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Advances in Construction Project Scheduling Optimization Using Primavera and Critical Path Analysis
Subject area: Science,Engineering and Technology · Area of research: Construction Project Scheduling
Abstract
Construction project delivery increasingly depends on advanced scheduling methodologies capable of managing complex task interdependencies, resource constraints, cost pressures, and uncertainty in execution environments. This review paper examines recent advances in construction project scheduling optimization through the integration of Primavera project management systems and Critical Path Analysis (CPA) techniques. The study synthesizes contemporary research and industry practices to evaluate how digital scheduling platforms enhance planning accuracy, improve resource allocation, and support data-driven decision-making across infrastructure and building projects. Particular attention is given to the evolution of Critical Path Method (CPM) applications within Primavera environments, including automated schedule optimization, risk-adjusted sequencing, constraint-based modeling, and real-time progress monitoring. The review further analyzes the incorporation of emerging technologies such as Building Information Modeling (BIM), artificial intelligence, and predictive analytics into Primavera-based workflows, enabling dynamic schedule recalibration and improved forecasting of project delays. Comparative discussions highlight limitations of traditional deterministic scheduling approaches and demonstrate how integrated optimization strategies reduce schedule variance and enhance project reliability. Additionally, the paper evaluates practical implementation challenges, including data quality issues, stakeholder coordination complexities, and the need for skilled schedulers capable of interpreting analytical outputs. By consolidating methodological developments and empirical findings, this review provides a structured understanding of how Primavera-supported Critical Path Analysis contributes to improved time performance, cost control, and risk mitigation in modern construction management. The paper concludes by identifying future research directions focused on autonomous scheduling systems, machine learning–driven optimization, and digital twin–enabled project control frameworks aimed at advancing sustainable and resilient construction delivery practices.
Keywords
Construction Scheduling Optimization, Primavera P6, Critical Path Method, Project Planning Analytics, Schedule Risk Management, Digital Construction Management
References
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How to cite this paper
@article{1719299,
author = {Dominic Feboh, Abeebat Ajirotutu, Ogochukwu T. Izuchukwu},
title = {Advances in Construction Project Scheduling Optimization Using Primavera and Critical Path Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {2},
number = {4},
pages = {195-212},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719299.pdf},
abstract = {Construction project delivery increasingly depends on advanced scheduling methodologies capable of managing complex task interdependencies, resource constraints, cost pressures, and uncertainty in execution environments. This review paper examines recent advances in construction project scheduling optimization through the integration of Primavera project management systems and Critical Path Analysis (CPA) techniques. The study synthesizes contemporary research and industry practices to evaluate how digital scheduling platforms enhance planning accuracy, improve resource allocation, and support data-driven decision-making across infrastructure and building projects. Particular attention is given to the evolution of Critical Path Method (CPM) applications within Primavera environments, including automated schedule optimization, risk-adjusted sequencing, constraint-based modeling, and real-time progress monitoring. The review further analyzes the incorporation of emerging technologies such as Building Information Modeling (BIM), artificial intelligence, and predictive analytics into Primavera-based workflows, enabling dynamic schedule recalibration and improved forecasting of project delays. Comparative discussions highlight limitations of traditional deterministic scheduling approaches and demonstrate how integrated optimization strategies reduce schedule variance and enhance project reliability. Additionally, the paper evaluates practical implementation challenges, including data quality issues, stakeholder coordination complexities, and the need for skilled schedulers capable of interpreting analytical outputs. By consolidating methodological developments and empirical findings, this review provides a structured understanding of how Primavera-supported Critical Path Analysis contributes to improved time performance, cost control, and risk mitigation in modern construction management. The paper concludes by identifying future research directions focused on autonomous scheduling systems, machine learning–driven optimization, and digital twin–enabled project control frameworks aimed at advancing sustainable and resilient construction delivery practices.},
keywords = {Construction Scheduling Optimization, Primavera P6, Critical Path Method, Project Planning Analytics, Schedule Risk Management, Digital Construction Management},
month = {October},
doi = {https://doi.org/10.64388/IREV2I4-1719299}
}