Cost Deviation Research Articles

Influence of Technical Reasons on Cost Overruns of Infrastructural Projects: A Sustainable Development Perspective

Cost overruns are a significant issue in infrastructure projects, adversely affecting not only economic performance but also sustainability goals by straining resources and extending project timelines. There is consensus among researchers about the persistence of cost overruns and the importance of accurate estimates. However, there are significant differences in the explanation of the reasons for the overruns. While we recognize the importance of understanding the reasons for cost overruns at all stages of project development, we have focused on the reasons for cost overruns after contract award due to the rarely available real and valid data collected at the source of the information. We analyze the deviations between actual and contracted costs, as well as the dependence of cost deviations of individual project types on the size of the contract. For example, the size of the tunnel significantly affects relative cost deviations, while for standard viaducts or road sections this effect is minimal. The results confirmed that cost overruns are less frequent in standard facilities than in those where the experience factor has little influence on the final cost estimate. For example, deviations in the average value of the contracted costs for highway sections range between −12.1% and 27.9% of the contracted value, and for standardized viaduct projects they range between −16.73% and 6.27% of the contracted value. The type of distribution function of deviations between actual and contractually agreed costs was investigated, and a predictive model for estimating future cost deviations of project programs was presented. The goal is to improve economic sustainability and the related optimization of resources in the development of infrastructure, which contributes to the broader agenda of sustainable development. The results encourage the adoption of improved project documentation and forecasting tools, which are essential for sustainable project management.

Distributed economic operation control in low-voltage resistive hybrid AC/DC microgrid clusters with interlinking converters

Connecting adjacent and diverse microgrids to form hybrid microgrid clusters improve the economic viability and the reliability of each microgrid in islanded operations. To realize the global economic operation among low-voltage resistive microgrid clusters, a two-level control strategy for subgrid control and microgrid clusters control is proposed in this study. In the case of subgrids, where the line impedance of the low-voltage grid is primarily resistive, we design the AC voltage increment droop (Vac-IC) and the DC voltage increment droop (Vdc-IC) to effectively distribute power. The Vac-IC and Vdc-IC droop controls cannot achieve economic power distribution due to the mismatched line impedances. Therefore, a distributed economic operation control based on the consensus algorithm is proposed in this study to eliminate the influence of mismatched line impedances on economic power distribution. In the context of microgrid clusters, the deviation of increment costs serves as an event-driven signal designed for Interlinking Converters (ILCs), aiming to optimize power exchange between adjacent subgrids. The coordination between the subgrid control and microgrid clusters control achieves global economic operation. Finally, Matlab/Simulink simulation verifies that the islanded hybrid microgrid clusters with the proposed strategy realize the economic operation well.

Impact of Diagnosis-Related Groups (DRG) reform on cost homogeneity of treatment for patients with malignant tumours

The cost fluctuations associated with chemotherapy, radiotherapy, and immunotherapy, as primary modalities for treating malignant tumors, are closely related to medical decision-making and impose financial burdens on patients. In response to these challenges, China has implemented the Diagnosis-Related Group (DRG) payment system to standardize costs and control expenditures. This study collected hospitalization data from patients with malignant tumors who received chemotherapy, radiotherapy, and immunotherapy at Hospital H from 2018 to 2022. The dataset was segmented into two groups: the intervention group, treated with traditional Chinese medicine (TCM) alongside standard therapies, and the control group, treated with standard therapies alone. Changes and trends in hospitalization costs under the DRG policy were analyzed using propensity-score matching (PSM), standard deviation (SD), interquartile range (IQR), and concentration index (CI). Findings showed a decreasing trend in the standard deviation of hospitalization costs across all treatment modalities. Radiotherapy exhibited the most significant decrease, with costs reducing by 2547.37 CNY in the control group and 7387.35 CNY in the intervention group. Following the DRG implementation, the concentration indexes for chemotherapy and radiotherapy increased, while those for immunotherapy did not exhibit this pattern. Costs were more concentrated in patients who did not receive TCM treatment. In summary, DRG reform positively impacted the cost homogeneity of inpatient treatments for malignant tumors, particularly in the control group not receiving TCM treatment. The effects of DRG reform varied across different treatment modalities. Although short-term fluctuations in hospitalization costs may occur, initial evidence during the study period shows the positive impact of DRG reform on cost homogeneity.

