Project Duration Research Articles

Evaluation on Fuzzy Equilibrium Optimization of Construction Project Duration Cost Quality Based on Internet of Things Technology

<div>With the continuous progress of society, people have higher and higher requirements for the quality of life. Energy is an important substance base for human existence and development. In the construction industry, construction project construction period cost control has become particularly important. The existing engineering projects have not significantly optimized the quality of construction period cost, leading to waste of resources. Therefore, it is particularly important to establish an efficient, reasonable, and perfect system to ensure the scientific use of construction project construction period cost. The Internet of things (IoT) technology was applied to engineering projects to study the fuzzy balance optimization of construction period, cost and quality of engineering projects, and analyze the connotation and influencing factors of engineering quality and the construction project target system under the concept of sustainable development, and through the balance analysis of project duration cost, project duration quality, project cost quality, and environmental pollution experiment on different processes of the project, it was found that the application of the IoT technology has reduced the construction period of the project. The IoT technology makes a fuzzy balanced optimization of the construction period cost quality of the project, which can reduce the cost consumption of the project and improve the quality of the project. The application of the IoT technology has reduced the environmental pollution by 2.4%. Based on the IoT technology, the construction period cost quality of the project has been optimized. On the premise of ensuring the project quality, it can reduce the construction period, reduce costs, reduce environmental pollution, and reduce the use of energy to promote sustainable development.</div>

Green artificial intelligence for cost-duration variance prediction (CDVP) for irrigation canals rehabilitation projects

This research introduces Automated Cost-Duration Variance Prediction (CDVP) system to empower project managers to proactively predict project budget, duration, cost variance and duration variance. The research involved the development and comparison of a total of 48 machine learning (ML) algorithms, with 12 algorithms dedicated to each predictive model. To conduct the study, a dataset comprising 1277 historical cases of Field Canals Improvement Projects (FCIPs) was utilized. Factor analysis and stepwise regression analysis were employed for feature selection where five key drivers were identified from the initial 25 parameters. The paper optimized the performance of each algorithm based on Bayesian optimization. The proposed system produces four outputs: project cost, project cost variance, project duration, and duration variance. The developed ML models were able to predict all of the system’s outputs with an adjusted determination coefficient R2 score greater than 0.93 and Mean Absolute Percentage Error (MAPE) less than 8.97. Environmentally, this research recommends to assess CO2 emissions as a performance criterion to select the low environmental impact algorithms. As a result, Light Gradient Boosting Machine (LightGBM) and AdaBoost Regressor were green sustainable algorithms that emitted on average 58 and 69 CO2e grams per computational hours. To enhance interpretability, the study employed the SHapley Additive exPlanations (SHAP) technique to provide explanations for the key drivers.

Perencanaan Ulang Rumah Susun Tenaga Pendidik Universitas Gadjah Mada dengan Konsep Building Information Modeling (BIM) 5D

The innovative development in construction technology has introduced a system called Building Information Modeling (BIM), which can facilitate processes from pre-construction to post-construction. Therefore, this research focuses on the redesign of the University of Gadjah Mada's Faculty Housing Tower using the integrated BIM concept, based on structural analysis with ETABS and modeling with Tekla Structures. The redesign is carried out according to SNI 2847-2019 for concrete building structures, SNI 1727-2020 for loading requirements, and SNI 1726-2019 for seismic resilience standards. This redesign produces a structural analysis to assess the structural feasibility of the building. The 3D design modeling is performed using Tekla software, which can generate 2D designs for Detail Engineering Design, as well as provide the necessary volume of work for cost estimation (RAB calculation). Additionally, the software allows for scheduling and work simulation. The redesign of the University of Gadjah Mada's Faculty Housing Tower results in a budget plan of IDR 24,274,962,257.00, calculated using Quantity Take Off from the Tekla Structures software. The scheduling and work implementation simulation for the structural components are also done using Tekla Structures software. The duration of this redesign project is estimated to be 4 months and 2 weeks, starting from the preparation phase to the completion of the roof structure

Scheduling Analysis with Monte Carlo Simulation and Statistic Method on a Building Construction Project

