AI Auto-Scheduler for Capital Project Optimization
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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 exceeding budgets and timelines.
Innovation Solution
A system utilizing a deep reinforcement learning trained AI auto-scheduler that receives inputs from total work, resources, and constraints databases to generate optimized schedules, prioritizing objectives such as minimizing slowdowns, completion time, and resource utilization, and automatically updates based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional software solutions are used for scheduling large-scale capital projects, then user control and flexibility are maintained, but the system requires significant user input and expertise, increasing operational complexity and time consumption
Solution Approach 1:
The AI auto-scheduler performs scheduling operations autonomously by receiving only high-level project parameters and constraints from users, then automatically generating optimized schedules without requiring detailed user input or manual scheduling operations. The system serves itself by internally managing resource allocation, task sequencing, and constraint satisfaction.
Solution Approach 2:
The patent replaces manual scheduling operations (mechanical human expertise and intervention) with an AI-based automated scheduling system. The AI model processes project data, applies scheduling algorithms, and generates schedules automatically, substituting the need for human schedulers to manually manipulate scheduling software.
2Adaptability or versatility
If traditional heuristic approaches are applied to scheduling, then simplicity is maintained for small tasks, but the approach becomes inadequate for large-scale projects with complex resource interplay and constraints
Solution Approach 1:
The patent transforms the scheduling problem by changing the parameters fed to the AI model from detailed task-level inputs to high-level project parameters and constraints. This parameter transformation enables the system to handle large-scale complex projects by receiving simplified inputs while generating detailed optimized schedules through the AI's internal processing.
3Reliability
If manual scheduling is performed by skilled experts, then flexibility in handling constraints is improved, but projects still run beyond budget and behind schedule due to the complexity of assembling schedules
Solution Approach 1:
The AI auto-scheduler incorporates feedback mechanisms by continuously monitoring project progress, resource availability, and constraint satisfaction. The system uses this feedback to dynamically adjust and optimize schedules, ensuring better adherence to project timelines and budgets while adapting to changing conditions in real-time.
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.