Cost Uncertainty Research Articles

ANALYSIS OF UNCERTAINTY IN A CONSTRUCTION PROJECT

This article presents the analysis of uncertainty and effectiveness in cost to construction project. Theoretical part describes various definitions of uncertainty, risk both with there sources as calculation bias, information ambiguity and data inaccuracy in construction projects. Main analysis is connected with the project's appraisal phase and its stages were the detailed information is the crucial factor for correct decision- making procedures under uncertainty and risk. Consequently this article is presenting various types risk what are undistinguishable from uncertainty circumstances in construction. After the description of risk and uncertainty types list of solutions were prepared. Application of the suggested risk and uncertainty extenuating techniques in practice was shown as analysis of precision and bias dependence with available information at different stages of appraisal phase.It is emphasized on uncertainty and risk management problem in the construction's decision- making. Recently methods based on the utility theory, game theory, statistical distribution and probability, were improved and adjusted due to decision-making needs in construction. That is why the model with implemented multiple-criteria approaches is suggested both as a tool for dealing with the uncertainty and risk problems and to meet the needs of project managers in appraisal phase.

Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Trading and Volatility in Dual Market: Theory and Evidence from Real Estate

This article investigates the informational role of trading volume and the impact of transaction costs on trading volume and the return dynamics in public and private markets, using real estate as an application. The model extends Kyle (1985) in a framework with two markets associated with asymmetric transaction costs across markets and over time. It derives equilibrium and predicts that at the market downtime, investors’ incentive to trade in the public market increases while that in the private market declines based on a return-cost trade-off, which is when transaction cost in the private market accelerates. Trading volume induces return volatility. The marginal effect of trading volume on price increases monotonically with uncertainty in asset values and transaction costs. An assessment using a simulation method and data on public and private real estate markets in the United States from 1996 to 2014 validates the model’s propositions. We offer two explanations for the increased real estate investment trust (REIT) return volatility during the financial crisis: increased trading volume in the REIT market and a larger marginal impact of volume on price during market downtime.

Optimization of Real‐Time Control With Green and Gray Infrastructure Design for a Cost‐Effective Mitigation of Combined Sewer Overflows

AbstractAn innovative optimization‐simulation framework is applied to a case study of the Province of Quebec, Canada, to optimize the spatial distribution of green infrastructure (GI), the capacity and location of gray infrastructure, and the parameters specific to real‐time control (RTC) operating rules of a sewer system for reducing combined sewer overflows (CSOs) frequency and volume. GI, gray infrastructure, and RTC are applied either individually or in integration through eight optimization scenarios which are simulated over a nine‐year period of historical rainfall data. Among all scenarios, spatial optimization of GI with RTC leads to maximal CSO volume reduction (98%) and is the most cost‐effective option analyzed (70$/m3 of seasonal average CSO reduction compared to 140$/m3 for the scenario involving gray infrastructure alone). However, it requires a high GI implementation level and the CSO frequency under this scenario is sensitive to varying GI design parameters. The findings suggest that the best alternative for CSO control is the integration of the optimization of green and gray infrastructures with RTC as it still provides high CSO volume reduction (95%) and remains a cost‐effective solution (90$/m3 of CSO reduction), while providing robustness under cost and design uncertainties.

Green manufacturers’ power strategy in the smart grid era

Conventional electricity generation from coal and natural gas is one of the largest greenhouse gas sources and using wind power is an effective way to achieve climate neutrality. Realizing this, green manufacturers such as SC Johnson and Silk decide to use 100% wind power for their manufacture. This enables the manufacturers to receive green certifications and hence, attract more customers. However, wind power can be unstable, so some green manufacturers like New Belgium Brewing purchase power from both the regular power supplier and the wind power supplier, resulting in a discount of market expansion. Modelling this complex system can be challenging that requires the use of meta-heuristic algorithms, so we build a game-theoretic model comprising of a wind power supplier, a regular power supplier and a green manufacturer to examine whether the green manufacturer is more benefited under all-wind power strategy. This helps clarify the strategic perdition about the firms’ equilibrium decisions. We find that the green manufacturer prefers all-wind power strategy when (a) the discount effect is significant and the market potential is greatly expanded; (b) both the discount effect and the market expansion are moderate. We further study the impact of the environmental effect, the improvement of wind power stability and the production cost uncertainty, finding that the main results are qualitatively unchanged.

