Critical Path Analysis and Resource-Leveling Optimization for Multi-Project Engineering Portfolios

Abstract

This study quantitatively evaluated the integration of Critical Path Analysis and resource-leveling optimization for improving schedule and resource performance across multi-project engineering portfolios. A longitudinal quasi-experimental optimization design was applied to 186 engineering projects comprising 8,742 scheduled activities, 12,486 precedence relationships, and 684 formal milestones. Baseline Critical Path Method (CPM) schedules were compared with resource-constrained and optimized schedules using project duration, portfolio makes pan, critical-path characteristics, float consumption, activity displacement, resource utilization, workload variability, interproject conflicts, lateness, and completion reliability. Baseline mean project duration was 24.8 months, with a portfolio makes pan of 62.4 months. After finite workforce, equipment, and specialist-resource constraints were imposed, mean duration increased to 29.6 months, representing a 19.4% extension, while portfolio makes pan increased by 19.9% to 74.8 months. Resource constraints consumed 64.7% of available float, increased critical activities from 31.8% to 42.6%, and reduced target-date completion to 41.9%. Resource-leveling optimization reduced mean project duration to 26.1 months and portfolio make span to 66.7 months while increasing on-time completion to 78.5%. Peak resource demand decreased by 17.4%, maximum utilization declined from 118.4% to 98.6%, workload variability decreased by 43.0%, overtime requirements fell by 64.1%, and interproject resource conflicts declined by 73.8%. High resource scarcity, network complexity, resource intensity, temporal overlap, and bottleneck exposure were associated with greater scheduling deterioration. Resource contention was the strongest positive predictor of schedule extension (β = 0.36, p < 0.001), while available float demonstrated a protective association (β = −0.27, p < 0.001). The schedule-extension model explained 68.4% of observed variance. Hybrid optimization achieved a 17.4% mean improvement, a 1.2% optimality gap, and a 99.5% feasible-solution rate. Overall, integrating critical-path analysis with resource-leveling optimization substantially improved schedule feasibility, resource stability, portfolio coordination, and engineering project completion reliability.