Scheduling Of Construction Projects Research Articles

Application of the maturity model for collaborative scheduling for construction projects

PurposeThis paper introduces the weighting, analysis and validation method used in the development of the Maturity Model for Collaborative Scheduling (MMCS). The scoring and ranking process introduced by the MMCS fills a gap in the literature by supporting the selection of collaborative scheduling (CS) practices that yield more weight toward the achievement of higher maturity levels in the development and implementation of CS. The ranking process can then be used during pre/post project execution to track collaborative scheduling in practice against the model’s weighting and provide the project team with constructive feedback and actionable steps for reaching the next highest level of collaboration.Design/methodology/approachThe MMCS, which focuses on five pillars (key areas of interest for CS) and related swim lanes (specific attributes), covers a broad range of areas in the construction industry and was coded into a survey. The relative weights of pillars and swim lanes were then established using the Delphi method with the group of subject matter experts (SMEs), analyzed using multi-objective decision analysis (MODA) and validated using 241 answers to a survey with questions drawn from the MMCS, including organizations across the industry in the United States.FindingsThe project scoring defines bounds for bronze, silver and gold levels of collaboration in scheduling. Project evaluations can then be used to identify areas for continuous improvement and enhanced collaboration. We offer recommendations and best practices for project improvement.Originality/valueTwo original contributions resulted from this work: (1) a method to elicit weights based on a combination of Delphi, MODA and survey methods was used to develop and validate a scale with three different maturity levels to support the use and continuous improvement of CS practices and (2) a validated model was used to assess the maturity level of CS in construction projects alongside specific recommendations to move upward in terms of maturity. In practice, project leaders can use this model to assess project performance, advance the project’s maturity and guide continuous improvement efforts for enhanced collaboration.

Optimizing Competency-Based Human Resource Allocation in Construction Project Scheduling: A Multi-Objective Meta-Heuristic Approach

Optimizing Competency-Based Human Resource Allocation in Construction Project Scheduling: A Multi-Objective Meta-Heuristic Approach

Predicting construction schedule performance with last planner system and machine learning

Construction project schedules deviate due to uncertainty and variability unless timely actions are implemented. While the limitations of traditional management practices help to facilitate such assessments have been widely covered, quantitative LPS research has shown empirical relations between its indicators, performance, and outcome. This paper creates a model to predict the schedule outcome during early execution using the LPS metrics and the Design Science approach. 18 solution artifacts were evaluated to predict three schedule outcome variables using 15 indicators in 1464 sample points collected from nine subsequent execution intervals from 164 projects. The selected artifact predicted the schedule outcome, which is a combination of the schedule performance at planned completion and the actual schedule deviation at completion, with a MAE of 1.24% and R2 = 0.96 averaged across the nine execution intervals, using solely LPS indicators. The model can be applied as an early warning mechanism in LPS IT-Support software.

Digital Management Methodology for Building Production Optimization through Digital Twin and Artificial Intelligence Integration

In a construction project schedule, delays in delivery are one of the most important problems. Delays can be caused by several project components; however, the issue is amplified when delays occur simultaneously. Classifying delays is relevant in order to allocate responsibility to the parties. In Italy, the delay in the delivery of medium and large-sized works in residential urban centers is about 15% compared to the project forecast. Moreover, the AECO sector’s ability to adapt to emerging challenges, such as environmental sustainability and digitization, is limited by the lack of innovation in management methods. The aim of this research is to create a methodology for managing the built and to-be-built environment in a digital way. This will optimize the building process by reducing delays and waste of resources. The methodology will use tools such as digital twin (DT), Building Information Modeling (BIM), Internet of Things (IoT), and Artificial Intelligence (AI) algorithms. The integration of lean construction practices can make the use of these technologies even more efficient, supporting better workflow management by using the BIM environment. The paper presents a methodology that can be applied to various scaling factors and scenarios. It is also useful for construction sites that are already in progress. As highlighted below, this brings significant economic-temporal advantages.

Financial resource scheduling and road construction projects performance in Nairobi Metropolitan, Kenya

Kenya has seen a boom in road building during the last couple of years as a result of the government's significant investments in the construction industry to upgrade infrastructure, such as road networks. Road network construction is one of the key pillar of the country long term vision 2030. However, the performance of the road projects has experienced various challenges leaving some roads uncompleted, others poorly done after huge investment of public resources. This study sought to evaluate the influence of financial resources scheduling on the performance of road construction projects in Nairobi Metropolitan, Kenya. The research was based on resource dependence theory. A descriptive design as well as the cross-sectional survey was used. The population of interest for this study consisted of 39 road construction projects being implemented by Kenya Urban Roads Authority and Kenya National Highway Authority within the Nairobi metropolitan area. A semi structured questionnaire was utilized to collect primary data. The quantitative data was analyzed using descriptive and inferential statistics. The study results revealed projects resource scheduling had a positive and significant effect on project performance of road construction projects in Nairobi Metropolitan, Kenya. The study concludes that effective financial resource scheduling enables that organization to manage risk that may occur from changes in the project’s outcome. Financial resource scheduling starts from developing the project budget which is a process for allocating administered and departmental funds necessary to build a financial foundation for producing stated project deliverables.

