AI Scheduler for Capital Projects Using Reinforcement Learning
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Solution Overview
Problem
Current software solutions for scheduling large-scale capital projects require significant user input and expertise, leading to challenges in managing complex resource interplay and constraints, often resulting in projects running over budget and behind schedule.
Innovation Solution
A system utilizing a trained reinforcement learning engine with neural networks to generate optimized task schedules based on inputs from total work, resource, and constraints databases, prioritizing objectives such as minimizing slowdown, completion time, and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If heuristic approaches are applied to schedule tasks, then the scheduling process becomes manageable for small sets of tasks, but the complexity increases significantly when scheduling large projects
Solution Approach 1:
The patent replaces manual heuristic scheduling methods with an automated artificial intelligence system that uses machine learning algorithms to generate optimized schedules. The AI scheduler automatically processes work breakdown structures, resource constraints, and project requirements without requiring manual intervention, thereby resolving the contradiction between scheduling capability and scheduling complexity.
2Reliability
If comprehensive manual scheduling is performed to accommodate resources and constraints, then project schedules can be optimized, but the process requires significant user expertise and time
Solution Approach 1:
The AI scheduler operates autonomously to generate optimized schedules without requiring continuous human intervention or expertise. The system self-manages the complex task of coordinating resources and constraints by automatically processing project data, evaluating multiple scheduling scenarios, and producing optimized schedules that would otherwise require extensive manual effort and specialized knowledge.
Solution Approach 2:
The system performs comprehensive schedule optimization in advance by generating detailed schedules that account for all resources and constraints before project execution begins. This preliminary automated optimization eliminates the need for time-consuming manual adjustments during project implementation.
3Productivity
If expert manual scheduling is used for large projects, then some level of optimization can be achieved, but projects still run beyond budget and behind schedule
Solution Approach 1:
The patent replaces manual expert scheduling with an AI system that processes scheduling optimization at a scale and speed unattainable by human experts. The AI scheduler evaluates numerous scheduling scenarios simultaneously, considering all resource constraints and project requirements, to generate optimized schedules that improve project delivery efficiency and reduce completion time beyond what manual methods can achieve.
Data Source
AI summary
A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.