Timber harvesting on fragile ground and impacts of uncertainties in the operational costs

ABSTRACT Forested wetlands with high water tables are sensitive to disruption from harvesting yet support commercially desired tree species like northern white-cedar. Winter harvest was conducted in Maine, USA, to compare operational costs and productivity of cut-to-length harvesting in cedar (fragile soil) and non-cedar stands (mixedwood, sturdy soil), evaluate uncertainties in harvesting costs and influential factors, and forecast time for post-harvest recovery to pre-harvest volumes. Operational costs were calculated using detailed time and motion studies. Operational costs for the cedar stands were higher than non-cedar. Regression models were developed for harvesters, forwarders, and self-loading trucks; number of logs per cycle was a common factor. Sensitivity analysis showed the dependence of operational costs on labor and fuel costs. Forest Vegetation Simulator projections were used to assess harvest sustainability and suggested the time required to regrow harvested merchantable volume is comparable to cutting cycles recommended for similar treatments in the region. Predicted growth rates exceed those reported regionally on similar sites, suggesting additional study of post-harvest response is warranted. Results highlight site constraints on both operational and stand productivity in lowlands and will be useful for timber harvesting decision-making and forest management planning if combined with assessment of residual stand growth response.

Infrastructure capacity planning for reducing risks of future hydrologic extremes

AbstractFloods and droughts and their associated economic, environmental, and social losses or damages are increasing in severity and frequency. Measures taken to reduce these losses or damages stemming from extreme events typically depend on how effective they are in reducing the consequences of having either too much or too little water and for longer periods of time. To identify trade-offs between the annual estimated loss or damage reduction, i.e., the benefits, however measured, and the average annual cost of various damage reduction measures, one can perform risk–cost analyses. Because of climate change, the likelihoods of future hydrologic extremes are both changing and uncertain. Also uncertain are any estimates of future damages that would occur given any specific extreme event. In addition, one cannot be certain of the future costs or benefits of damage reduction measures. This paper outlines a range of practical approaches for identifying these trade-offs, taking into account the uncertainties associated with future damages resulting from any specific flood or drought event, the changing uncertainties of future flood and drought events, and the uncertainty of future damage mitigation costs.

Impact of shared battery energy storage system on total system costs and power peak reduction in commercial buildings

The power system is experiencing an increasing share of renewable and intermittent energy production and increasing electrification. However, these changes are creating high power peaks, are straining the grid and call for expensive investments in expansions and improvements. This paper examines how the operational strategy of shared battery energy storage systems (s-BESS) can address these issues for commercial buildings with relatively high power peaks. Due to the uncertainty in long-term costs when subject to a measured peak (MP) grid tariff, the scheduling of the battery is optimised with a receding horizon control algorithm. The optimisation model is used on a Norwegian real-life case study to find the best possible configuration with an already existing battery. Although current Norwegian regulations challenge the possibility for shared metering and billing for a s-BESS configuration, the results show that the total system cost was reduced by 19.2% compared to no battery. The community peak was reduced by 17.8% compared to no battery and 6.22-17.5% compared to individual storage, which indicates that s-BESS is of value for the DSO as well.

Electrifying light-duty passenger transport for CO[formula omitted] emissions reduction: A stochastic-robust input–output linear programming model

Electrification of light-duty passenger vehicles has been widely considered as an important strategy for decarbonizing the transport sector. This study proposes a novel input–output linear programming model with stochastic-robust optimization methodology to identify the optimal pathways to reduce direct and embodied emissions from light-duty passenger transport under technology cost and emission intensity uncertainties. The model introduces detailed transport technologies into a well-known economic input–output model so that the carbon dioxide (CO2) emissions embodied in production inputs of vehicles in the entire economic system can be fully accounted for. We apply the model to a case study in China, which shows that tightening cumulative CO2 emissions from light-duty passenger transport by 30% of the base case requires large deployment of electric vehicles, especially plug-in hybrids and battery electric vehicles. The emission reductions mainly come from vehicle operation-related emissions. Nevertheless, the total reduction in operation-related emissions is offset by the increase in capital-related CO2 emissions by 13%–18%, depending on the variability in future emission intensity of electricity. Finally, we compare the data uncertainty handling performance of stochastic-robust model with a stochastic programming model and two deterministic optimization models under optimistic and pessimistic parameter settings. The comparison shows that the optimal technology portfolio generated from the proposed model is not only reliable in achieving the predefined emissions target, but is also able to reduce the total investment and operation costs in the light-duty passenger transport sector under data uncertainty.