In the construction of an office building in Indonesia, a delay occurred in the first-floor concrete work. The project was planned to be completed in 161 days but it took 168 days to complete. This study analyzed the issues that arose by doing a scheduling analysis and by comparing the Monte Carlo Simulation and the Statistic method. A comparison between these methods was carried out to determine the project duration scheduling probability level from each method. The data included in the study were the project’s Time Schedule and the interview data from respondents who were directly related to the office building construction project. Monte Carlo Simulation analysis showed that to get a 100% probability level, the duration can only be accelerated by two days, from 168 days to 166 days, while the Statistical method analysis showed that to get a 99.99% probability level the duration can only be accelerated by 4,2 days, from 168 days to 163.8 days. From the results above, it can be concluded that the Monte Carlo Simulation has a more accurate calculation than the Statistic Method because the result from the Monte Carlo Simulation was 166 days with a 100% probability level. It is close to the project realization duration which was 168 days.
 Keywords: construction delay analysis, monte carlo simulation, statistic method comparison

OPTIMIZING PROJECT TIMELINES: THE IMPACT OF E-PROCUREMENT IN ACCELERATING PUBLIC SECTOR PROJECTS IN NIGERIA

Purpose: E-procurement is recognized globally as a tool for enhancing efficiency and transparency, with major intergovernmental bodies advocating its adoption. This study investigates the impact of electronic procurement (e-procurement) on project completion times in the Nigerian public sector. The research focuses on e-tendering, e-invoicing, and e-sourcing, hypothesizing their effects on project completion times. Theoretical Framework: The study employs a conceptual framework highlighting the interplay between e-procurement practices and government policies. Theoretical frameworks, including Innovation Diffusion Theory and Technology Acceptance Theory guided the study. Design/Methodology/Approach: The study adopted a descriptive survey research design to collect primary data from 361 procurement staff from four agencies in Abuja using a structured questionnaire. Data collected were analysed using descriptive statistics and multiple regression analysis. Findings: The results indicate a positive relationship between e-procurement practices and project completion times, with e-invoicing having the most significant impact. However, the introduction of government e-procurement policies as a variable diminishes the individual effects of e-tendering, e-invoicing, and e-sourcing. Research, Practical & Social Implications: The study established a positive relationship between electronic procurement practices and project completion times, with higher implementation levels resulting in shorter project durations. However, organizations should evaluate their impact, balance oversight and efficiency. Originality/Value: By focusing specifically on the Nigerian public sector, testing clearly defined e-procurement elements, collecting robust primary data, and generating new context-specific evidence, this study makes an original, timely and valuable contribution to knowledge on the impact of e-procurement adoption.

Optimal sizing of hybrid renewable energy systems using quasi-optimal control

This paper addresses the critical challenge of sizing hybrid renewable energy systems over their lifespan while accounting for uncertainties in energy sources and load demands. By leveraging stochastic programming, we introduce a novel modeling technique that ensures robust optimization without compromising numerical tractability. Unlike conventional methods, which determine system parameters for the entire project duration upfront, our approach enables annual adjustments to the renewable components’ sizing. This dynamic strategy alleviates early-stage under-utilization, aligning system capacity with evolving demand patterns. Central to our approach is the reformulation of the two-stage stochastic program as a quasi-optimal control. This reduces the number of optimization variables significantly, simplifying the solution process. Moreover, thousands of constraints are replaced with a system of differential equations, enhancing computational efficiency. By minimizing capital costs and dynamic operating expenses, we achieve an optimal system size. Through a real-world application in a rural area of South Africa, we demonstrate the effectiveness of our approach. The results showed a remarkable improvement in problem-solving efficiency, reduced under-utilization, and heightened adaptability to unpredictable weather conditions. This innovative framework represents a substantial leap forward in hybrid renewable energy systems design, offering a more agile and efficient solution for sustainable energy generation.

Finite time preventive maintenance optimization by using a Semi-Markov process with a degraded state. A case study for diesel engines in mining

During the design of maintenance plans for an asset, the frequency calculation of preventive interventions is a maintenance engineer's essential task. This document presents a tool (mathematical formula) to calculate the preventive maintenance interval. The tool considers the maintenance costs and the income generated for the company by its use. Managers can benefit from its simple application to gain efficiency. Typically, business projects are executed over a period. The tool calculates the optimal interval for any given project duration. It also allows the analysis of the size of the preventive interval when the income received changes. This change may be due to asset impairment or changes in the market price of the business product. For the development of the tool, a semi-Markovian model is designed with four states representing the operation and maintenance of the asset. The asset evolves by transiting between states and accumulating costs and income in the form of returns. From the model, a system of difference equations is developed whose variable is the expected accumulated return at any transition, and the z-transform is used to solve it. Deriving its mathematical expression, the formula for the preventive interval that maximizes the expected accumulated return is obtained. Both formulae are used in a case study. Taking advantage of the degraded state, it is shown that higher income increases the interval size while lower-income decreases it. The limits of these variations are also established. Simulation and numerical methods are used to validate analytic results.

