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Agent-based simulation for multi-resource-constrained scheduling of scattered atypical repetitive projects
Rawan Abdelrahman Sultan1, Khaled Hamdy2, Yasmeen A S Essawy2,3
1Department of Structural Engineering, Ain Shams University (ASU), Cairo, Egypt. rawansultan01@gmail.com.
Scientific Reports
|April 3, 2026
Summary
This study introduces a simulation-optimization framework for complex construction projects, significantly reducing project duration by 46.25% through advanced scheduling and resource allocation. The new approach enhances planning for atypical repetitive projects with scattered sites.
Area of Science:
- Construction Management
- Operations Research
- Computational Science
Background:
- Conventional planning tools inadequately address the logistical challenges of scattered, short-duration, atypical repetitive projects.
- Simulation-based planning, while documented, has seen limited practical adoption in the construction industry.
Purpose of the Study:
- To propose and validate a novel simulation-optimization framework for enhancing multi-resource-constrained scheduling decisions in complex construction projects.
- To explore makespan-oriented resource allocation and sequencing strategies under various feasibility constraints.
Main Methods:
- Integration of GIS-enabled agent-based modeling, stochastic simulation, and a genetic algorithm (GA)-based optimization experiment within the AnyLogic simulation environment.
- Development of an adaptive scheduling algorithm for dispersed sites and a distance-aware rule-based assignment heuristic to minimize travel and ensure continuity.
- Incorporation of stochastic disruption modeling and spatially explicit visualizations for managerial interpretation.
Main Results:
- The framework consistently generated substantially shorter schedules across stochastic replications when applied to a real-world telecom megaproject.
- Achieved a 46.25% reduction in project duration compared to the practitioner's baseline heuristic plan.
- Recorded emergent performance metrics including travel time/share, crew idle time, and utilization to evaluate operational behavior alongside makespan.
Conclusions:
- The proposed simulation-optimization framework effectively addresses the complexities of scheduling atypical repetitive projects, leading to significant reductions in project duration.
- The framework provides valuable insights into operational behavior through emergent performance metrics and enhanced visualizations.
- Future work should incorporate real-time traffic data, explicit cost modeling, and distance-penalty functions for further refinement.
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