Optimizing The Number Of Mechanic Crews And Heavy Equipment Replacement Rule Options To Reduce Heavy Equipment Unplanned Downtime, Enhance Heavy Equipment On Running, And Maximize Profit At PT. XYZ’s Construction Project

Abstract

This research aims to reduce unplanned downtime, enhance the on-running time, and maximize profits from heavy equipment in PT. XYZ's construction projects through the application of Agent-Based Modeling (ABM). The study uses ABM to simulate the interaction between mechanical crews and heavy equipment units. It explores various optimization scenarios involving the number of crews and heavy equipment replacement rule options. Data was collected from field observations, and company internal reports, then implemented in AnyLogic software for simulation. The simulation results indicate that a combination of five mechanical crews and a heavy equipment replacement rule option after five preventive maintenance (PM) periods significantly reduces unplanned downtime by 6.13%, increases on-running time to 89.32%, and maximizes profit to Rp. 11,052.04 million. Model validation via a two-sample t-test shows no significant difference between real and simulated data (P-value = 0.3908). This research introduces an integrated ABM approach that combines equipment availability, maintenance crew availability, and profit optimization, bridging gaps left by prior studies that analyzed these factors separately. The findings provide practical insights into optimizing heavy equipment management, emphasizing the importance of proactive maintenance strategies and efficient resource allocation to reduce operational costs and improve sustainability in large-scale construction projects.