Probability Distribution Of Cost Research Articles

Economic evaluation of digitally enabled aged and neurological rehabilitation care in the Activity and MObility UsiNg Technology (AMOUNT) trial

Objective To investigate the trial-based cost-effectiveness of the addition of a tailored digitally enabled exercise intervention to usual care shown to be clinically effective in improving mobility in the Activity and MObility UsiNg Technology (AMOUNT) rehabilitation trial compared to usual care alone. Design Economic evaluation alongside a pragmatic randomized controlled trial. Participants 300 people receiving inpatient aged and neurological rehabilitation were randomized to the intervention (n = 149) or usual care control group (n = 151). Main measures Incremental cost effectiveness ratios were calculated for the additional costs per additional person demonstrating a meaningful improvement in mobility (3-point in Short Physical Performance Battery) and quality-adjusted life years gained at 6 months (primary analysis). The joint probability distribution of costs and outcomes was examined using bootstrapping. Results The mean cost saving for the intervention group at 6 months was AU$2286 (95% Bootstrapped cost CI: −$11,190 to $6410) per participant; 68% and 67% of bootstraps showed the intervention to be dominant (i.e. more effective and cost saving) for mobility and quality-adjusted life years, respectively. The probability of the intervention being cost-effective considering a willingness to pay threshold of AU$50,000 per additional person with a meaningful improvement in mobility or quality-adjusted life year gained was 93% and 77%, respectively. Conclusions The AMOUNT intervention had a high probability of being cost-effective if decision makers are willing to pay AU$50,000 per meaningful improvement in mobility or per quality-adjusted life year gained, and a moderate probability of being cost-saving and effective considering both outcomes at 6 months post randomization.

Optimal dispatching of an energy system with integrated compressed air energy storage and demand response

The integrated energy system is considered to be an important way to avoid energy supply risks by virtue of advantages in meeting diversified energy demand and improving energy utilization efficiency. Energy storage enables microgrid operators to respond to variability or loss of generation sources. In view of the difficulty of battery to fully improve the energy utilization efficiency and solve the problems of clean energy power large-scale consumption, and the difficulty of single demand response to significantly improve the operational reliability and economy of the integrated energy system, a rolling optimization planning framework and model of an integrated energy system considering compressed air energy storage and sliding time window-based electric and heating integrated response demand is proposed, which can obtain both optimal resource configuration and energy management strategy. The planning problem is to coordinate the output of various dispatching resources on the basis of satisfying the constraints, so as to minimize the total cost. Taking into account the uncertainty of wind power and photovoltaic output and the unplanned outage risk of the unit, a stochastic optimization model is constructed based on the probability density function of the risk variable, and finally the cost probability distribution is obtained. The results verify the universality of the planning model. The comparison of five different energy system configuration schemes and multiple time window length schemes verifies the validity and superiority of the model. The simulation results show that the integrated energy system scheme proposed by this planning model has better economy than the scheme without compressed air energy storage, and the operating cost decreases by 11.9% and the total cost decreases by 4.5%. In addition, the results show that through the introduction of demand response, the operating economy and flexibility of the integrated energy system are improved, and as the sliding window length increases, the total cost gradually decreases.

The financial trade‐off between the production of biochar and biofuel via pyrolysis under uncertainty