The Efficacy of Social Media Communication in Engaging Citizen Scientists: Insights From the Jozi Bee Hotel Project

The Jozi Bee Hotel Project recruited Johannesburg residents to help gather data on solitary bee abundance. They were motivated and guided by a strategic communications campaign. This study explores social media’s impact on the citizen scientists’ project involvement, particularly their interaction with visual elements of the communications campaign. Analysis of engagement metrics reveals that social media played a crucial role in sustaining volunteer participation. This demonstrates that a well-executed, ongoing social media campaign has the power to maintain participant engagement throughout the duration of a research project. It suggests that a consistent, long-term social media strategy can drive citizen science participation.

ADAPTIVE LOB SCHEDULING FOR OPTIMIZING RESOURCE LEVELING AND CONSUMPTION

Several techniques were developed to improve resource allocation in construction projects, yet few in repetitive construction projects. Accordingly, this research aims at minimizing resource consumption and fluctuations using Line-of-Balance (LOB) repetitive scheduling technique. An optimization model has been developed consisting of three modules: LOB schedule module, resource leveling module, and optimization module. The model helps determine the optimum (a) start time for each activity considering any needed delays to level resources, (b) number of crews travelling from one unit to another, (c) unit to change the number of crews, and (d) the new changed number of crews. Unlike the existing efforts, the developed model provides the capabilities of (a) allowing the change of number of crews of an activity at a certain repetitive unit to increase or decrease the progress rate; and (b) accommodates non-serial repetitive projects to enhance the model’s practicality. Using a pipeline project, the model outperformed the existing LOB-based models in minimizing resource consumption and fluctuations within the desired project duration. This study offers the project planners a useful tool to efficiently utilize their projects’ resources and avoid hidden costs due to inefficient resource utilization on-site as well as overcoming shortage in resource availability.

Analysis of the impact of corrective actions for stochastic project networks

In project management, a project plan is constructed that assigns a planned start time to each project activity. Based on this plan, the total planned project duration and cost can be determined. However, during project execution, deviations from the plan are inevitable due to uncertainty and variability. When these deviations endanger the timely completion of projects, the project manager should take corrective actions to get the project back on track. In this study, corrective actions are modelled as modifications of the original activity duration distributions (i.e., reduced mean and/or standard deviation) to account for the uncertain nature of their impact. Further, an analytical procedure is developed to rank activities according to their expected impact on the project duration distribution when they are controlled by a corrective action. This activity ranking is used to determine the number of actions that should be taken and to select the set of activities that will be controlled. A computational experiment on a large set of project networks with varying network complexity and network structures has been conducted. These experiments have shown that taking actions on a relatively small subset of activities, rather than on the entire set of project activities, proves more efficient, when the subset of activities is carefully selected. More precisely, the efficiency of the corrective actions process depends on both the number of actions and the activity selection criterion (activity ranking).

Improving Project Estimates at Completion through Progress-Based Performance Factors

From a managerial perspective, project success hinges on estimates at completion as they allow tailoring response actions to cost and schedule overruns. While the literature is moving towards sophisticated approaches, standard methodologies, such as Earned-Value Management (EVM) and Earned Schedule (ES), are barely implemented in certain contexts. Therefore, it is necessary to improve performance forecasting without increasing its difficulty. The objective of this study was twofold. First, to guide modeling and implementing project progress within cost and to schedule Performance Factors (PFs). Second, to test several PFs utilized within EVM and ES formulae to forecast project cost and duration at completion. Progress indicators dynamically adjust the evaluation approach, shifting from neutral to conservative as the project progresses, either physically or temporally. This study compared the performance of the progress-based PFs against EVM and ES standard, combined, and average-based PFs on a dataset of 65 real construction projects, in both cost and duration forecasting. The results show that progress-based PFs provide more accurate, precise, and timely forecasts than other PFs. This study allows practitioners to select one or more of the proposed PFs, or even to develop one, following the guidelines provided, to reflect best their assumptions about the future course of project performance.

