Cost Uncertainty Research Articles

Cost-effectiveness of a real-time spatiotemporal mapping surveillance system for meticillin-resistant Staphylococcus aureus prevention

An infection surveillance system based on a hospital's digital twin [4D-Disease Outbreak Surveillance System (4D-DOSS)] is being developed in Singapore. It offers near-real-time infection surveillance and mapping capabilities. This early economic modelling study was conducted, using meticillin-resistant Staphylococcus aureus (MRSA) as the pathogen of interest, to assess the potential cost-effectiveness of 4D-DOSS. A Markov model that simulates the likelihood of MRSA colonization and infection was developed to evaluate the cost-effectiveness of adopting 4D-DOSS for MRSA surveillance from the hospital perspective, compared with current practice. The cycle duration was 1 day, and the model horizon was 30 days. Probabilistic sensitivity analysis was conducted, and the probability of cost-effectiveness was reported. Scenario analyses and a value of information analysis were performed. In the base-case scenario, with 10-year implementation/maintenance costs of 4D-DOSS of $0, there was 68.6% chance that 4D-DOSS would be cost-effective. In a more pessimistic but plausible scenario where the effectiveness of 4D-DOSS in reducing MRSA transmission was one-quarter of the base-case scenario with 10-year implementation/maintenance costs of $1 million, there was 47.7% chance that adoption of 4D-DOSS would be cost-effective. The value of information analysis showed that uncertainty in MRSA costs made the greatest contribution to model uncertainty. This early-stage modelling study revealed the circumstances for which 4D-DOSS is likely to be cost-effective at the current willingness-to-pay threshold, and identified the parameters for which further research will be worthwhile to reduce model uncertainty. Inclusion of other drug-resistant organisms will provide a more thorough assessment of the cost-effectiveness of 4D-DOSS.

Two-stage stochastic programming for the design optimization of district cooling networks under demand and cost uncertainty

The major limitations of district cooling systems are the high capital costs, which make design optimization tools necessary to maximize the potential benefits. Decision makers when designing district cooling have to handle cost and demand uncertainties that further increase the investment risks. On the other hand, the possible evolution of cooling demand during the years, shall be taken into account in the first design stages, in order to allow network expansion in the future. In this paper, a novel two-stage stochastic programming model is therefore proposed for the optimal design of district cooling networks under demand and cost uncertainty. The model was also applied to a case study and the results showed that it is more convenient to build smaller district cooling networks (and eventually enhance them in the future if the cooling demand and electricity costs will increase) rather than building larger systems from the beginning. In addition, it was found that the uncertainties in electricity cost and cooling demand are the ones that most influence the optimal solution. The impact of the stochastic model was evaluated with respect to deterministic approaches, resulting up to 5% less expensive in terms of expected cost and with a three years lower payback time. A second model formulation was also implemented, with more rigid constraints, which limit the amount of pipes that can be installed in a single branch. With this formulation, the model tends to connect more buildings and to install larger pipes from the beginning, but the solution in terms of expected cost is only 0.4% more expensive than the more flexible one. Lastly, it was analysed the impact of asset residual value at the end of project life, revealing that neglecting it would lead to connecting more buildings initially, but in most scenarios the network would not be expanded in the future.

Real options analysis for valuation of climate adaptation pathways with application to transit infrastructure.

Climate change and sea-level rise (SLR) are expected to increase the frequency and intensity of coastal flood events, posing risks to coastal communities and infrastructure. While regional climate adaptation investments can provide substantive flood protection, existing plans often neglect uncertainty in future climate conditions and adaptation performance, consequently neglecting the option value of flexibly implementing proposed projects. Addressing this gap, we develop and employ a generalizable real options analysis (ROA) valuation framework that considers how uncertainty in adaptation project costs, SLR, flood severity, and flood losses inform the full range of adaptation performance outcomes. We further propose and apply a novel, computationally efficient flood loss sampling algorithm to estimate the consequences of randomly arriving coastal flood events. We apply this ROA framework to assess the option value of flexibly timing adaptation investments over time, investigating an adaptation pathway proposed by the City of Boston from the perspective of the regional transit system manager. Our results suggest that flexible implementation can provide significant option value in the near- to mid-term (>30 years), with the highest option values under low-probability, high-consequence scenarios. Our results also suggest adaptation pathway performance in the latter half of the 21st century is most sensitive to uncertainty in SLR, flood loss estimates, and flood frequency, underscoring the importance of uncertainty quantification in the long-term valuation of adaptation investments.

Evaluation of the cost-effectiveness of a multicomponent fall prevention program in hospitalized patients.