Analysis of Implementation Delay in Road Construction Project (A Case Study of Road Improvement Project in Jayapura Regency)

Planning and control over time and cost are important parts of overall construction project management. Work achievement of a project can be assessed through its quality, time, cost and safety aspects. So, in completing work project, time and cost incurred are aspects that must be continously measured for its deviation from the original plan. İn this study, time and cost deviations makes the management of road improvement project of Jayapura Regency getting worse. By employing several project performance indicators such as time and cost, it is possible to take preventative steps to make the road improvement project of Jayapura Regency can be proceed as planned. The research method applied in this research were qualitative and quantitative method where the qualitative method used a structured interview to respondents, while the quantity method involved a questionnaire distribution to total 100 respondents (10 project owners, 45 implementer contractors, and 45 consultants). Based on the result of this study, factors causing delays in the completion of road improvement project in Jayapura Regency are: (1) labor factor with value of 3.5400, (2) equipment factor with value of 3.4400, (3) finance factor with value of 3.4200, (4) material factor with factor of 3.3900, (5) scope of work and contract/work documents with value of 3.3900, (6) site characteristic factor with value of 3.3700, (7) changes factor with value of 3.3400, (8) managerial factor with value of 3.3300, (9) situation factor with value of 3.3200, (10) planning and scheduling factor with value of 3.2900, and (11) inspection, control and work evaluation systems with value of 3.2700. As for the ranking of variables causing delays for the completion of road improvement project in Jayapura Regency are: (a) workforce discipline with value of 3.5600, (b) inadequate number of workers or in accordane with the existing work activities with value of 3.5600 and (c) workforce nationalism with value of 3.5300

Review of Cost Control Research in Prefabricated Construction under EPC Mode

Aims: Amidst the accelerating global urbanization and growing demand for sustainable development, prefabricated construction emerges as a pivotal transformational force in the construction industry due to its efficiency and environmental friendliness. This paper comprehensively reviews cost control research in prefabricated construction under the EPC (Engineering, Procurement, Construction) model. Study Design: The study adopts a literature-based approach, incorporating findings from domestic and international scholars, to uncover the critical issues and challenges in cost control within the EPC framework. Additionally, it analyzes the application of technological innovations such as BIM (Building Information Modeling) and DEMATEL (Decision Making Trial and Evaluation Laboratory) in cost control, offering a theoretical foundation and practical guidance for prefabricated construction under EPC. Place and Duration of Study: The research spans various regions globally, examining recent practices and theoretical advancements in cost control for prefabricated construction projects under EPC over the past decade to ensure timeliness and comprehensiveness. Methodology: The methodology encompasses a systematic literature review to construct a theoretical framework, coupled with an analysis of case studies showcasing effective cost control measures. Furthermore, the study explores the utilization of BIM and DEMATEL to enhance cost prediction accuracy, process monitoring, and decision-making support. Results: The findings reveal that the EPC model facilitates more systematic and efficient cost control in prefabricated construction. The application of BIM significantly improves cost forecasting precision and process transparency, enabling timely cost deviation identification and correction. DEMATEL effectively identifies key cost drivers and their interrelationships, informing targeted cost control strategies. Conclusion: This paper underscores the significance of cost control in prefabricated construction under the EPC model and highlights the pivotal role of BIM and DEMATEL in enhancing cost control effectiveness. It concludes by advocating for deeper integration of prefabricated construction with the EPC model to propel the green, intelligent transformation and sustainable development of the construction industry.

A techno-economic analysis of future hydrogen reconversion technologies

The transformation of fossil fuel-based power generation systems towards greenhouse gas-neutral ones based on renewable energy sources is one of the key challenges facing contemporary society. The temporal volatility that accompanies the integration of renewable energy (e.g. solar radiation and wind) must be compensated to ensure that at any given time, a sufficient supply of electrical energy for the demands of different sectors is available. Green hydrogen, which is produced using renewable energy sources via electrolysis, can be used to chemically store electrical energy on a seasonal basis. Reconversion technologies are needed to generate electricity from stored hydrogen during periods of low renewable electricity generation. This study presents a detailed techno-economic assessment of hydrogen gas turbines. These technologies are also superior to fuel cells due to their comparatively low investment costs, especially when it comes to covering the residual loads. As of today, hydrogen gas turbines are only available in laboratory or small-scale settings and have no market penetration or high technology readiness level. The primary focus of this study is to analyze the effects on gas turbine component costs when hydrogen is used instead of natural gas. Based on these findings, an economic analysis addressing the current state of these turbine components is conducted. A literature review on the subsystems is performed, considering statements from leading manufactures and researchers to derive the cost deviations and total cost per installed capacity (€/kWel). The results reveal that a hydrogen gas turbine power plant has an expected cost increase of 8.5% compared to a conventional gas turbine one. This leads to an average cost of 542.5 €/kWel for hydrogen gas turbines. For hydrogen combined cycle power plants, the expected cost increase corresponds to the cost of the gas turbine system, as the steam turbine subsystem remains unaffected by fuel switching. Additionally, power plant retrofit potentials were calculated and the respective costs in the case of an upgrade were estimated. For Germany as a case study for an industrialized country, the potential of a possible retrofit is between 2.7 and 11.4 GW resulting to a total investment between 0.3 and 1.1 billion €.