AbstractThere is a lack of widespread interest in slow pyrolysis biochar pathways relative to other bioenergy pathways because of the perceived absence of biochar's market value when produced at a commercial scale. Thus, most refereed techno‐economic analyses focus on fast pyrolysis. This study quantifies the carbon price point at which the economic feasibility of the slow‐pyrolysis pathway for biochar production is equal to or greater than a fast‐pyrolysis pathway for biochar and biofuel production using baseline minimum carbon prices (MCP). These factors are then modeled under uncertainty to generate stochastic cash flows for the calculation of a 20‐year net present value and carbon abatement cost probability distributions. This article examines whether a slow‐pyrolysis pathway is ever more financially attractive than a fast‐pyrolysis pathway at realistic carbon prices. The results show fast pyrolysis to fuels and biochar achieving the lowest baseline MCP of $61.38/Mg, while the slow pyrolysis to biochar and methanol at 450 °C scenario yields the highest baseline MCP of $642.40/Mg. A notable result is the baseline MCP for the slow pyrolysis to biochar scenario achieving $123.48/Mg, when compared with the fast pyrolysis to fuels and electricity scenario, resulting in a baseline MCP of $182.03/Mg. The results suggest that carbon prices, when high enough, can incentivize biochar carbon sequestration produced from slow‐pyrolysis pathways rather than carbon abatement with biofuels, and that slow pyrolysis to biochar and methanol scenarios require a higher baseline MCP and are less financially competitive than the other pathways. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

Trusted Transactions in Micro-Grid Based on Blockchain

In order to build a local electricity market (LEM), community members can trade electricity peer-to-peer (P2P) with their neighbors. This paper proposes a Hierarchical Bidding and Transaction Structure based on blockchain (HBTS). First, combined with the multi-agents, each microgrid corrects the estimated cost probability distribution of other microgrids by Bayesian theorem, making its probability closer to the accurate probability. Second, for maximize the benefits of the microgrid, this paper uses the Nash equilibrium in the Cournot model to find the optimal quotation and output of different bidding strategies for the microgrid under different power demand conditions. Then the exchange of electricity translates into an exchange of digital proof of electricity purchases and sales of electricity on the Hyperledger Fabric, ensuring the security of the transaction process and the irreparable modification of ledgers. Finally, we verify the effectiveness of the bidding strategy through experiments, and analyze the transaction process.

Two weeks of additional standing balance circuit classes during inpatient rehabilitation are cost saving and effective: an economic evaluation.

Among people admitted for inpatient rehabilitation, is usual care plus standing balance circuit classes more cost-effective than usual care alone? Cost-effectiveness study embedded within a randomised controlled trial with concealed allocation, assessor blinding and intention-to-treat analysis. 162 rehabilitation inpatients from a metropolitan hospital in Sydney, Australia. The experimental group received a 1-hour standing balance circuit class, delivered three times a week for 2 weeks, in addition to usual therapy. The circuit classes were supervised by one physiotherapist and one physiotherapy assistant for up to eight patients. The control group received usual therapy alone. Costs were estimated from routinely collected hospital use data in the 3 months after randomisation. The functional outcome measure was mobility measured at 3 months using the Short Physical Performance Battery administered by a blinded assessor. An incremental analysis was conducted and the joint probability distribution of costs and outcomes was examined using bootstrapping. The median cost savings for the intervention group was AUD4,741 (95% CI 137 to 9,372) per participant; 94% of bootstraps showed that the intervention was both effective and cost saving. Two weeks of additional standing balance circuit classes delivered in addition to usual therapy resulted in decreased healthcare costs at 3 months in hospital inpatients admitted for rehabilitation. There is a high probability that this intervention is both cost saving and effective. ACTRN12611000412932. [Treacy D, Howard K, Hayes A, Hassett L, Schurr K, Sherrington C (2018) Two weeks of additional standing balance circuit classes during inpatient rehabilitation are cost saving and effective: an economic evaluation. Journal of Physiotherapy 64: 41-47].

A Robust profit measure for binary classification model evaluation

Using profit-based evaluation measures is a necessity in business-oriented contexts, as they aid companies in making cost-optimal decisions. Among the measures that effectively include the true nature of costs and benefits in binary classification, the expected maximum profit (EMP) has been used successfully for churn prediction and credit scoring, and defined in general for binary classification problems. However, despite its competitive results against the most frequently used measures, the EMP relies on a fixed probability distribution of costs and benefits, the range of which in real applications is not entirely known. In this paper, we propose to extend this measure by adding random shocks to these distributions. We call this new measure the R-EMP, following the convention of the analogous EMP measure. Our metric adds a stochastic component to each point of the cost-benefit distributions, assuming that costs and benefits have a fixed probability, but its distribution range is subject to an external shock, which can be different for each cost or benefit. The experimental set-up is focused on a credit scoring application using a dataset of a Chilean financial institution, with the attribute selection for a logistic regression being accomplished using the AUC, EMP, H-measure, and R-EMP as the selection criteria. The results indicate that the R-EMP measure is the most robust metric for achieving the greatest profit for the company under uncertain external conditions.