The Multi-Skilled Resource-Constrained Project Scheduling Problem: A Systematic Review and an Exploration of Future Landscapes

Abstract The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem’s scale, complexity, and constraints, necessitating careful consideration of each method’s strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources.

Integration of Java Programming-Based Pert and Earned Value Management Methods Based on Residential Precast Case Study to Improve Project Cost Efficiency

The need for residential houses is still high for property and construction commodities. The availability of land and market prices for residential houses continue to increase yearly. Only 45.04% of Jakarta residents had a home ownership status in 2020. 2021 is the precast building mode. It is justified that precast construction structures can save 35% on construction costs and are 27.33% more reliable than ordinary concrete structures. The research examines the integration of the PERT method and earned value management (EVM) based on Java programming in residential precast case studies. The results of this study are that the implementation of the PERT method can increase the efficiency of project duration by 6%, and the implementation of the EVM method can increase project cost efficiency by 7% with the indicators Schedule Performance Index (SPI) = 1.01 (under schedule) and Cost Performance Index = 1, 07 (Under Budget)

Duration and Labor Resource Optimization for Construction Projects—A Conditional-Value-at-Risk-Based Analysis

The complexity and uncertainty of construction projects contribute to low efficiency in the construction industry. This research applied the Takt-time planning method to optimize the construction working process, and proposed a risk control framework based on Value at Risk (VaR) and Conditional Value at Risk (CVaR) approaches to explore and predict a project schedule and cost performance under different scenarios. This research selected a high-rise residential building project for a case study and collected 1672 productivity data samples. Arena Simulation models were established based on 90 combinations of labor assignments to assess Takt-time planning strategies’ impact on project performance in four scenarios. The VaR and CVaR evaluations at 75% and 90% confidence levels were compared to balance project benefits and risks. Without any overtime or additional workers, this research found a Takt-time planning method that can reduce the project duration by 20.2% and labor costs by 2.1% at the same time, using a labor assignment of 12 bar placers, 12 carpenters, and 5 pipefitters. The findings can assist construction managers to achieve a shorter duration, reduced cost, and safer work environment, which will be very effective and beneficial to improve project overall performance.

A prediction-based model to optimize construction programs: Considering time, cost, energy consumption, and CO2 emissions trade-off

Efforts to combat climate change demand global action to promote a sustainable development worldwide. Given the construction industry's significant energy consumption, it is crucial to curtail energy consumption and CO2 emissions in this sector. This study aims to predict and trade-off four essential objectives: time, cost, energy consumption, and CO2 emissions. Initially, various factors affecting time and cost was identified using the CoCoSo method. Subsequently, 11 machine learning algorithms were employed to predict all four targets. Results revealed that support vector regression achieved an accuracy of 95.77% in project duration prediction, while the neural network algorithm demonstrated accuracy of 93.64%, 97.63%, and 97.99% in predicting cost, energy consumption, and CO2 emissions, respectively. Additionally, the research findings from six scenarios highlighted the non-linear relationship among project objectives. Deviating from the optimal point resulted in increases on both sides, with more significant impacts observed at values less than optimal duration. This research equips engineers with a valuable tool for accurately predicting project outcomes by adjusting input values, ensuring successful project execution and efficient resource allocation. Additionally, the framework's predictive abilities are beneficial for companies with energy and CO2 policies, influencing various sectors, regions, and policies tied to energy, emissions, costs, climate change, ESG, and UN SDGs. Its application extends to shaping regulations, strategies, and targets.

Critical Risk Factors influencing Time Schedule of Residential Projects in Pakistan

Delays in residential construction projects are common issues in both developing and developed countries. This research aims to identify the key reasons behind these delays in Pakistan. When the projects run behind schedule, this can lead to problems such as exceeding the budget, reduced productivity at the construction site, and potentially lower quality work. The study involved a detailed survey using questionnaires that were addressed to industry professionals. Their responses were used to rank the critical factors causing delays. The most significant factors were found to be fluctuating material prices, financial challenges for contractors, underestimating project duration, poor site management, inexperienced contractors, ineffective project management, communication gaps among project stakeholders, shortage of skilled workers, changes in project design, unqualified contractors, and inadequate project planning. Identifying these critical factors through the Relative Importance Index (RII) method can help in addressing and preventing delays in Pakistan's construction projects, ensuring timely completion and better project outcomes in the future.

