Cost Uncertainty Research Articles

Cost of Home-Based Couples Human Immunodeficiency Virus Counseling and Testing and Human Immunodeficiency Virus Self-Testing During Pregnancy and Postpartum in Southwestern Kenya

ObjectiveHome-based couples HIV testing and counseling and HIV self-testing (HIVST) for pregnant women can promote HIV status disclosure and male partner testing; however, cost data are lacking. We examined a home-based couples intervention (HBCI) and HIVST intervention costs per couple (unit cost) during pregnancy and postpartum in Kenya. MethodsThis randomized controlled trial is comparing HBCI and HIVST for couples among pregnant women attending antenatal care clinics in two counties in southwestern Kenya. We used micro-costing to estimate the unit cost per couple receiving the intervention as the total of direct and indirect costs for each study arm in 2019 US$. We used a one-month window to conduct a time and motion study to determine personnel effort and resources. We then compared the unit cost by arm, identified key cost drivers, and conducted sensitivity analyses for cost uncertainties. ResultsAt base-case, the unit cost was $129.01 and $41.99, respectively, for HBCI and HIVST. Personnel comprised half of the unit cost for both arms. Staff spent more time on activities related to participant engagement in HBCI (accounting for 6.4% of the unit cost) than in HIVST (2.3%). Staff training was another key cost driver in HBCI (20.1% of the unit cost compared to 12.5% in HIVST). Sensitivity analyses revealed that the unit cost ranges were $104.64-$154.54 for HBCI and $30.49-$56.59 for HIVST. ConclusionsOur findings may guide spending decisions for future HIV prevention and treatment programs for pregnant couples in resource-limited settings such as Kenya.

Optimal decision-making of mutual fund temporary borrowing problem via approximate dynamic programming

Temporary borrowing is a liquidity risk management tool for mutual fund managers to meet investor redemption demands. We develop a new Markov decision process model to describe the temporary borrowing process, considering the multiple lending channels, the validity period of loans, and the uncertainties of cost, demand, and maximum loan amount simultaneously. An approximate dynamic programming (ADP) algorithm is initiated to solve the cost-minimizing temporary borrowing problem. We construct the value function as a separable approximation and prove the convexity of its components with respect to the available funds in different channels. Moreover, a new value function updating formula, DMAX, is designed to overcome value function overestimation. The proved convexity and the proposed formula contribute to fast and reliable value function estimation. Numerical experiments based on actual business data show that the proposed algorithm can obtain near-optimal decisions in deterministic cases and maintain high robustness in stochastic cases.

Brentuximab vedotin in combination with doxorubicin, vinblastine and dacarbazine for first-line treatment of stage IV HL: cost impact on subsequent lines in Italy

Introduction: This study estimates the change in the costs of second-line or later (2L+) treatments compared to the current scenario, associated with the introduction of brentuximab vedotin (Adcetris®) (BV) in combination with doxorubicin, vinblastine and dacarbazine (A+AVD) for the treatment of previously untreated (1L) patients with stage IV classical Hodgkin’s lymphoma (cHL). Methods: An economic model has been developed that estimates the variation in treatment costs of 2L+ associated with the introduction of BV in 1L from the point of view of the Italian National Health System over a time horizon of 3 years. The population eligible to receive a treatment of 2L+ has been estimated from the literature, considering an increasing consumption in the three years of A+AVD in 1L. Two main scenarios and several alternative scenarios were considered to address the uncertainty that characterizes the distribution of market shares of 2L+ treatments. Results: In the baseline scenario, over three years, the introduction of BV in 1L is associated with a cumulative reduction in treatment costs of 2L+ of € 1.74 M. In all scenarios, a reduction in treatment costs of 2L+ is confirmed, with a total saving that varies between € 5.6 M and € 1.3 M compared to the main scenarios. Conclusions: The present analysis shows that the introduction of A+AVD in 1L for the treatment of stage IV CD30+ cHL patients is associated with a reduction in treatment costs of 2L+, even if there are some limitations related to the uncertainty of real cost and population estimates.

Single machine robust scheduling with budgeted uncertainty

We consider robust single machine scheduling problems. First, we prove that with uncertain processing times, minimizing the number of tardy jobs is NP-hard. Second, we show that the weighted variant of the problem has the same complexity as the nominal counterpart whenever only the weights are uncertain. Last, we provide approximation algorithms for the problems minimizing the weighted sum of completion times. Noticeably, our algorithms extend to more general robust combinatorial optimization problems with cost uncertainty, such as max-cut.