An analytical approach to evaluate life-cycle cost of deteriorating pipelines

Optimal inspection and maintenance planning for deteriorating pipelines is important for cost-effective risk management decisions. The objective of this study is to develop an analytical approach for determining the expected value and standard deviation of life-cycle cost (LCC) for deteriorating pipelines, which can incorporate imperfect repair and inspection actions without making independent assumptions of failure and repair events. The framework is developed based on a decision tree model by using analytical methods to evaluate events and considers the impact of the inspection schedules and possible repair actions on the probability distributions of failure times. To illustrate the proposed framework, a steel transmission pipeline with a corrosion defect is used, where a stochastic model of corrosion growth is assumed and burst failure is considered as the pipeline failure mode. The case study indicates that the failure cost due to two or more failure occurrences can be ignored; and the inspection interval, repair threshold and type, the length of lifetime considered, and unit failure cost all play important roles in the expected value of the total cost. The variation in the LCC estimation is found to be significant, and it has a great impact on determination of optimal decision variables.

Breaking the Sisyphean loop: reconceptualizing the treatment of risk and uncertainty in transport projects

Worldwide transport projects repeatedly exceed their budgets. This problem has received widespread attention over several decades, with little progress made to improve project cost performance. Consequently, this has led to the emergence of the following recurring question: How can public agencies responsible for delivering transport projects improve the estimation accuracy of their actual construction costs? We use the metaphor of the Sisyphean task to assist us in suggesting that the conflation of risk and uncertainty results in an inability to estimate actual construction costs accurately. Ambiguity aversion, an illusion of certainty, and the repeated occurrence of cost deviations, as demonstrated by our illustrative example and analysis of the elemental cost models of 39 transport projects, collectively create a Sisyphean loop that is impossible to break. Thus, we question the prevailing conceptualisation and treatment of risk and uncertainty used to estimate the actual construction costs of projects. We offer a new theoretical lens framed around ecologically rational heuristics and provide suggestions to reconceptualize and recalibrate the treatment and assessment of uncertainty in cost estimates. We hope this paper provides a much-needed circuit breaker to public agencies’ prevailing treatment of risk and uncertainty and stimulates new lines of inquiry to improve the cost performance of transport projects.

Analyzing Cost Deviation Factors in Iraqi Construction Projects: A Comprehensive Examination of Influencing Variables

Analyzing Cost Deviation Factors in Iraqi Construction Projects: A Comprehensive Examination of Influencing Variables

External quality assurance of cost estimates in major public projects: empirical evidence on cost performance

PurposeValue creation for society from public projects requires that the overall benefits exceed the use of taxpayers' money. At the same time, cost overruns in public projects are a well-documented feature in the literature, but practical guidance on reducing the extent and magnitude of overruns is rare. In 2000, Norway introduced a governance regime that includes mandatory external quality assurance (QA) of cost estimates for major public projects. This paper compares the cost performance of public projects on each side of this QA scheme.Design/methodology/approachWe use an original dataset covering 1,704 projects from 2000 to 2021, reported first-hand from Norwegian public agencies. We apply quantitative methods in the form of descriptive statistics, regression models, and statistical testing of hypotheses to answer our research questions.FindingsThe mean cost overrun across projects in our dataset is smaller than several previous international studies have reported. We find no statistical support for different cost performances between QA and non-QA projects. Secondly, cost overruns seem to vary between different public sectors. A third finding is a small development with lower cost overruns over time for the non-QA projects, and we raise the question of whether the QA scheme has contributed to overall learning effects. The fourth finding is that cost deviations are quite independent of project size.Originality/valueThe paper offers novel insights for decision-makers and researchers on the effects of external quality assurance on cost performance in public projects.