The Impact of Climate Change on Construction Activity Performance

There are specific construction operations that require weather forecast data to make short-term decisions regarding construction; however, most resource-related decision making and all project management plans must be carried out to anticipate weather conditions beyond the capabilities of the currently available forecasting technologies. In this study, a series of single- and multi-risk analyses were performed with ~9 km grid resolution over Türkiye using combinations of weather and climate variables and their threshold values which have an impact on the execution and performance of construction activities. These analyses will improve the predictability of potential delays, enable the project to be scheduled on a future-proof basis by considering the calculated normal and periodic predictions on the grid scale, and serve as a dispute resolution tool for related claims. A comprehensive case study showcasing the methodology and illustrating its application shows that the project duration is expected to be extended because of the impact of climate on both historical and future periods. While the original project duration was 207 days, when climate effects were considered, the optimum mean and median values increased to 255 and 238 days, respectively, for the historical period. The optimum duration mean and median change to 239 days by the end of the century, according to the SSP5-8.5 scenario, if the construction schedules consider climate change. The change in duration was mainly due to rising temperatures, which increased winter workability and reduced summer workability. However, if the historical practices are carried over to future schedules, the mean and median increase to 258 days and 244 days, respectively, which may cause unavoidable direct, indirect, or overhead costs.

ANALYSIS OF THE FEASIBILITY STUDY OF THE DOMESTIC WASTE BUSINESS IN SIDENRENG RAPPANG DISTRICT DUCK BONE FLOUR (DUCK BONE FLOUR)

Duck bone waste which is a domestic waste in Sidenreng Rappang Regency has not been utilized and there is no handler which is feared will pollute the environment. So it is necessary to use it by making it economically valuable by making duck bone flour. The purpose of this study was to analyze the feasibility of investing in duck bone waste (Duck Bone Flour). The method used is a saturated sample where all of the population is a sample because the number of samples is limited. Methods of data analysis using financial analysis. The analytical tool used for the financial aspect uses analysis of the Payback Period (PP), Net Present Value (NPV), Internal Rate of Return (IRR), and Profitability Index (PI), to determine whether a business is feasible or not. The results showed that the Duckbone Flour Business, from a financial point of view, with a project duration of 10 years and a discount rate of 10%, it proved feasible. An analysis of admission criteria found a payback period (PP) of 6 months and 1 week. Net Present Value (NPV) is IDR 1,730,085,150, Profitability Index (PI) is 1.63% and Internal Rate of Return (IRR) is 32%. This research is important to be carried out as a source of information for the community and the government about the economic value of duck bone waste.

Project acceleration using temporary workers with heterogeneous efficiency: optimal and best recruitment policies

ABSTRACT This paper investigates recruitment decisions of temporary work with several levels of experience, from beginners to experts (heterogeneous efficiency) to accelerate a manufacturing project with the objective of labour cost minimisation. A Mixed Integer Linear Program formulation of the problem is developed to find the optimal pool of temporary work of each experience level so as to minimise the cost subject to a desired deadline. The deadline is decreased decrementally in order to identify any potential patterns of recruitment along the reduction in the project duration. A variety of projects, efficiency levels with respect to years of experience and cost scenarios are considered in an experimental framework. Simulation results show a high sensitivity of the optimal pool of temporary work to the project type, efficiency levels and costs. A worst-case analysis suggests that using solely expert temporary workers might be the best recruiting policy to accelerate a project, whatever the desired deadline.

Assessing the Delay, Cost, and Quality Risks of Claims on Construction Contract Performance

Conflicts are frequent within the complex professional environment of the construction industry. If claims cannot be overcome amicably, they result in disputes that lead to litigation. Identification of the causes of these claims and their impact on the duration, cost, and quality of the final project is expected to facilitate the prevention of unsuccessful performance of construction contracts. The novelty of this study is that after codifying the most common causes of construction contract claims derived from the extant literature, they are further investigated in terms of their probability of occurrence and the perceived impact they have on the project completion time, its total cost, and quality. Based on calculated relative importance indices from expert opinion, this paper proposes probability and severity of impact values for 39 common causes of claims in the construction industry. These can be applied to calculate their risk values for stakeholders in public construction contracts to plan mitigation measures for contractual claims. The findings show that the top five highest risk causes of contractual claims in the Greek construction industry are changes in quantities, work, or scope, design quality deficiencies or errors, payment delays, delays in work progress, and the financial failure of the contractor.