Robust optimization on redundancy allocation problems in multi-state and continuous-state series–parallel systems

In this paper, we consider the redundancy allocation problem (RAP) with uncertainties in component parameters for multi-state series–parallel system (MSSPS) and continuous-state series–parallel system (CSSPS). In real-world cases, the component parameters such as costs and reliabilities are often uncertain due to epistemic uncertainty. The existing research works mainly focused on binary-state RAP with data uncertainties. Few studies considered the epistemic uncertainty in MSSPS RAP. To the knowledge of the authors, nearly no research work addressed it in CSSPS. Therefore, in this paper we focus on MSSPS RAP and CSSPS RAP with uncertainties and propose a common model suitable for both of them. Moreover, the epistemic uncertainty of component state is handled by a state distribution distributed in an ambiguity set. The uncertain cost parameters are considered as the interval values. Given the partial information of the probability distribution of uncertain data, we establish a robust model to deal with different types of uncertain parameters. The robust model we proposed has a strong risk-averse capacity against the epistemic uncertainties and can help the ambiguity-averse managers design a system where all parameters are evaluated over the worst-case situation within the ambiguity set. Due to its intractability, we reformulate this proposed model as a mixed-integer linear programming problem via duality theory. The performance of the proposed model is illustrated by numerical experiments on the well-known benchmark problem for MSSPS RAP from three aspects: the robustness of the solutions under different conservative levels; the performance of robust solutions to hedge against the uncertainty of component state; the comparison of stochastic programming model and robust model to hedge against the uncertainties of component cost and state.

Risk-adjusted valuation for real option decisions

We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors’ subjective decision making, in response to the market valuation of an investment opportunity. The investor’s subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor’s and the market’s view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical real-option valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.

Hydrogen storage in saline aquifers: The role of cushion gas for injection and production

Hydrogen stored on a large scale in porous rocks helps alleviate the main drawbacks of intermittent renewable energy generation and will play a significant role as a fuel substitute to limit global warming. This study discusses the injection, storage and production of hydrogen in an open saline aquifer anticline using industry standard reservoir engineering software, and investigates the role of cushion gas, one of the main cost uncertainties of hydrogen storage in porous media.The results show that one well can inject and reproduce enough hydrogen in a saline aquifer anticline to cover 25% of the annual hydrogen energy required to decarbonise the domestic heating of East Anglia (UK). Cushion gas plays an important role and its injection in saline aquifers is dominated by brine displacement and accompanied by high pressures. The required ratio of cushion gas to working gas depends strongly on geological parameters including reservoir depth, the shape of the trap, and reservoir permeability, which are investigated in this study. Generally, deeper reservoirs with high permeability are favoured. The study shows that the volume of cushion gas directly determines the working gas injection and production performance. It is concluded that a thorough investigation into the cushion gas requirement, taking into account cushion gas costs as well as the cost-benefit of cushion gas in place, should be an integral part of a hydrogen storage development plan in saline aquifers.

Now or later? Optimal timing of mangrove rehabilitation under climate change uncertainty

Climate change poses a variety of threats to ecosystems, biodiversity, and human lives. Mangrove forest is one of the key nature-based solutions that address climate change and its impacts while providing socio-economic and ecological services. With the increasing frequency of extreme weather events, various national and local governments are considering mangrove rehabilitation as a natural defense to these threats. This study aims to assess the value of mangrove rehabilitation by focusing on the ecological goods and services it provides as well as the cost of the project at different implementation periods. We apply a real options-based trinomial tree model to evaluate the optimal timing of making an investment decision for the rehabilitation project under uncertainty in climate change damage cost. Validating the model using the Philippines as a case, the results found that the economic benefits from the mangrove forest, including the market value of forestry and fishery products, protection from climate-related damages, carbon sequestration, and ecotourism, exceeded the investment cost for the rehabilitation project. Considering the uncertainty in damage costs from natural disasters, the real option valuation gave a higher total investment value which described a more optimal decision to postpone the implementation of the mangrove rehabilitation. However, the results found conditions in the model to invest immediately when the frequency of natural disasters and their associated damage costs significantly increased. The implications of the results recommend government policies to enable the realization of mangrove rehabilitation as a nature-based solution addressing the Paris Agreement, Sustainable Development Goals, and the Disaster Risk Reduction global agendas of transition to low-carbon and climate-resilient economies.