The Cost of Alternative Water Supply and Efficiency Options under Uncertainty: An Application of Modern Portfolio Theory and Chebyshev’s Inequality

Sea-level rise, population growth, and changing land-use patterns will further constrain Florida’s already scarce groundwater and surface water supplies in the coming decades. Significant investments in water supply and water demand management are needed to ensure sufficient water availability for human and natural systems. Section 403.928 (1) (b) of the Florida Statutes requires estimating the expenditures needed to meet the future water demand and avoid the adverse effects of competition for water supplies to 2040. This study considers the 2020–2040 planning period and projects (1) future water demand and supplies; and (2) the total expenditures (capital costs) necessary to meet the future water demand in Florida, USA. The uniqueness of this study compared with the previous studies is the introduction of a probabilistic-based approach to quantify the uncertainty of the investment costs to meet future water demand. We compile data from the U.S. Geological Survey, Florida’s Department of Agriculture & Consumer Services, Florida’s Water Management Districts, and the Florida Department of Environmental Protection to project the future water demand and supplies, and the expenditures needed to meet the demand considering uncertainty in the costs of alternative water supply options. The results show that the total annual water demand is projected to increase by 1405 million cubic meters (+15.9%) by 2040, driven primarily by urbanization. Using the median capital costs of alternative water supply projects, cumulative expenditures for the additional water supplies are estimated between USD 1.11–1.87 billion. However, when uncertainty in the project costs is accounted for, the projected expenditure range shifts to USD 1.65 and USD 3.21 billion. In addition, we illustrate how using Modern Portfolio Theory (MPT) can increase the efficacy of investment planning to develop alternative water supply options. The results indicate that using MPT in selecting the share of each project type in developing water supply options can reduce the standard deviation of capital costs per one unit of capacity by 74% compared to the equal share allocation. This study highlights the need for developing more flexible funding strategies on local, regional, and state levels to finance additional water supply infrastructure, and more cost-effective combinations of demand management strategies and alternative water supply options to meet the water needed for the state in the future.

A novel efficient probabilistic prediction approach for train-induced ground vibrations based on transfer learning

To deal with the issues of high computational cost and prediction uncertainty of numerical models in train-induced ground-borne vibration prediction, a prediction method based on transfer learning is proposed in this study. In this method, the vehicle–track-coupled analytical model and three-dimensional finite element model are first used to calculate the train-induced ground vibration under various condition variables, and these data were used as training samples to pre-train the deep neural network models. Numerous train-induced ground vibration experiments were then conducted along the metro lines in Beijing, and those measured vibration data were used to fine-tune the pre-trained deep neural network model with the transfer learning strategy. A random variable obeying a Gaussian distribution is assumed over the predicted vibration acceleration levels to model the randomness of train-induced vibration, and the parameters of this distribution were determined by the statistical results of vibration monitoring data in the metro tunnels. The fully trained model could complete the prediction of train-induced ground vibration in seconds. Finally, a case study was carried out, by comparing the probabilistic prediction results with the statistical results of the field measurements, and the feasibility and the improvement of the proposed method were demonstrated.

Dynamic Programming Approach in Aggregate Production Planning Model under Uncertainty

In order to achieve a competitive edge in the market, one of the most essential components of effective operations management is aggregate production planning, abbreviated as APP. The sources of uncertainty discussed in the APP model include uncertainty in demand, uncertainty of production costs, and uncertainty of storage costs. The problem of APP usually involves many imprecise, conflicting and incommensurable objective functions. The application of APP in real conditions is often inaccurate, because some information is incomplete or cannot be obtained. The aim of this study is to develop APP model under uncertainty with a dynamic programming (DP) approach to meet consumer demand and minimize total costs during the planning period. The APP model includes several parameters including market demand, production costs, inventory costs, production levels and production capacity. After describing the problem, the optimal APP model is formulated using artificial neural network (ANN) techniques in the demand forecasting process and fuzzy logic (FL) in the DP framework. The ANN technique is used to forecast the input demand for APP and minimize the total cost during the planning period using the FL technique in the DP framework to accommodate uncertainties. The model input is historical data obtained through interviews. A case study was conducted on the the need for aluminum plates for the automotive industry. The results show that the ANN technique proposed for demand projection has a low error value in forecasting demand and FL in the DP framework is able to find minimal production costs in the APP model.