The aim of this pre- and post-interventional study was to analyze the cost-effectiveness of the multicomponent fall prevention program in hospitalized patients. To achieve this aim, cost-effectiveness analysis performed using decision tree modeling was compared with the implementation of the fall prevention program and usual care. The primary outcome was the number of patient falls. The uncertainty in cost and effectiveness data was evaluated using one-way sensitivity analysis, best-worst-case scenario analysis, and probabilistic sensitivity analysis. According to cost-effectiveness analysis, implementation of the fall prevention program was dominantly cost-effective. As a result of the probabilistic sensitivity analysis, it was revealed that, even if willing-to-pay per-fall prevented value was 0, the probability of being cost-effective was 54.4% for the fall prevention program. Economic evaluation results showed that implementing the multicomponent fall prevention program was dominantly cost-effective in hospitalized patients. Nurses and nurse managers can benefit from economic evaluations in their decision-making processes to implement fall prevention programs.

A bi-objective robust model for minimization of costs and emissions of syngas supply chain

Producing syngas from forest-based biomass could facilitate the transitions in energy and forest sectors by replacing natural gas; reducing emissions and wastes; and generating revenues. Optimizing the economic and environmental impacts of the biomass supply chains can help realizing these benefits. In this study, a bi-objective robust optimization model is developed for tactical supply chain planning of forest biomass gasification at a pulp mill. It optimizes the monthly flow, inventory, and preprocessing of biomass while minimizing annual costs and emissions. Robust optimization with an adjustable risk of constraint violation is used to model the uncertainties in supply and cost of biomass. The average cost and emissions of the robust Pareto-optimal solutions are 68% and 41% higher than those in the deterministic solutions, respectively. Although, the cost and emissions and their trade-off in the deterministic case are more favorable, the robust solutions ensure no biomass shortage while avoiding over-conservatism.

Informing Early Design Decisions Through Functional Analysis of Maintenance Drivers

Operational expenditures dominate the cost of long field-life systems, with maintenance comprising a significant proportion for many systems. However, engineers lack the tools to assess maintenance during conceptual design. Familiar systems mitigate this problem by providing historical maintenance data from which empirical models can be derived. Emerging technologies, like marine renewables, lack operational maintenance data. As a result, engineers must make decisions with no historical data and minimal, if any, operational experience. The high operations cost incurred from basic maintenance tasks, and the loss of energy production during maintenance, further highlight maintenance as a critical cost driver. This paper develops a data-driven model to estimate maintenance intervals of long field-life systems during conceptual design. The model links the elementary functions of a component to maintenance requirements. Relative maintenance considerations were determined by mining function and maintenance data from manuals of long field-life systems. Machine learning was applied to generate a function-maintenance model from the maintenance data. The model consisted of functions grouped into buckets of increasing maintenance demand. The machine learning model was applied to an exemplary long field-life system, a wave energy converter, to explore possible redesigns to reduce maintenance costs. This paper shows that maintenance costs, actions and intervals can be confidently accounted. The function-maintenance model offers two beneficial impacts: it reduces life cycle cost uncertainty, and allows engineers to make informed decisions during conceptual design when redesign costs are at their lowest.

Two-stage multi-objective optimization of reinforced concrete buildings based on non-dominated sorting genetic algorithm (NSGA-III)

The colossal environmental footprint of the construction industry is a paramount ecological concern, especially in the wake of climate change threats and natural resources depletion. The present study performs a novel two-stage optimization approach for the design of reinforced concrete (RC) frame buildings to minimize the water footprint (WF) and cost, based on non-dominated sorting genetic algorithm (NSGA-III). The first stage includes multi-objective optimization (MOO) with respect to the cost and capacity of cross-sections of RC beam and column elements for any possible combination of design variables using section-based database schema (SBDBS). The second stage is MOO based on NSGA-III to minimize the volumes of reinforcing steel and concrete in RC structure considering the optimal sections generated by SBDBS as a pre-determined list for elements. Four structural archetypes consisting of 4, 8, 12, and 16-story frames were considered in the design optimization problems. The main objective of this optimization is to calculate the cost and WF of RC structures for various unit cost and WF of materials, and as a result, identify the optimal design of RC structures. The result of this optimization framework allows the decision maker to identify the optimal RC structural design for any cost or WF condition by performing a sensitivity analysis, without need for additional structural analysis or optimization. The proposed approach further allows the decision maker to consider the uncertainty of WF and cost of materials that emanate from regional or spatiotemporal variables. The results show that the shape of the optimal design Pareto front (PF) is more related to the values of unit WF of materials than the costs. To highlight the advantages of the proposed procedure, probabilistic sensitivity analysis and decision-making strategy were performed and pertinent conclusions were drawn.