COVID-19 Vaccine Decision-making Factors in Racial and Ethnic Minority Communities in Los Angeles, California

The COVID-19 pandemic has had disproportionate effects on racial and ethnic minority communities, where preexisting clinical and social conditions amplify health and social disparities. Many of these communities report lower vaccine confidence and lower receipt of the COVID-19 vaccine. Understanding factors that influence the multifaceted decision-making process for vaccine uptake is critical for narrowing COVID-19-related disparities. To examine factors that members of multiethnic communities at high risk for COVID-19 infection and morbidity report as contributing to vaccine decision-making. This qualitative study used community-engaged methods to conduct virtual focus groups from November 16, 2020, to January 28, 2021, with Los Angeles County residents. Potential participants were recruited through email, video, and telephone outreach to community partner networks. Focus groups were stratified by self-identified race and ethnicity as well as age. Transcripts were analyzed using reflexive thematic analysis. Themes were categorized by contextual, individual, and vaccine-specific influences using the World Health Organization's Vaccine Hesitancy Matrix categories. A total of 13 focus groups were conducted with 70 participants (50 [71.4%] female) who self-identified as American Indian (n = 17 [24.3%]), Black/African American (n = 17 [24.3%]), Filipino/Filipina (n = 11 [15.7%]), Latino/Latina (n = 15 [21.4%]), or Pacific Islander (n = 10 [14.3%]). A total of 39 participants (55.7%) were residents from high-poverty zip codes, and 34 (48.6%) were essential workers. The resulting themes included policy implications for equitable vaccine distribution: contextual influences (unclear and unreliable information, concern for inequitable access or differential treatment, references to mistrust from unethical research studies, accessibility and accommodation barriers, eligibility uncertainty, and fears of politicization or pharmaceutical industry influence); social and group influences (inadequate exposure to trusted messengers or information, altruistic motivations, medical mistrust, and desire for autonomy); and vaccination-specific influences (need for vaccine evidence by subpopulation, misconceptions on vaccine development, allocation ambiguity, vaccination safety preferences, the importance of perceiving vaccine equity, burden of vaccine scheduling, cost uncertainty, and desire for practitioner recommendation). In this qualitative study, participants reported a number of factors that affected their vaccine decision-making, including concern for inequitable vaccine access. Participants endorsed policy recommendations and strategies to promote vaccine confidence. These results suggest that support of informed deliberation and attainment of vaccine equity will require multifaceted, multilevel policy approaches that improve COVID-19 vaccine knowledge, enhance trust, and address the complex interplay of sociocultural and structural barriers to vaccination.

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

Economic feasibility analysis of carbon capture technology in steelworks based on system dynamics

In the context of the carbon neutral goal proposed by China, the Iron and Steel Industry (ISI) which is responsible for considerable carbon emissions has to take effective actions to emissions reduction. As Carbon Capture, Utilization and Storage (CCUS) has been recognized as an important technical way to reduce industrial carbon emissions in recent years, its application in the ISI is still greatly limited due to the inherent large cost and high economic uncertainty. Besides, there is also a lack of relevant research on the application of CCUS in the ISI. Therefore, it's necessary to study whether steelworks will participate in the CCUS system by applying carbon capture technology. Focusing on steelworks and employing the System Dynamics method, this paper analyzes technical and economic feasibility of applying carbon capture from the perspective of iron and steel supply chain, and identifies three key variables of the system through sensitivity analysis. The results demonstrate that steelworks can successfully apply carbon capture technology under specific conditions. Three key factors include improving the converting efficiency of reinvestment into capture technology, establishing an appropriate scheme of government subsidies, and fostering environmental awareness of downstream consumers to increase their willingness to pay for low-carbon steel.