Shipping Cost Uncertainty, Endogenous Regime Switching and the Global Drivers of Inflation

Shipping Cost Uncertainty, Endogenous Regime Switching and the Global Drivers of Inflation

COVID-19 prevention materials in Jiangsu Province, China Safeguarding Program Research

Originated in 2019, the COVID-19 at the end of the year had a huge impact on China and the world. The Chinese government has used the whole country to comprehensively prevent the outbreak of the new crown virus. In order to effectively prevent the COVID-19, in addition to the epidemic prevention and treatment measures of the frontline medical staff, it is also necessary to fully guarantee the back-end epidemic prevention materials, and the behavior of epidemic prevention material protection has a typical transaction cost paradigm. Starting from the three factors of asset specificity, uncertainty and transaction frequency of transaction costs, combined with the actual work experience of the COVID-19 prevention, the author analyzes the contractualization and institutional arrangements of the COVID-19 prevention materials.

Inventory system with generalized triangular neutrosophic cost pattern incorporating maximum life-time-based deterioration and novel demand through PSO

At the manufacturing plant or while the products are being transferred from one supply layer to another, there is a considerable possibility of receiving damaged or faulty items mixed in with non-defective commodities. This research focuses on the non-defective and defective products that are shipped to retailers by their suppliers. The retailer reworks faulty items to make them non-defective, and the retailer receives a discount on the cost of purchasing defective items. The presented inventory system addresses the uncertainty in inventory costs and also considers the deterioration of items with prioritized maximum product life. In this study, our aim is to minimize the total inventory cost when demand rate as a function of quality and power pattern of time under crisp and generalized triangular neutrosophic environments. Based on the payment deal, interest charges are imposed only when the payment delay has passed a particular allowable time limit. The neutrosophic number, which provides three different types of membership functions representing truth, hesitation, and falseness, is used in the inventory model to handle the cost pattern’s uncertainty. A particle swarm optimization approach is used to analyze the proposed inventory model, and the results are validated using a numerical example and sensitivity analysis for various parameters.

A Scenario-Based Simulation Model for Earthwork Cost Management Using Unmanned Aerial Vehicle Technology

Risks are involved in every aspect of earthwork projects. This paper specifically discusses the cost risk associated with the volume calculation of such projects. In the design phase, it is not possible to accurately predict the quantity per soil type underground of the site. As a result, there are uncertainties in the excavation cost that may cause cost overrun. There is a need for an innovative method to forecast, control, monitor, and manage excavation cost from design phase to completion. There is, however, an innovative method for calculating volume accurately using a digital surface model method. The digital surface model can be acquired using GPS and unmanned aerial vehicles (UAV). This paper proposes a simulation model which is able to analyze, control, and monitor the cost based on excavation volume so stakeholders are able to gain the actual volume quickly and accurately. Monte Carlo simulation is applied to the excavation volume per soil type, resulting in a range of possible outcomes for excavation cost. The developed model was verified by applying it to an actual case project. Throughout the project, the cost was successfully monitored and maintained below the maximum expected cost. However, the final actual cost in the last simulation almost reached the maximum expected cost, indicating the need for cost monitoring. By periodically comparing the simulation result to the actual excavated volume obtained from the UAV, the proposed model can assist stakeholders in controlling the cost overrun risk and developing strategies during the earthwork life cycle.

PROPERTY LIFE CYCLE COST ANALYSIS METODS

The minimization of the property life cycle cost as a criterion for making a decision regarding the economic feasibility of purchasing or building a certain real estate object compared to other objects with functional utility today has become a mandatory requirement of most regulatory and legal acts in Ukraine.
 At the same time, the practical implementation of this requirement faces the problem of methodical provision of life cycle cost analysis from the standpoint of the uncertainty of these costs in the future and the need to take into account the time value of money.
 The application of valuation procedures based on cash flow discounting, proposed in this article, can contribute to the solution of this problem.
 Discounting cash flows will require the classification of life cycle costs not only by content load, but also by the time of their occurrence into initial and future costs, which in turn are divided into once-only, periodic and regular costs.
 Acceptable units of measurement of discounted cash flows can be net present costs, equivalent annual cost, net savings; savings to investments ratio, internal rate of return, discounted payback period, each of which corresponds to its own model and valuation criterion.
 To solve the problem of the uncertainty of future costs and to take into account the risk inherent in their forecasting, the methods of analyzing the sensitivity of the results to changes in the market situation are proposed and the justified feasibility of using stochastic discounted cash flows models is justified.

