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Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

Cost classification and models creation is very important for controlling in every company.

The estimation of the software cost remains one of the most challenging problems in software engineering; as a preliminary estimate of cost includes many elements of uncertainty. Reliable and early estimates are difficult to obtain because of the lack of the detailed information about the future system at an early stage. However, the early estimates are really important when bidding for a contract or determining whether a project is feasible in terms of cost-benefit analysis. Estimators often rely on their past experiences for the prediction of effort for software projects. The fundamental factors that are contributing towards inaccuracy of the cost estimation process are imprecise and drifting requirements, information not readily available on past projects, and the methods that were developed and trained on specific data. In this thesis, we have developed a software cost estimation tool that helps commercial software-development organizations to effectively and quantitatively measure and analyze the software metrics based upon the functional requirements, operational constraints and organization’s capability to handle a project. This cost estimation tool is a fusion implementation or an essence of certain software measurement and estimation techniques that help a software organization to evaluate and analyze fundamental software metrics such as complexity, time, effort, and cost all of which are essential to improving turnaround time and attaining organizational maturity. The new cost estimation method is proposed for the iterative software development projects. The use case technique is implemented per iteration for the specification of the software requirements. COCOMO II and Function Point were used to compute the effort required for successive iterations. We also computed the magnitude of relative error for successive iterations. We tested the proposed method on student projects in order to illustrate its usefulness.

Drilling and blasting are seen as sub-systems of size reducing operations in mining. To have better design parameters for economical excavation of mineral production and fragmentation, the comminution and fragmentation operations need to be studied and optimized independently, as well as together, to create optimized use of energy and cost-effective operation. When there is a change in drillhole diameter or fragmentation specification, changes in the blast design parameters are required affecting the cost of a drilling and blasting operation. A model was developed to calculate blast design parameters and costs on the basis of the required 80% fragment size needed for crusher operation. The model is based on previously developed fragmentation models, found in the literature. The model examines the effect of drilling diameter on blasting requirements to achieve certain fragmentation targets and calculates blast design parameters and costs for a range of diameters from 75 to 350 mm. To examine the effectiveness of this model, two different 80% passing sizes of fragments have been considered. It was shown that cost optimization occurs at an intermediate diameter, since there are opposing trends of the effect of diameter on powder factor and accessories needed. To achieve a certain fragmentation target, the total cost of drilling and blasting shows a clear trend allowing an optimum selection of diameter. The selected diameter also allows the examination of the suitability of the drill machine under the given geological and operational conditions of the drilling site. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-03-27 07:34:33.787

Optimal strategies under worst-case scenarios have been studied in Bernard et al. [2013a]. Bernard et al. utilize copulas to construct cost-efficient strategies with a predefined dependence structure in the tail between the payoff and the market. In their study they show that such strategies with state-dependent copula constraints dominate traditional diversification strategies in terms of the provided protection in the states of market downturns. We derive similar strategies, however using correlation constraints instead of copula constraints in the tail. We found that for an investor seeking negative dependence with the market, it is cheaper to construct a strategy with conditional correlation constraint in the tail. However, the constructed strategies with conditional correlation constraints do not provide sufficient protection in bad states of the economy. Therefore, when analyzing a strategy, negative correlation with the market in the tail is not a sufficient indicator for the protection level in the event of a market crisis.

This thesis employed methods used in health care decision making to evaluate strategies for prevention and treatment of febrile neutropenia (FN) in non-Hodgkin lymphoma (NHL) patients. The objectives of this thesis were to quantify the cost-effectiveness of filgrastim and pegfilgrastim as primary prophylaxis against FN in NHL patients, to develop an algorithm for converting health-related quality of life data collected in non-Hodgkin lymphoma patients into preference-based health utility values, and to evaluate NHL patients’ preferences for outpatient treatment of FN. The cost-effectiveness analysis demonstrated that neither filgrastim, nor pegfilgrastim are cost-effective, with respective incremental cost-effectiveness ratios [95% confidence interval] of $4,599,000/QALY [$597,045, dominated] and $6,272,000/QALY [$730,692, dominated], well above the normally accepted threshold of $50,000/QALY. The algorithm for deriving health utility values was based on a regression model that used health utility values obtained from the EQ-5D instrument as the outcome variable and the four subscales of the Functional Assessment of Cancer Therapy – General (FACT-G) questionnaire as the predictor variables. The model final model included three of the FACT-G subscales, and had an R-squared value of 0.502 and a mean squared error of 0.013. A discrete choice experiment was used to examine patients’ preferences for out patient treatment of FN, and demonstrated that out-of-pocket costs, unpaid caregiver time required daily, and probability of return to hospital are all significant attributes when considering outpatient therapy for FN. Adjusted odds ratios [95% confidence intervals] of accepting outpatient treatment for FN were 0.84 [0.75 to 0.95] for each $10 increase in out-of-pocket cost; 0.82 [0.68 to 0.99] for each 1 hour increase in daily unpaid caregiver time; and 0.53 [0.50 to 0.57] for each 5% increase in probability of return to hospital. These results provide important information for clinicians and health care decision makers involved in implementing programs for NHL patients with FN.