Fuzzy-multi-mode Resource-constrained Discrete Time-cost-resource Optimization in Project Scheduling Using ENSCBO

Construction companies are required to employ effective methods of project planning and scheduling in today's competitive environment. Time and cost are critical factors in project success, and they can vary based on the type and amount of resources used for activities, such as labor, tools, and materials. In addition, resource leveling strategies that are used to limit fluctuations in a project's resource consumption also affect project time and cost. The multi-mode resource-constrained discrete-time–cost-resource optimization (MRC-DTCRO) is an optimization tool that is developed for scheduling of a set of activities involving multiple execution modes with the aim of minimizing time, cost, and resource moment. Moreover, uncertainty in cost should be accounted for in project planning because activities are exposed to risks that can cause delays and budget overruns. This paper presents a fuzzy-multi-mode resource-constrained discrete-time–cost-resource optimization (F-MRC-DTCRO) model for the time-cost-resource moment tradeoff in a fuzzy environment while satisfying all the project constraints. In the proposed model, fuzzy numbers are used to characterize the uncertainty of direct cost of activities. Using this model, different risk acceptance levels of the decision maker can be addressed in the optimization process. A newly developed multi-objective optimization algorithm called ENSCBO is used to search non-dominated solutions to the fuzzy multi-objective model. Finally, the developed model is applied to solve a benchmark test problem. The results indicate that incorporating the fuzzy structure of uncertainty in costs to previously developed MRC-DTCRO models facilitates the decision-making process and provides more realistic solutions.

Energy performance contracting in a supply chain with financially asymmetric manufacturers under carbon tax regulation for climate change mitigation

Energy performance contracting is a turnkey service for an enterprise to achieve the goal of energy-saving or emission abatement. This study investigates the value of energy performance contracting in a two-echelon supply chain consisting of one supplier and two financially asymmetric manufacturers under carbon tax regulation for climate change mitigation. The supplier provides a trade credit for a financially weak manufacturer who engages in Cournot competition with a financially strong manufacturer. Furthermore, the supplier can act as an energy service company to offer energy performance contracting for the two manufacturers to achieve carbon tax savings. The impact of sharing ratio of energy performance contracting and coefficient of variable cost are analyzed to explore whether and which manufacturer should be chosen by the supplier to implement energy performance contracting. The results suggest that when the coefficient of variable cost is within a high range, the supplier will not provide energy performance contracting for any manufacturer. However, when the coefficient of variable cost and sharing ratio are not very high, the supplier prefers to provide energy performance contracting for the financially weak manufacturer. With the increasing of sharing ratio, the supplier changes the decision to select the financially strong one. Moreover, the model is extended to consider the impact of demand uncertainty and coefficient of investment cost. Furthermore, the result of numerical studies indicates that there exists a Pareto zone for the three supply chain members to achieve a multi-win situation, if the supplier provides the energy performance contracting for both the manufacturers.

Robust two-stage combinatorial optimization problems under convex second-stage cost uncertainty

In this paper a class of robust two-stage combinatorial optimization problems is discussed. It is assumed that the uncertain second-stage costs are specified in the form of a convex uncertainty set, in particular polyhedral or ellipsoidal ones. It is shown that the robust two-stage versions of basic network optimization and selection problems are NP-hard, even in a very restrictive cases. Some exact and approximation algorithms for the general problem are constructed. Polynomial and approximation algorithms for the robust two-stage versions of basic problems, such as the selection and shortest path problems, are also provided.

Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts

The impact of uncertainty on decision-making is increasingly prioritized, especially for large geotechnical projects such as tunnels. In this article, the effects of geological/geotechnical uncertainties in tunnel construction time and costs were reduced. The forecasting of geological/geotechnical conditions in a tunnel project was based on the Markov chain. To accessing the more accurate input data of the Markov chain and estimate the construction time and cost required for tunnel construction, tunneling experts were interviewed through a questionnaire. Also, to assess the updating effect on obtained outcomes during the tunnel construction, the model was updated for one time. Finally, comparing the predicted results with the actual results, the effects of the interviewing and updating process were investigated. According to the results, the uncertainties in the predictions were found to be reduced significantly. Lastly, this research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues.