Cost Applied Within an Integrated System Model

In an environment of declining financial budgets for space projects, new approaches - such as Design-To-Cost - are being implemented to improve today's satellite design processes. Using an example of a current mission (the power subsystem of the Solar Probe spacecraft) under study at NASA's Jet Propulsion Laboratory, the main part of the paper discusses an Integrated System Model (structured into a performance model, a cost model, and an effectiveness model) that is part of a model-based design process used to perform cost-effectiveness trades. A simulation tool is used during the first step to size the components of the power subsystem, and then simulate its performance during operation. The determined dimensions are transferred into an EXCELTM-spreadsheet and linked to the components' costs. With a cost accounting tool that combines cost estimating relationships with the Work Breakdown Structure of the power subsystem, the life-cycle cost of each alternative design concept is computed. To determine the cost-risk of the different design alternatives for each component, cost probability distributions are introduced. By performing Monte-Carlo simulations, cost sensitivities are revealed. In the next step of the trade study process, the effectiveness of the alternatives is analyzed. Having determined cost and effectiveness, estimates can be made for where the different alternatives lie in the design space. The last part of the paper identifies the main drivers for the spacecraft's performance and cost. Finally it is shown how the mission design benefited from the Integrated System Model and from the application of Design-To-Cost.

Framework to support decisions on chemical pest control under uncertainty, applied to aphids and brown rust in winter wheat

An approach is presented for supporting decision making on chemical pest control in an uncertain environment. Timing of chemical control is considered as a decision problem, which is analysed in terms of objectives, strategies, selection criteria, and a model of the system dynamics. Return on expenditure and insurance are considered to be two generally applicable objectives in pest control. These objectives are made operational by defining the profitability of strategy A compared with strategy B as the probability that A results in lower costs than B, and the risk associated with a strategy as the 0.90 quantile of the probability distribution of costs associated with that strategy, respectively. The authors propose to express the performance of relevant strategies in terms of profitability and risk. Risk-neutral damage thresholds where expected costs of no chemical control at any time just exceed those of immediate application of a pesticide, constitute a yardstick for assessing a strategy's performance. The approach is illustrated using a deterministic model of the pathosystem aphid-brown rust-winter wheat. Uncertainty is modelled as random inputs. No control and immediate pesticide application represent the only relevant strategies, as postponing chemical control decreases profitability and increases risk. The decline in risk attributable to pesticide application is larger for brown rust than for aphids. At risk-neutral damage thresholds the probability of positive returns on pesticide application amounts to only 30%. The causes of this bias to chemical control in recommendations for risk-neutral decision makers are discussed. Current recommendations for both aphids and brown rust appear to presume that farmers become more risk-averse as crop development advances.

A Method for Simulating Partial Dependence in Obtaining Cost Probability Distributions

A Method for Simulating Partial Dependence in Obtaining Cost Probability Distributions

Optimal Risk-Sharing When Risk Preferences are Uncertain

In many cases, a firm or agency needs a product that only one vendor can supply and for which the final cost is uncertain. An optimal risk-sharing arrangement is sought when the buyer and contractor agree on the probability distribution of cost but the buyer is uncertain of the contractor's risk-preferences. We find that when the buyer and contractor have exponential utilities, the optimal profit arrangement for the higher risk-averse contractor is no longer linear but concave in the costs. The degree of concavity is affected by the probabilistic beliefs on the contractor's risk-preferences. As the more risk-averse contractor becomes more likely, her chosen profit arrangement becomes less concave approaching the ideal, linear arrangement. The less risk-averse contractor is provided a profit arrangement with a certainty equivalent above her reservation price. This is the price the buyer must pay in order to entice a less risk-averse contractor into agreeing to accept a more risky profit arrangement.

Optimization of nuclear reactor strategies under uncertainty

Operations research is applied to analysing uncertainties in the selection of nuclear reactor types. Dynamic programming methods have been extended to account for uncertain decision factors by including in the objective function a variance-related uncertainty factor as well as expected cost values. A technique is also described for obtaining total cost probability distributions. A case study is presented which uses the method to analyze a long-term planning problem for a given electric utility system. This problem is also solved by using fuzzy programming methods.

On the theory of the amplification of ultrasound by semi-metals in electric and magnetic fields: Karazinov, R. F., and Skobov, V. G.Zh. Eksperimental'noi i teor. Fiz., 43, no. 4 (10), p. 1496 (1962)

The goal of this study was to characterize the uncertainty associated with the cost of drilling and completion of geothermal wells. Previous research and publications have produced correlations for the average cost of geothermal wells as a function of well depth. This project develops this concept further by using a probabilistic approach to evaluate the distribution of geothermal well costs for a range of well depths. The well cost uncertainty was characterized by identifying the main cost components of geothermal wells and quantifying the probability distributions of the key variables controlling these costs. These probability distributions were determined based on the detailed cost records of U.S. geothermal wells drilled or designed from 2009 to 2013 as well as cost data from drilling equipment manufacturers and vendors. Probability distributions of the key variables were examined to find statistically significant correlations between them. Lastly, the previously determined probability distributions of individual cost components and the correlations between them were input into WellCost Lite, a predictive geothermal drilling cost model, using the Monte Carlo method. This approach allowed us to generate the overall well cost probability distributions for 8000–15,000 ft. (2400–4600 m) geothermal wells. We have shown that the median geothermal well cost increases exponentially with depth. Deep wells typically have higher cost uncertainty and more positively-skewed cost probability distributions. The correlations presented in this paper can be used to determine the economic feasibility of geothermal energy systems, assess the project risk, and facilitate investment decisions.