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.

Exploring Cost Variability and Risk Management Optimization in Natural Disaster Prevention Projects

The purpose of this study is to analyze the causes of cost variation in natural disaster prevention projects (NDPPs) in Gyeongsangbuk-do, South Korea, and develop tailored cost and risk management strategies. Utilizing a binary logistic regression model, this research uniquely focuses on the Gyeongsangbuk-do region, gathering data from 244 stakeholders through an online survey. The study identifies critical variables influencing cost deviation, including project management risk (PMR), project costing and execution risk (PCER), project execution strategy risk (PESR), construction project risk (CPR), project cost and schedule risk (PCSR), project management challenges (PMCs), and construction project subcontractor and safety management (CPSSM). Significant findings revealed PMR (OR = 3.744, 95% C.I. [1.657, 8.457]), PCER (OR = 5.068, 95% C.I. [2.236, 11.484]), and PESR (OR = 3.447, 95% C.I. [1.853, 6.413]) as the primary causes of cost deviation, alongside the notable impacts of other factors such as CPSSM. The developed predictive model is instrumental for NDPP stakeholders in Gyeongsangbuk-do, providing advanced risk management capabilities and aiding in effective preventive measures. This study not only corroborates theoretical hypotheses from previous research but also offers new insights into cost deviation causes in NDPPs, thereby enhancing strategic decision-making and advancing risk management perspectives.

Droop parameter-based economic dispatch for islanded microgrid considering demand response

The droop parameter setting of the distributed generator (DG) has an essential role in islanded microgrid (MG) systems. This paper presents droop-based economic load dispatch (ELD) in an islanded MG. In this approach, the Power–frequency (P–f) droop characteristic of DG is integrated into its incremental cost, which is the derivative of the cost function of DG. The optimisation problem is formulated by considering the standard deviations of the incremental costs of DGs. Under steady-state conditions, DGs operate at a single frequency and the P–f droop-based ELD ensures that the incremental costs of DGs are equal, minimising the overall cost of active power generation, following the equal incremental cost principle. Furthermore, an incentive-based demand response (DR) programme is integrated into the system to maximise the utility benefit function by incentivising customers for power curtailment. The results exhibit significant power curtailment during peak load hours, indicating the effectiveness of the DR programme. The proposed combined mathematical model is applied to the modified 33-bus islanded MG and solved using basic particle swarm optimisation. It calculates optimal droop parameters for a day-ahead scenario and emphasises the efficiency benefits of optimised droop settings and the integration of DR in islanded MGs.

Novel Probabilistic Approach for Quantification of Cost-Overruns Risk and Determination of Primary Causes

Cost management is a crucial component of construction project management, which assumes an increasingly critical role in modern large-scale projects. Ideally, cost management is achieved through a comparison between the current cost and the predetermined cost schedule. However, in real-world projects, it is often difficult to consistently obtain accurate costs for each cost item throughout the project life cycle and to predict the probability of various risk occurrences. To address this challenge, we propose a novel building information modeling- (BIM-) based algorithm to quantify the risk of cost overruns and cost deviation; and identify the primary causes without a detailed cost breakdown. The role of BIM in our approach is to automatically extract precise quantitative information that we can use to calculate the probability density function of the current cost and the expected total cost and eventually combine reliability analysis to provide the risk probability. The numerical experiment demonstrates that this algorithm can estimate the probability of cost overruns and cost deviation, and identify the primary cause of deviation. Additionally, the sensitivity requirements for cost management can be adjusted by adjusting the parameters, indicating its potential to meet multiple requirements in the different projects. The proposed algorithm provides a method for detecting the current risk of cost overruns and cost deviation, as well as identifying the primary cause of deviation, which is of potential value in the practical applications.

Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

Under the pressures of environmental pollution, solar energy and hydrogen have attracted widespread attention, thus promoting the development of photovoltaic–hydrogen integrated energy system. However, multiple uncertainties seriously interfere with the system's economic and stable operation. Therefore, this article proposes a temporal covariance‐involved distributionally robust optimization model for system scheduling considering multiple uncertainties from source, load sides, and market. First, the possible range of the uncertainties distribution is described by a novel moment‐based ambiguity set modified with temporal covariances. Second, the objective function is constructed as a min{sup} form to minimize the expected operation cost under the “worst‐case” distribution in the ambiguity set. Then, the constraints present a two‐stage structure. The first stage guarantees the operation feasibility under the forecasting scenario, and the second stage accounts for forecasting errors to formulate the rescheduling space for resisting uncertainties. Finally, the model is transformed into an easy‐to‐solve semidefinite programming. Simulation finds that the model 1) significantly reduces the average and standard deviation of system operation cost by 22.35% and 27.41%, respectively; 2) ensures 100% photovoltaic accommodation; and 3) maintains low conservativeness and complexity simultaneously. Above all, this study provides an effective approach for comprehensive ultilization of clean energies under multiple uncertainties.