Robust Adaptive Routing Under Uncertainty

We consider the problem of finding an optimal history-dependent routing strategy on a directed graph weighted by stochastic arc costs when the objective is to minimize the risk of spending more than a prescribed budget. To help mitigate the impact of the lack of information on the arc cost probability distributions, we introduce a robust counterpart where the distributions are only known through confidence intervals on some statistics such as the mean, the mean absolute deviation, and any quantile. Leveraging recent results in distributionally robust optimization, we develop a general-purpose algorithm to compute an approximate optimal strategy. To illustrate the benefits of the robust approach, we run numerical experiments with field data from the Singapore road network. The e-companion is available at https://doi.org/10.1287/opre.2017.1662 .

Uncertainty analysis of geothermal well drilling and completion costs

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 8000–15,000ft. (2400–4600m) 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.

Higher moments and probability distribution of maintenance cost in the delay time model

The delay time model is a practical way to model random occurrences of failures and the effect of inspection and maintenance actions on the reliability of a repairable system. The delay time model involves two random variables describing the time of initiation of defects and time to failure after the defect initiation. This article presents a clear and structured approach to the evaluation of maintenance cost using the theory of stochastic renewal processes. This article derives the mean, variance, skewness and kurtosis of the maintenance cost in a finite time horizon. Furthermore, the probability distribution of cost is accurately estimated using the Hermite polynomial model. Using the cost distribution, the value at risk is estimated and proposed as a measure to optimize the maintenance program.

Forecasting the socio-economic impact of the Large Hadron Collider: A cost–benefit analysis to 2025 and beyond

In this paper we develop a cost–benefit analysis of a major research infrastructure, the Large Hadron Collider (LHC), the highest-energy accelerator in the world, currently operating at CERN. We show that the evaluation of benefits can be made quantitative by estimating their welfare effects on different types of agents. Four classes of direct benefits are identified, according to the main social groups involved: (a) scientists; (b) students and young researchers; (c) firms in the procurement chain and other organizations; and (d) the general public, including onsite and website visitors and other media users. These benefits are respectively related to the knowledge output of scientists; human capital formation; technological spillovers; and direct cultural effects for the general public. Welfare effects for taxpayers can also be estimated by the contingent valuation of the willingness to pay for a pure public good for which there is no specific direct use (i.e., as non-use value). Using a Monte Carlo approach, we estimate the conditional probability distribution of costs and benefits for the LHC from 1993 until its planned decommissioning in 2025, assuming a range of values for some critical stochastic variables. We conservatively estimate that there is around a 90% probability that benefits exceed costs, with an expected net present value of about 2.9 billion euro, not considering the unpredictable applications of scientific discovery.

Cost Risk Assessment for Large Hydropower Project Based on the Probability Distribution

Probability distribution is adopted to evaluate the cost risk of large hydropower project. Risk factors are firstly identified through Work Breakdown Structure(WBS), Cost Breakdown Structure(CBS) and Risk Breakdown Structure(RBS), and then individual risk factor probability distribution is measured by method combined Monte Carlo simulation model and the controlled interval memory (CIM) model. By superimposing individual ones, probability distribution of cost under the influence of a number of risk factors is achieved, which provides effective information for investment decision and cost control.