This thesis employed methods used in health care decision making to evaluate strategies for prevention and treatment of febrile neutropenia (FN) in non-Hodgkin lymphoma (NHL) patients. The objectives of this thesis were to quantify the cost-effectiveness of filgrastim and pegfilgrastim as primary prophylaxis against FN in NHL patients, to develop an algorithm for converting health-related quality of life data collected in non-Hodgkin lymphoma patients into preference-based health utility values, and to evaluate NHL patients’ preferences for outpatient treatment of FN. The cost-effectiveness analysis demonstrated that neither filgrastim, nor pegfilgrastim are cost-effective, with respective incremental cost-effectiveness ratios [95% confidence interval] of $4,599,000/QALY [$597,045, dominated] and $6,272,000/QALY [$730,692, dominated], well above the normally accepted threshold of $50,000/QALY. The algorithm for deriving health utility values was based on a regression model that used health utility values obtained from the EQ-5D instrument as the outcome variable and the four subscales of the Functional Assessment of Cancer Therapy – General (FACT-G) questionnaire as the predictor variables. The model final model included three of the FACT-G subscales, and had an R-squared value of 0.502 and a mean squared error of 0.013. A discrete choice experiment was used to examine patients’ preferences for out patient treatment of FN, and demonstrated that out-of-pocket costs, unpaid caregiver time required daily, and probability of return to hospital are all significant attributes when considering outpatient therapy for FN. Adjusted odds ratios [95% confidence intervals] of accepting outpatient treatment for FN were 0.84 [0.75 to 0.95] for each $10 increase in out-of-pocket cost; 0.82 [0.68 to 0.99] for each 1 hour increase in daily unpaid caregiver time; and 0.53 [0.50 to 0.57] for each 5% increase in probability of return to hospital. These results provide important information for clinicians and health care decision makers involved in implementing programs for NHL patients with FN.

Software cost estimation nowadays plays a more and more important role in practical projects since modern software projects become more and more complex as well as diverse. To help estimate software development cost accurately, this research does a systematic analysis of the similarity distances in analogy-based software cost estimation and based on this, a new non-orthogonal space distance (NoSD) is proposed as a measure of the similarities between real software projects. Different from currently adopted measures like the Euclidean distance and so on, this non-orthogonal space distance not only considers the different features to have different importance for cost estimation, but also assumes project features to have a non-orthogonal dependent relationship which is considered independent to each other in Euclidean distance. Based on such assumptions, NoSD method describes the non-orthogonal angles between feature axes using feature redundancy and it represents the feature weights using feature relevance, where both redundancy and relevance are defined in terms of mutual information. It can better reveal the real dependency relationships between real life software projects based on this non-orthogonal space distance. Also experiments show that it brings a greatest of 13.1% decrease of MMRE and a 12.5% increase of PRED(0.25) on ISBSG R8 dataset, and 7.5% and 20.5% respectively on the Desharnais dataset. Furthermore, to make it better fit the complex data distribution of real life software projects data, this research leverages the particle swarm optimization algorithm for an optimization of the proposed non-orthogonal space distance and proposes a PSO optimized non-orthogonal space distance (PsoNoSD). It brings further improvement in the estimation accuracy. As shown in experiments, compared with the normally used Euclidean distance, PsoNoSD improves the estimation accuracy by 38.73% and 11.59% in terms of MMRE and PRED(0.25) on ISBSG R8 dataset. On the Desharnais dataset, the improvements are 23.38% and 24.94% respectively. In summary, the new methods proposed in this research, which are based on theoretical study as well as systematic experiments, have solved some problems of currently used techniques and they show a great ability of notably improving the software cost estimation accuracy.

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