HTA21 Evolving Landscapes for Advanced Therapy Medicinal Products (ATMPS): Access in the US, UK, & EU

Advanced Therapy Medicinal Products (ATMPs) are distinct from traditional pharmaceutical products and include high cost and benefits uncertainty. This research aims to identify developments in the regulatory and reimbursement landscape to improve access for patients.

Sustainable value methodology to compare the performance of conversion technologies for the production of electricity and heat, energy vectors and biofuels from waste biomass

The Sustainable Value methodology was used to compare and rank eight combinations of waste biomass types and conversion technologies on a common assessment basis to produce energy, energy vectors and advanced biofuels. The studied combinations included agricultural and agro-industrial residues, slurries and effluents, pulp and paper mill sludge, piggery effluents and organic fractions of municipal solid waste, to produce biodiesel by (trans)esterification, biogas by anaerobic digestion, ethanol by fermentation, hydrogen by dark fermentation, electricity and heat by combustion, biogas and synthesis gas by gasification, and bio-oils by pyrolysis or hydrothermal liquefaction. The numerator “Functional Performance” of the Sustainable Value indicator was estimated according to 14 criteria of process technology, material and energy inputs and outputs, and acceptance by the stakeholders. The performance of the technologies was classified based on the values of relative importance (φ) and level of satisfaction ( S ) attributed to each criterion. The gasification of residues from the olive-oil industry reached the highest “Functional Performance”, followed by anaerobic digestion of chestnut processing residues and pig-rearing effluents. The Sustainable Value denominator “Costs” depended mainly on the degree of maturity of the technologies, which penalised pyrolysis, hydrothermal liquefaction and dark fermentation. The final ranking of the Sustainable Value indicator was gasification > combustion > anaerobic digestion > (trans)esterification > pyrolysis and fermentation to ethanol > hydrothermal liquefaction > dark fermentation, respectively for the most adequate waste biomass types under study. Thermochemical conversions were mainly impacted by process and input criteria, while output and social acceptance criteria were more decisive for the biochemical conversions. • Sustainable Value (SV) analysis applies to waste biomass-to-energy (WBtoE) conversion • SV indicator enables the ranking of bio- and thermochemical WBtoE technologies • Costs uncertainty of low-maturity WBtoE technologies negatively impacts the SV score • Gasification of olive-oil industrial residues delivers the highest SV score of 4.09 • Biogas production by codigestion can further increase the SV score to 6.76

Portfolio ownership, production shifts, and downside risks: Evidence from Korea

This study intends to advance the multinational operational flexibility literature while examining how current ownership status affects production shifts and downside risks among international investments of multinational corporations. It also explores the moderating effects of environmental boundary conditions influencing the relationships between production shifts and downside risks. An empirical examination of a large sample of Korean overseas manufacturing subsidiaries shows that a low ownership level in an MNC’s investment portfolio decreases production shifts. We also find that the reduced production shifts due to small portfolio ownership lead to downside risk increase. The nested relationship between decreased production shifts and increased downside risks is more salient under significantly-reduced labor cost uncertainty to a low level or lowly-correlated labor costs across countries. These findings imply that portfolio ownership can be understood as a corporate strategic intention to enhance cross-country transferrability and coordinatibility for cross-border production shifts and downside risk reduction.

Incorporation of Price, Reserve, and Cost Uncertainty Measurement in Real Option Valuation

Incorporation of Price, Reserve, and Cost Uncertainty Measurement in Real Option Valuation

Brayton technology for Concentrated Solar Power plants: Comparative analysis of central tower plants and parabolic dish farms

Concentrated solar power plants intend to be key in the pool of renewable energy production technologies in the next future because of their versatility and high efficiency. In this work a comparative study between two promising technologies is developed. A central tower receiver surrounded by a heliostat field and a farm of parabolic dishes, both coupled to a hybrid Brayton cycle, are considered. Two power scales are surveyed (between 5 and 20 MW) at three different locations with quite different latitudes (between Sahara desert and medium European latitudes) and meteorological conditions. A modelling scheme developed by our group that allows to obtain the expected thermodynamic and thermo-economic plant records is applied. Key indicators like efficiency, net generated energy, levelized cost of electricity, and specific plant investment are calculated and analysed. Variability of natural gas prices and also land cost uncertainty are reflected on levelized cost of electricity range. Among the plants compared, minimum values are found within the interval [135–163]USD/MWh for central towers at the highest power considered and southern latitudes. In addition, the area needed for the installation of the plants and the influence of CO2 taxes is also analysed. Displayed cashflows show larger investment costs for central tower than for dish farms. Finally, those systems are put into the context of other concentrated solar power, other renewable, like wind or photovoltaic, and other conventional power plants.