Intelligent scheduling optimization of prefabricated components for assembly buildings

Prefabricated construction, as an emerging architectural model, has the characteristics of fast construction process and flexible design, and has been widely used in the field of architecture in China. As the core component of prefabricated buildings, the schedule of prefabricated components directly affects the progress and quality of the project. This study extends the volume of prefabricated components from a single building to a group of buildings, designs and solves an intelligent scheduling scheme model based on project construction schedule and procurement plan, and proposes two reference building group scheduling schemes and their applicable conditions. Genetic algorithm is used to optimize vehicle scheduling and transportation paths to achieve intelligent scheduling of prefabricated components in prefabricated building groups, ensuring project progress and quality.

Fuzzy-Graphical Evaluation and Review Technique for Scheduling Construction Projects

Fuzzy-Graphical Evaluation and Review Technique for Scheduling Construction Projects

Building Information Modeling (BIM) for Construction Project Schedule Management: A Review

Nowadays, the implementation and application of the BIM process in construction project management is a pressing need. This aligns with the global development trends in the construction sector and project information management in general. Numerous scholars and companies are actively engaged in learning, understanding, and investigating various aspects of BIM to stay up-to-date and meet the inevitable developmental requirements. This study focuses on the role and application of BIM, intending to identify limitations that hinder its fulfillment of expectations in project schedule management. In addition, it explores studies that show how other countries have effectively employed BIM in project management and progress tracking throughout the project lifecycle. The study aims to address three main objectives: (a) comprehensively examine and provide evidence related to the concept of BIM in project schedule management, (b) present the benefits of applying BIM in comparison to traditional methods in project management and operation, and (c) identify limitations stemming from various factors that may pose challenges in the application of BIM in project schedule management.

Accelerating the middle – rise building construction duration using BIM 4D, a case study in central Jakarta

Construction planning involves scheduling and sequencing of project activities for proper execution. Research was conducted to obtain and enhance construction project scheduling, which was good and in accordance with the targets achieved. The results of the project performance analysis in the 9th month using the Earned Value Method resulted in SPI <1 and SV <0, indicating that the project experienced delays. Scheduling using the PDM based on the actual duration of the project shows that the project will be completed in 511 days. The project duration can be accelerated to 429 days by combining overlap and overtime methods. The scheduling results were simulated using BIM 4D to generate a visualization of the scheduling to improve the project monitoring process.

A study on time schedules for construction projects in Hanoi, Vietnam

With its position as the capital, Hanoi is the political center as well as the second largest economic center of the country. Therefore, the city is always allocated a large budget in construction investment to create material facilities for political tasks and economic and social development. During the implementation of construction projects, a number of difficulties and limitations have appeared. In which, projects are delayed in construction and disbursement, reducing investment efficiency and not meeting the expectations of the government and people. From this fact, the authors have conducted a study to evaluate the causes affecting the time schedule of construction projects in Hanoi. The method F-APH (Fuzzy Analytic Hierarchy Process) was used to analyze data objectively and accurately about the causes affecting the time schedule. From there, these causes are classified into groups of subjective causes (from within the project) and groups of objective causes (from outside the project). The results show that subjective causes, originating from project participants, have a stronger influence than objective causes. A number of specific proposals to the actors involved in construction projects are made to eliminate or limit the impact of the causes of construction progress.

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

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.

Analisa Produktivitas Tenaga Kerja Pada Proyek Pembangunan Gedung Bertingkat Dengan Menggunakan Metode Time Study (Studi Kasus: Proyek Pembangunan Gedung Kejaksaan Tinggi Kalimantan Tengah)

The productivity of workers determines the success of the implementation of the construction project schedule, because it has an impact on the compatibility between the planning of the construction schedule and the progress of work in the field. The worker productivity that will be calculated is the work of reinforcing beams in the Central Kalimantan High Prosecutor's Office building construction project. The calculation of worker productivity will be carried out using the time study method.This study aims to determine the extent of worker productivity during working hours in the morning and evening. The method used in this research is the time study method, which is the measurement of labor productivity in the field by determining the standard time of a job. From this method can be seen the comparison of worker productivity from the results of a job. In this case study, the factors that affect worker productivity will be identified. The data collection technique in this study used a questionnaire by giving written questions to respondents that were relevant to the research objectives. Based on the results of the analysis, it is known that the productivity of workers in the morning higher with an average weight of 41,825 kg/hour than in the afternoon of 40,433 kg/hour. Where on the first day the productivity of morning workers is 12,56% and in the afternoon it is 11,97%. On the second day the productivity of morning workers is 12,81% and in the afternoon it is 12,18%. On the third day the productivity of morning workers is 12,68% and in the afternoon 12,43%. On the fourth day the productivity of morning workers is 12,85% and in the afternoon 12,52%. In general, it can be seen that the productivity of workers in the morning is higher than that of workers in the afternoon. There are 3 categories of factors that influence worker productivity: not influential (TB) are wind gusts; influential (B) are weather factors; number of workers, age, wages; managerial factors; effectiveness of working hours; and very influential are skill factors