The probability distribution of maintenance cost of a system affected by the gamma process of degradation: Finite time solution

The stochastic gamma process has been widely used to model uncertain degradation in engineering systems and structures. The optimization of the condition-based maintenance (CBM) policy is typically based on the minimization of the asymptotic cost rate. In the financial planning of a maintenance program, however, a more accurate prediction interval for the cost is needed for prudent decision making. The prediction interval cannot be estimated unless the probability distribution of cost is known. In this context, the asymptotic cost rate has a limited utility.This paper presents the derivation of the probability distribution of maintenance cost, when the system degradation is modelled as a stochastic gamma process. A renewal equation is formulated to derive the characteristic function, then the discrete Fourier transform of the characteristic function leads to the complete probability distribution of cost in a finite time setting. The proposed approach is useful for a precise estimation of prediction limits and optimization of the maintenance cost.

Cost Risk as a Discriminator in Trade Studies

Abstract Prior to formal program initiation, analysts typically undertake trade studies to investigate which of several candidate architectures or designs can best provide a desired capability at minimum cost. The various candidates, however, typically differ significantly in risk and uncertainty as well as in cost, but members of the government or industry trade-study team do not have the time and the candidate solutions usually aren't sufficiently detailed at this stage to allow a thorough risk analysis to be conducted. Yet, those differences in risk and uncertainty, as well as in cost, should be taken into account to the extent possible during the trade-study decision process. Because timeliness and simplicity are key requirements of analyses undertaken in support of trade studies, what usually happens is that a “point” cost estimate, or perhaps a 50%-confidence estimate, is established for each candidate, and the go-ahead decision is made based on that estimate. A nagging question remains: “What if Candidate A, the lower-cost option based on those estimates, faces risk issues that make its 80th-percentile cost higher than that of Candidate B?” In other words, Candidate B would be the lower-cost option if the cost comparison were made at the 80% confidence level. This situation is classic, where the decision maker must choose between a low-cost, high-risk option and a high-cost, low-risk option. This article offers a methodology that allows the program manager to take account of all risk scenarios by making use of all cost percentiles simultaneously, namely the entire cost probability distribution of each candidate not simply the point estimate or the 80% confidence cost. As it turns out, the expression of system cost in terms of a lognormal or simulation-generated probability distribution makes it possible to estimate the probability that each candidate will turn out to be the least costly of all the options, and probabilities of that kind are the basis on which an informed decision can be made.

Complexity Control of H.264/AVC Based on Mode-Conditional Cost Probability Distributions

A computational complexity control algorithm is proposed for an H.264 encoder running on a processor/power constrained platform. This new computational complexity control algorithm is based on a macroblock mode prediction algorithm that employs a Bayesian framework for accurate early skip decision. Complexity control is achieved by relaxing the Bayesian maximum-likelihood (ML) criterion in order to match the mode decision threshold to a target complexity level. A feedback algorithm is used to maintain the performance of the algorithm with respect to achieving an average target complexity level, reducing frame by frame complexity variance and optimizing rate-distortion performance. Experimental results show that this algorithm can effectively control the encoding computational complexity while maintaining a good rate-distortion performance at a range of target complexity levels.

The stochastic [formula omitted]-median problem with unknown cost probability distribution

We want to find the location of p facilities which minimize the expected total cost, when the cost for using a facility is a stochastic variable with unknown probability distribution. Using the method of the asymptotic approximations the expected optimal value of the allocation variables is shown to be a multinomial Logit model.

The economics of harvesting and transporting corn stover for conversion to fuel ethanol: A case study for Minnesota

Abstract Corn-stover feedstock costs were estimated for a proposed biomass-to-ethanol conversion facility in southern Minnesota, USA, accounting for county-specific yields and transportation distances, erosion constraints, machinery specifications, and transportation, storage, and densification costs. Monte Carlo simulation was used to estimate the probability distribution of costs under alternative assumptions on key parameters whose values vary widely in the literature. For a facility producing 0.189 hm 3 y −1 of ethanol, marginal feedstock cost was estimated at $60 Mg −1 ($200 m −3 ethanol) for the more-intensive harvest method and $72 Mg −1 ($210 m −3 ) for the less-intensive method. Costs were greater than $68 Mg −1 ($240 m −3 ) for a facility producing > 0.757 hm 3 y −1 ethanol under the more-intensive method, and greater than $93 Mg −1 ($320 m −3 ) for the less-intensive method. Monte Carlo simulation estimated a mean marginal cost of $57 Mg −1 ($69 Mg −3 under the less-intensive harvest method) for 0.189 hm 3 ethanol output, with a $12 ($10) standard deviation. Costs were found to be at or below $68 Mg −1 90 percent of the time ($78 Mg −1 for the less-intensive method). A $12 Mg −1 standard deviation in stover cost would result in a $40 m −3 swing in ethanol cost.