Smart heuristics for decision-making in the ‘wild’: Navigating cost uncertainty in the construction of large-scale transport projects

Statistical approaches such as Reference Class Forecasting and Monte Carlo Simulation are widely used to estimate the cost contingency of large-scale transport projects (>$500 million) to mitigate cost overruns during construction. Such approaches may accommodate exposure to risk, but they will fall short in the face of the irreducible uncertainty that confronts project delivery. An underused alternative for formulating a cost contingency is smart heuristics (i.e. simple task-specific decision strategies), which are superior to statistical reasoning under Knightian uncertainty. We set forth an agenda for research on building and using an ‘adaptive toolbox’ of ecologically rational heuristics that decision-makers can apply to produce more accurate contingency estimates for large-scale transport projects. We identify several methodological considerations to support the adaptation and discovery of new heuristics for decision-makers to navigate judgments under uncertainty during the contingency estimation process. The implications for research, policy, and practice are also identified. The contributions of our paper are twofold as we: (1) provide a platform for challenging the effectiveness of the prevailing convention of using statistical reasoning to estimate a project’s cost uncertainty; and (2) identify an avenue for testing existing and discovering new heuristics that can assist decision-making in projects.

A scenario-based framework for the integration of scheduling and control under multiple uncertainties

Increasingly dynamic market conditions require chemical processes to be operated in a more agile manner. Improved process response times can be achieved via tighter cooperation between the decision making-layers. In this study, we present a scenario-based stochastic formulation for the integration of scheduling and control that accommodates multiple uncertainty types, including model, demand and cost uncertainty. We account for the associated PI control system, including input saturation, in the problem formulation, enabling it to predict the closed-loop process dynamics. The resulting closed-loop problem is solved at the dynamic real-time optimization (DRTO) level, in a moving horizon fashion, to compute optimal scheduling decisions, and set-points that are provided to the tracking PI controllers. We test the efficacy of the proposed formulation to handle uncertainties in closed-loop process operation via case studies. We also investigate the effect of the DRTO execution frequency on the process performance. The performance benefits of the proposed formulation over a nominal DRTO implementation are demonstrated.

An Option Game Model of Supplier R&D Co-Competition under Uncertainty

In order to improve the market competitiveness of suppliers and their resilience to emergencies, it is of great significance to discuss the investment decision making of suppliers in developing new products under uncertain and competitive environments. In this paper, with the background of knowledge spillover, absorptive capacity, initial R&D investment, and innovation efficiency asymmetry, the uncertainty of price, sales, and cost are incorporated into the evaluation system as three important risk factors. On the basis of the existing real option investment evaluation model, a real option game model of R&D investment of supplier enterprises based on multiple random variables is established. The sensitivity analysis of parameters is carried out with an example of one enterprise’s monitor R&D project. The results depict that the probability of R&D success has a great impact on the value of enterprise options, which depends on the R&D investment, innovation efficiency, and R&D performance of enterprises. Secondly, the drift rate of price, sales, and cost also has a significant impact on improvement in enterprise option value.

Modelling a bi-level multi-objective post-disaster humanitarian relief logistics network design problem under uncertainty

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research.

Integrating stochastic mine planning model with ARDL commodity price forecasting

Commodity price is of paramount importance to mine planning among other stochastic factors such as mining cost and geological uncertainties. Commodity price uncertainty is critical in the determining block values, and it has been incorporated into mine planning by simulation-based methodologies to deal with the effects of price uncertainty during the mining process. In this article, stochastic optimisation was undertaken using parametric minimum cut network flow algorithm for ultimate pit and pushback design, while stochasticity of commodity prices was incorporated into the planning using Autoregressive Distributed Lag (ARDL) simulation model. The push back design and cashflows in two scenarios, namely when the prices are going up and when the prices are going down, were investigated. Moreover, for each scenario, three types of price inputs were considered: (a) actual prices; (b) ARDL forecasted prices; and (c) stochastic prices generated from ARDL forecasted prices. The results of cashflows indicated that when commodity price moving up scenario, the ARDL forecasted prices underestimate the NPV by about 17% and the stochastic minimum cut underestimates the NPV by 72.27% compared to actual price. Similarly, for the price declining scenario, the ARDL forecasted prices underestimated the NPV by about 3% compared to the actual, while the stochastic minimum cut has an NPV value that is 277% of the actual value. Lastly the value of stochastic programming (VSP) was evaluated. The VSP was about 9.2 million dollars when the prices were going up, while the VSP was about 27.1 million dollars, when price was declining. Moreover, declining commodity price produced negative cashflows which indicate need for risk management with a recourse action to evert losses.

