Agent-based simulation for multi-resource-constrained scheduling of scattered atypical repetitive projects

Conventional planning tools struggle to manage the logistical complexity of scattered, short-duration atypical repetitive projects. Although simulation-based planning is well documented, its practical adoption in construction remains limited. This study proposes a simulation-optimization framework implemented in AnyLogic that integrates GIS-enabled agent-based modeling, stochastic simulation, and a GA-based optimization experiment to enable exploration of multi-resource-constrained scheduling decisions. Conceptually informed by multi-mode resource-constrained time-cost trade-off and multiple traveling-salesman formulations, the framework explores makespan-oriented resource-allocation and sequencing configurations under capacity, readiness, and spatial feasibility constraints. Key components include: (i) an adaptive scheduling algorithm for dispersed sites, (ii) a distance-aware rule-based assignment heuristic that supports continuity while limiting avoidable travel, (iii) stochastic disruption modeling, and (iv) spatially explicit visualizations to aid managerial interpretation. Applied to a real-world telecom megaproject, the framework consistently generated substantially shorter schedules across stochastic replications, achieving 46.25% reduction in project duration relative to the practitioner's baseline heuristic plan. Travel time/share, crew idle time, and utilization were recorded as emergent performance metrics and were used to evaluate operational behavior alongside makespan. Limitations include the absence of real-time traffic data, explicit cost modeling, and distance-penalty functions. Future work will incorporate economic and distance-based constraints and broaden comparative evaluation against alternative scheduling approaches.