Bioeconomic modeling for control of weeds in natural environments

When a weed invasion is first discovered a decision has to be made on whether to attempt to eradicate it, contain it or do nothing. Ideally, these decisions should be based on a complete benefit-cost analysis, but this is often not possible. A partial analysis, combining knowledge of the rate of spread, seedbank longevity, costs of control and techniques of economic analysis, can assist in making a good decision. This paper presents a decision model to determine when immediate eradication of a weed should be attempted, or more generally whether weed control should be attempted at all. The technique is based on identifying two ‘switching points’: the invasion size at which it is no longer optimal to attempt eradication but where containment may be an option; and the invasion size at which it becomes optimal to apply no form of control at all. The model is applied to a woody perennial weed in a natural environment. The results show that seedbank longevity is the main constraint on the maximum eradicable area and spread rate is the main constraint on the maximum containment area. Stochastic simulations are undertaken to derive probability distributions of costs which are than used to evaluate the effect of budget constraints on areas that can be eradicated. We find that, in the absence of a budget constraint, it may be desirable to eradicate invasions from areas as large as 8000 ha, but when budget constraints typical of those faced by agencies in Australia are introduced, feasible eradicable areas are less than 1000 ha.

Real Options for Project Schedules (ROPS)

Real Options for Project Schedules (ROPS) has three recursive sampling/optimization shells. An outer Adaptive Simulated Annealing (ASA) optimization shell optimizes parameters of strategic Plans containing multiple Projects containing ordered Tasks. A middle shell samples probability distributions of durations of Tasks. An inner shell samples probability distributions of costs of Tasks. PATHTREE is used to develop options on schedules. Algorithms used for Trading in Risk Dimensions (TRD) are applied to develop a relative risk analysis among projects.

An ROI-Based strategy for implementation of existing and emerging technologies in helicopter manufacturing

An aircraft structure has several part families. These part families are currently manufactured using a baseline technology. There are several other competing technologies, already existing or still in the development stages, that could be applied to the structure under consideration. Each technology has a probability distribution of costs and savings (over baseline) associated with it. The problem is to determine which of the candidate technologies should be used and to what extent, to manufacture different part families. In this paper, the authors have obtained the optimum time-dependent technology mix over a finite planning horizon, such that the return on investment (ROI) is maximized. This has been done subject to the constraints that the risk resulting from investment in a combination of technologies, does not exceed, in any year, some predefined quantity, and that the quantity produced using a particular technology does not exceed its applicability in any year.

Probability distributions of cost, revenue and profit over a warranty cycle

A warranty cycle starts with the sale of a new item and terminates when the warranty servicing obligation of the manufacturer created by this sale ends. Cost and profit measures over a warranty cycle are of interest with regards to accounting, pricing, product quality, warranty policy design, and other issues. For most predictive and decision making purposes, these variables cannot be described adequately by their means alone. In this paper, we obtain the probability distributions of the manufacturer's rebate, cost, revenue and profit during a cycle, under a (combination) freereplacement/pro-rata warranty policy, with the incorporation of the customer repurchase behavior under warranty. We first derive the joint distribution of the numbers of free and pro-rata replacements during a cycle. We then construct and compute the distributions of interest by conditioning on the numbers of such replacements. In so doing, we take into account the warranty policy, in terms of the lengths of the free and pro-rata periods, the cost and rebate profiles, customer repurchase probabilities during a pro-rata period, and the failure time distribution of the product. We provide several numerical examples, using data that are typical of battery warranties.