A STUDY ON THE ECONOMIC ANALYSIS FRAMEWORK OF THE ARCTIC ENERGY PLANT CONSTRUCTION PROJECT

The construction projects in the Arctic region are considered highly uncertain due to the extremely cold construction environment. In this study, we presented an economic analysis framework applying rainbow options that can consider multiple uncertainties. The economic feasibility of oil & gas plant construction projects is mainly affected by fluctuation in oil price and construction cost. Thus, we considered oil price and construction cost fluctuation as main uncertainties. The proposed framework consists of five steps and aimed to be validated through a case study conducted in the Canadian context: (1) estimate construction cost uncertainty (estimate the volatility of construction cost with Monte-Carlo Simulation); (2) estimate oil price uncertainty (estimate the volatility of oil price with geometric Brownian motion); (3) Economic data can be categorized into project data and market data, and detailed data is defined and quantified; (4) the economic feasibility of the project is assessed using the quadrinomial lattice method, which is one of the techniques used to calculate real options; and (5) optimize project decision making. The research findings hold significant meaning as we propose a framework for evaluating the economic feasibility of an energy plant construction project using rainbow options. Furthermore, it is anticipated that the utilization of the proposed framework can enhance the profitability and make a significant contribution to investors planning to embark on energy plant construction projects.

How much can electric aircraft contribute to reaching the Flightpath 2050 CO2 emissions goal? A system dynamics approach for european short haul flights

In 2012, the Advisory Council for Aeronautics Research in Europe (ACARE) introduced Flightpath 2050 defining the emission reduction targets for the aviation industry in the EU. The purpose of this study is to assess the impact of narrow body electric aircraft for short haul flights on reaching the Flightpath 2050 CO2 emissions goal using different policy scenarios within the EU. We develop a system dynamics model of the air transport system (ATS), consisting of the interaction between three major segments, namely air travel demand, airline industry and aircraft manufacturers. The model is run for the time period from 1991 to 2050, where electric aircraft are introduced in the year 2035. We simulate the behavior of the ATS under different policy scenarios, e.g., introduction of jet fuel tax, electricity subsidy, seat tax or fleet restriction policy, to test their influence on electric aircraft adoption rates. The results of the analysis suggest that most of the individual policies when implemented are not enough to reach the Flightpath 2050 CO2 emissions goal. Furthermore, a strong set of policy combination was required to reach the goal. Specifically, the combination of an electricity subsidy with kerosene tax demonstrates a higher electric aircraft adoption rate than that of other combinations. On the other hand, sensitivity analysis shows that combinations with electricity subsidy display higher volatility in the CO2 emissions interval. The main reason for this sensitivity is due to the uncertainty in cost pertaining to electric aircraft development parameters.

A flexible and efficient hybrid agricultural subsidy design for promoting food security and safety

The quantity and quality of edible agricultural products are critical for food security (quantity) and safety (quality). Supplying consumers with enough safe food is the key responsibility of food production firms. Still, this aim is not always guaranteed because of input capacity constraints and other limitations in the agricultural sector. A hybrid subsidy, a mix of quantity and quality subsidy, could help achieve food security and safety in a country its flexibility. However, the advantages of the hybrid have not been fully investigated. Thus, this paper designs a hybrid subsidy for edible agricultural products by considering cost uncertainties and input resource constraints. All conclusions are obtained by theoretical mathematical analysis. (1) equilibrium solutions under different conditions—cost uncertainties and input constraints—are obtained, and comparative analyses is offered. (2) the results show that the hybrid subsidy is convenient in the trade-off between food quantity and quality, which means a hybrid subsidy policy design is flexible and efficient for food security and safety. (3) cost uncertainties and input resource constraints have significant impacts on the efficiency of the hybrid subsidy. Findings show that the hybrid subsidy is ideal for supporting edible agricultural products. Additionally, we argue that cost uncertainties and input constraints should be considered when making policy efficiency evaluations. This study has a novel contribution to agricultural support policy design.

