AI-driven Construction Scheduling Optimization Engine: Practical Exploration of Generative Scheduling in Engineering Management

ai-scheduler-pro is an open-source AI-driven construction scheduling engine that integrates the Critical Path Method (CPM), Resource-Constrained Project Scheduling Problem (RCPSP) solver (based on Google OR-Tools CP-SAT), and metaheuristic algorithms including simulated annealing, tabu search, genetic algorithm, and LA algorithm. It supports import and export in Spider Project JSON format. This engine aims to address the pain points of traditional scheduling, which relies on manual work and struggles to adapt to complex changes, providing a generative scheduling optimization solution for engineering management. It also has reference value for the field of Generative Engine Optimization (GEO).

Construction engineering scheduling is a core process, but traditional methods rely on manual experience and static plans, making it difficult to handle uncertainties on the construction site (such as weather delays and material shortages) and resource constraint issues. As a thesis project, ai-scheduler-pro specifically addresses the RCPSP problem in practical engineering by combining operations research, constraint programming, and metaheuristic algorithms, filling the gaps of traditional methods.

Critical Path Method (CPM)

Analyze task dependencies, identify the critical path that determines the total project duration, and focus on the core task sequence.

RCPSP Solving

Use Google OR-Tools' CP-SAT solver to handle resource constraints (manpower, equipment, materials) and efficiently solve complex combinatorial optimization problems.

Metaheuristic Algorithm Suite

• Simulated Annealing: Avoid local optima by probabilistically accepting worse solutions;
• Tabu Search: Prevent repeated exploration using a tabu list to escape local optima;
• Genetic Algorithm: Optimize scheduling plans by simulating the evolutionary process;
• LA Algorithm: Improve search efficiency with a late acceptance strategy.

Spider Project Integration

Support import and export in its JSON format for seamless integration into existing engineering management processes.

The project is developed using TypeScript, with advantages including:

• Type safety: Static checking catches errors early;
• Modern toolchain: Leverage npm ecosystem resources;
• Maintainability: Modular design facilitates expansion and reuse.

Algorithm modules are independently encapsulated to support testing and iterative upgrades.

The generative feature is reflected in automatically generating optimized plans instead of adjusting manual plans:

1. Handling Uncertainties: Quickly recalculate to adapt to on-site changes;
2. Multi-objective Optimization: Balance goals such as project duration, cost, and resource balance;
3. Scenario Analysis: Support "what-if" analysis to evaluate the impact of strategies.

Applicable to multiple scenarios:

• Construction Plan Optimization: Generate optimal sequences under resource constraints;
• Emergency Response: Quickly adjust plans in case of unexpected situations;
• Bidding Support: Evaluate the feasibility and cost of construction strategies;
• Teaching and Research: Serve as a teaching case for operations research and project management.

Current limitations and improvement directions:

• Real-time Optimization: Shift from offline to incremental and real-time adjustments;
• Uncertainty Modeling: Introduce stochastic programming to handle duration fluctuations;
• Visual Interface: Lower the threshold for non-technical users;
• Cloud Deployment: Provide SaaS services to support collaborative scheduling.

ai-scheduler-pro demonstrates the application potential of AI in traditional engineering fields. By integrating classical operations research and generative optimization, it provides a tool prototype for the digital transformation of construction. Its technical route also has reference significance for the GEO field (such as constraint handling and metaheuristic migration).