This study aims to develop a five-dimensional (5D) optimization framework to resolve spatial and resource conflicts in construction scheduling. By integrating resource leveling and workspace congestion with the traditional time, cost and quality objectives, the research addresses the critical gap between theoretical scheduling models and practical site feasibility.
The framework uses triangular fuzzy numbers to model uncertainties in activity durations and costs. A Nondominated Sorting Genetic Algorithm III (NSGA-III) generates a comprehensive Pareto front for a complex highway interchange. To eliminate subjective bias in schedule selection, the methodology integrates cooperative game theory. An entropy-weighted nash Bargaining mechanism automatically evaluates the competing objectives to identify the optimal compromise.
The application of this framework demonstrates that integrating spatial and resource constraints substantially alters optimal execution paths. The Nash-derived schedule reduced spatial conflicts by nearly 38%, duration by 6.53%, and cost by 8.42% compared to standard practices. Variance analysis across 30 independent runs confirmed the statistical significance of these improvements, proving the model consistently outperforms conventional single-objective and multi objective baselines.
The primary contribution is a data-driven workflow that objectively resolves high-dimensional stakeholder conflicts. By leveraging game-theoretic principles, the framework provides an automated mechanism to secure balanced, mathematically fair schedules, mitigating the operational risks of trade stacking and severe resource peaks.