Eradication of Solar Power Unsustainability through Cost-Benefit Analysis: KwaZulu Natal Case Study

As a developing country, South Africa relies on electricity as the most critical amenity needed for development. KwaZulu-Natal (KZN) is one of the nine provinces in South Africa that faces an energy supply challenge. KZN is also the warmest province among them all due to its location. As one of the warmest provinces, solar power could be utilised to assist in dealing with the energy crisis. This paper focuses on the cost uncertainties attributed to the implementation of solar power which will assist in meeting the demand of energy in the region. The unsustainability of energy has led to a near collapse of the region’s economy. This study also contributes to improving the scientific implementation of solar power in the region to deal with the unsustainability criteria observed. The employment of Cost Benefit Analysis (CBA) revealed solar power as one of the most reliable renewable energies for planned implementation bases. With the development of the Economical, Methodological and Environmental (EME) strategic framework which addresses all social cohesion of solar power, the early turn around has been seen through the use of cost-benefit analysis (CBA) in the region of KZN.

A model-based cost-effectiveness analysis of fracture liaison services in China

SummaryThis study is a model-based cost-effectiveness analysis of fracture liaison services (FLS) in China, suggesting that FLS could potentially lead to lifetime cost-saving in patients who have experienced a fracture. However, Chinese-specific real-world data is needed to confirm the results of our study.PurposeThe study aimed to assess the potential cost-effectiveness of fracture liaison services (FLS) from the Chinese healthcare perspective with a lifetime horizon.MethodsA previously validated Markov microsimulation model was adapted to estimate the cost-effectiveness of FLS compared to no-FLS. The evaluation was conducted in patients aged 65 years with a recent fracture. Treatment pathways were differentiated by gender, FLS attendance, osteoporosis diagnosis, treatment initiation, and adherence. Given the uncertainty in FLS cost, the cost in the base-case analysis was assumed at US$200. Analyses were also performed to determine the maximum cost for making the FLS cost-saving and cost-effective at the Chinese willingness-to-pay (WTP) threshold. One-way sensitivity analyses were conducted.ResultsWhen compared with no-FLS, the FLS was dominant (lower costs, higher quality-adjusted life years) in our target population at the FLS cost of US$200 per patient. For every 100 patients who were admitted to the FLS, approximately four hip fractures, nine clinical vertebral fractures, and three wrist fractures would be avoided over their lifetimes. Our findings were robust to numerous one-way sensitivity analyses; however, the FLS was not cost-effective in patients aged 80 years and older.ConclusionFLS could potentially lead to lifetime cost-saving in patients who have experienced a fracture. Our study informs the potential cost-effectiveness of FLS and the knowledge gap in China; more future research incorporating Chinese-specific real-world data are needed to confirm the results of our study and to better evaluate the cost-effectiveness of FLS in China.

Moderating Effect of Firm Size on Financial Uncertainty Indicators and Financial Performance of Manufacturing Firms in Kenya

Purpose: To what extend does firm size moderates’ relationship between manufacturing firms’ financial performance and financial uncertainty indicators. This was necessitated below expectation performance of manufacturing firms in Kenya. They are expected to employ 30% of Kenya population, contribute to Gross domestic product up to 10% and attract at least 10% of large investor. This has not been possible in the last 12 years.
 Design/Methodology/Approach: The study adapted descriptive design. Study was done in Kenya for manufacturing firms registered under Kenya Association of Manufacturers (KAM) for period between 2009 to 2020. Total population was 856 with a sample size of 90 firms chosen using stratified and them random sampling. Indicators were Return on Equity, trade credit uncertainty, Liquidity uncertainty, Leverage uncertainty, operational cost uncertainty and Logarithm of total asset’
 Findings: Firm size moderates relationship between financial uncertainty indicators and financial performance of manufacturing firms in Kenya. Firm size negatively and significantly affected performance. Though moderation was not significant, but it strengthened relationship between variables.
 Implications/Originality/Value: Firm size should be monitored since it significantly affected financial performance of manufacturing firms in Kenya. Firm assets should not be financed by leverage since it had significant negative relationship with financial performance.