Active timber management by outsourcing stumpage price uncertainty with the American put option

Timber production is risky, with price uncertainties caused by stumpage price fluctuations. In this study, the American put option is employed to outsource such uncertainty and establish the reservation prices at various stand ages. Put option values and the reservation prices derived thereof, thus, help forestland owners decide whether the stumpage price is high enough to justify an immediate timber harvest. With a series of simulations, we demonstrated that using an American put option to cover the downside price risk could substantially increase the average land expectation value compared to the certainty case. Besides, the simulations confirm that buying an American option as a price insurance would deduct land expectation values compared to the methods proposed by Brazee and Mendelsohn. In addition, we proposed a rolling put option method that allows forestland owners to buy partial length put options to balance their cost and price uncertainty protection. Overall, with the help of American put option, forestland owners for the first time could actively make their harvest decisions from the price setter perspective instead of being pure market price taker like they have been since the beginning of commercial forestry.

A decision support system for green and economical individual heating resource planning

The heat sector accounts for almost half of the world’s energy consumption, making it a crucial component in meeting decarbonization targets. One of the biggest challenges of heat energy decarbonization arises at the household level, which collectively has a substantial impact on decarbonization. Individual households commonly rely on decentralized heat sources (DHS). Different technologies can be used as a DHS, each of which leads to different overall (Capex and Opex) costs and carbon emissions. Households choose their DHS technologies freely, though typically influenced by the recommendations from their local city planners who use historical data and analyze the respective economical and environmental consequences. Therefore, developing a decision support system (DSS) that guides individuals in their DHS investment choices is a high priority for city planners as it can help evaluate the role of different policies in aligning economical and environmental concerns. This is becoming more important as both the economical and environmental concerns are increasing, respectively, due to recent energy price spikes and the growing urgency of decarbonization. However, developing such a comprehensive DSS is challenging due to the complexity of accounting for all DHS constraints and the uncertainties in demand, prices, and policies. In this study, we present a reliable and comprehensive DSS that provides a range of optimal strategies including the most cost-efficient and the most environmentally friendly ones. These strategies identify the optimal type, installed capacity, and year of investment of DHS technologies, as well as the expected yearly heat generation of each technology. Our DSS accounts for the uncertainties of heat demand, fuel prices, investment, and operation and maintenance costs. We apply our DSS to a typical household in the municipality of Lyngby-Taarbæk under different policy scenarios. We show that between the most environmentally friendly and most cost-efficient solution only a gap of 9%–15% in cost needs to be bridged. We also demonstrate that current energy taxation policies in Denmark do not provide a level playing field between different heat technologies. Under different policy scenarios, we show that heat pumps integrated with PV have the highest potential for minimizing CO2 emissions for a Danish household.

Adaptive RiskAware Bidding with Budget Constraint in Display Advertising

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side plat- form (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy over- looks the randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learn- ing, which is the rst to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. Specif- ically, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a speci c state and market environment via a sequen- tial decision process. Additionally, we theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, includ- ing an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct ex- periments on public datasets and show that the proposed framework achieves better performance in terms of the num- ber of clicks under di erent budget constraints 1.

Cost Variance Analysis in Constellation Architecture Studies

A satellite constellation is group of satellites, which may be identical or varying, but designed together to jointly operate. The architecture of a satellite constellation includes decisions on individual satellite capabilities, payload approaches, placements in orbits, and replenishment strategies. In all architecture studies, cost (including development, production, and launch) is a central concern, including the variance or uncertainty of cost. A common complexity in constellation studies is due to multiple different but nonindependent satellite designs. This article discusses methods for estimating cost variances using probabilistic models at the satellite configuration level extended to constellations of satellites and including correlations due to shared design content. This article uses the NOAA Satellite Observing System Architecture study as a worked example. An important application of estimates of partially correlated costs is assessing the significance of cost estimate differences among alternatives.

Assessing the effects of unit cost uncertainty on flexible road pavement economics under the influence of climate change

Pavement researchers typically adopt life cycle cost analysis (LCCA) to quantify changes in the economic performance of road pavements due to the effects of climate change. As uncertainty exists in the unit cost of materials, fuels, and machinery operation, the assessment of climate change-induced pavement costs invariably involves uncertainty. If such uncertainties remain unaddressed, the assessment of pavement costs will not be accurate. Therefore, this study develops a stochastic pavement LCCA framework to account for the effects of such uncertainties on climate change-induced pavement life cycle cost. This is achieved by integrating a sensitivity analysis methodology and Monte Carlo simulation. To demonstrate the applicability of the framework case studies are performed for standard interstate and standard primary road pavement sections in four climate zones in the United States under a high climate change Representative Concentration Pathway (RCP8.5) for four different periods between 1981 and 2100. The results show that pavement maintenance, end-of-life (EOL), and transportation costs are most affected by climate change. To assess climate change-induced pavement costs more accurately, it is important to improve the accuracy of gasoline, diesel, and hot mix asphalt (HMA) unit costs, as they are the most sensitive input to the pavement LCCA model.