Research on the evaluation method of the cost deviation control effect for complex large-scale construction projects based on the matter-element extension theory

Research on the evaluation method of the cost deviation control effect for complex large-scale construction projects based on the matter-element extension theory

Research on the evaluation method of cost deviation control effect for complex large-scale construction projects based on the matter-element extension theory

Research on the evaluation method of cost deviation control effect for complex large-scale construction projects based on the matter-element extension theory

Optimizing Cost Management in Dynamic Business Environments: An In-depth Analysis of Production Cost Budgeting, Considering Specific Elements and External Factors

This research discusses the effectiveness of using budgets as a means of controlling production costs at PT. Sari Tuna Makmur in the 2017-2021 period. By examining the budget and realization of direct labor costs (salary costs), factory overhead costs, as well as differences in budgeted tuna production costs, this research aims to identify factors that influence variations between budget and realization. The research results show that the company is generally successful in controlling production costs. Although there are variations in direct labor costs during the COVID-19 pandemic, PT. Sari Tuna Makmur was able to achieve production costs that were lower than the budget in previous years. Internal factors such as business policies, production capacity, and production facilities, as well as external factors such as competition and consumer demand, play an important role in successful cost control. However, special challenges arise during the pandemic, where the decision not to reduce the workforce may result in direct labor cost deviations. Therefore, this research recommends increasing the accuracy of budget preparation and more detailed attention to factors that influence production costs, especially in unexpected situations. This research contributes to the understanding of production cost control strategies amidst the dynamics of the business environment. The practical implication is the need for companies to continue to optimize cost control strategies, consider changes in external conditions, and increase accuracy in preparing budgets to maintain the company's operational and financial sustainability.

A Trust Model Based on Characteristic Factors and SLAs for Cloud Environments

In view of the lack of research regarding characteristic factors (e.g., service cost, quality, etc.) in trust models for cloud environments, as well as the lack of negotiation and monitoring mechanisms, a trust model based on characteristic factors and service level agreements (SLAs) is proposed in this article. First, on the basis of comprehensive trust and self-recommended trust, we introduce the cost deviation trust and service quality coefficient, to simultaneously consider the influence of common and characteristic factors upon trust evaluations and thereby improve the accuracy of trust evaluations. Second, we establish a negotiation and monitoring mechanism whereby both parties sign an SLA before the trade and require SLA agent monitoring services to improve the accuracy of the service cost and quality evaluation and the efficiency of malicious entity identification. Finally, using the agreement quality, experience quality, and monitoring quality, we more accurately judge the trade result and update the recognition degree (which plays a key role in trust evaluation), to further improve the accuracy of trust evaluation and thereby the trade success rate. The results of experiments conducted upon real datasets show that our model can effectively resist spoofing, coordination, and defamation attacks from malicious entities, and it offers a high trade success rate.

Risk-managed operation of community integrated energy systems in day-ahead and real-time markets based on portfolio theory

Operation strategy is a critical issue for the community integrated energy system (CIES) to improve its economic performance. However, the uncertainties in energy markets may result in severe deviation and fluctuation in the operation cost. It is challenging to balance the cost and risk in the operation optimization of CIES. This paper proposes a risk-managed operation optimization method based on portfolio theory for the CIES in day-ahead and real-time electricity markets. First, both kinds of markets are modeled as risky investment markets, whose returns and risks are described by the mean value and the variance of the electricity price. Second, the power purchase of the CIES from different markets is modeled as risky investment activities, in which the Markowitz portfolio theory is employed to balance the risk and cost. Third, the optimal portfolio can be obtained by solving the operation optimization problem of the CIES considering the risk preference of the owner. The case studies show that the CIES can reduce price volatility by 10.11% with only a 3.46% increase in cost in the day-ahead market. The proposed method provides a balance between different returns and risk fluctuations according to user preferences, which verified the validity and feasibility.