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

Developing a Bayesian belief network model for quantifying and predicting claim causes-related schedule delays

Nowadays, construction claims have become an inevitable part of the construction project, and they have a serious impact on the project schedule. This study aims to develop a Bayesian belief network (BBN) model for quantifying and predicting claim cause-related schedule delays in Vietnam. Based on data from survey questionnaires and interviews with experts, 23 critical claim causes of construction project schedule delays and 17 significant cause-effect relationship pairs were found and used to build the BBNs models. The results obtained from the BBNs models are good at quantifying the probability of schedule delays in three different case study projects. In the case study of a transportation project, for example, there is a major delay of 34 months, which is about 38.64% of the original length of the project. The model predicted that there is a 69% chance that the project’s schedule delay will be more than 20% of the original length. Using these BBNs models, industry practitioners can prevent claim causes-related schedule delays early. This approach is general and can be applied to any construction project in other countries with small adjustments, especially in the case of developing countries similar to Vietnam.

Analysis of factors influencing resource scheduling for emergency construction projects considering multiple spatial characteristics

In response to the growing frequency of international emergencies, the demand for resources dedicated to emergency construction projects has significantly increased due to their pivotal role in disaster containment and mitigation. This article examines the factors influencing the allocation of multi-spatial resources for such projects, conducting both causal and hierarchical analyses to unveil their interdependent mechanisms. It addresses a theoretical research gap in resource scheduling through bibliometric analysis. The study utilizes the Fuzzy Decision-Making Laboratory Analysis (Fuzzy-DEMATEL) method to identify key factors within the system and constructs a hierarchical model using Interpretive Structural Modeling (ISM) to systematically analyze the factors affecting multi-space resource allocation in emergency construction projects. The results reveal 43 influencing factors, categorized into 11 levels, with the political and social environment, resource scheduling determination, and design changes standing out as the primary influencers. Additionally, the article proposes a three-tiered strategy based on experimental results to offer theoretical insights for multi-spatial resource scheduling in emergency construction projects.

Scheduling of Bus Rapid Transit Station Construction Project Using Dependency Structure Matrix in India

Scheduling of Bus Rapid Transit Station Construction Project Using Dependency Structure Matrix in India

Improvement of risk management in the project scheduling of road construction projects – Case study

In this paper the risks that affect highway construction projects duration are studied by qualitative and quantitative approaches. For the qualitative analysis the consistency of expert opinions was evaluated, and two methods were applied to identify critical risks that affect local projects. The quantitative analysis studied the impact of those risks over the activities of a local project and also the relationship between risks using fuzzy logic and Monte Carlo simulation.

Critical Path Analysis Using Microsoft Project 2016 on the Implementation Schedule of Residential House Construction Projects

A project requires good planning, careful execution and effective and efficient use of resources. The implementation of the development of a construction project consists of a series of activities that are interrelated with one another. This is where the importance of planning and scheduling projects properly to facilitate implementation in the field and development can be completed on time according to schedule. The existence of obstacles or obstacles that will occur in the process of implementing a construction project can be seen and predicted from the level of urgency of the work items to be carried out, the analysis that will be used to predict the constraints or obstacles that may occur is by analyzing the critical path of the project implementation schedule construction. The use of Microsoft Project is very effective in analyzing data and determining critical paths. Based on the critical path analysis that has been carried out on the construction project schedule for the construction of residential houses, it is found that several work sub-items are on the critical path, especially in preparatory work and structural work, which means that the work sub-item in this work must pay close attention to the implementation process both in terms of resource readiness. human and equipment.

Optimized variable resource allocation framework for scheduling of fast-track industrial construction projects

Efficient scheduling and resource allocation for large-scale industrial projects is challenging due to their size and complexity, especially with fast-track contracts, which often lack detailed information during the early planning phase. This paper introduces a data-driven workface planning framework to enhance scheduling and resource allocation while accommodating uncertainties and constraints (e.g., minimum and maximum resource allocation curves, dynamic predecessor relationships, congestion limits). This framework employs an integrated approach, combining time-stepped simulation with graph-based optimization. By leveraging historical data and expert knowledge, the data-driven framework mitigates certain subjective assumptions, including durations and resource allocations. In practical application, the framework generates near-optimal schedules, even with limited information. Applying the framework to a fast-track industrial construction project case study demonstrated enhanced resource allocation. These findings offer practical benefits to industrial projects regarding time and cost savings and serve as a foundation for future research in data-driven project planning approaches.