17986449. SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Nitika Sharma of Zirakpur (IN)
Sarbajit K. Rakshit of Kolkata (IN)
SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17986449 titled 'SCHEDULING PROJECT ACTIVITIES USING TWIN COMPUTING SIMULATION
Simplified Explanation
The patent application describes a proactive optimization method based on a critical path analysis for improving processes by identifying and mitigating delays.
- Collect data on tasks in a process's contextual situation
- Train a twin computing simulation model for each task using the collected data
- Run a contextual situation simulation using the simulation models to determine the critical path causing delays
- Identify an optimized task using machine learning to mitigate delays from the critical path
Potential Applications
This technology could be applied in various industries such as manufacturing, logistics, project management, and healthcare to optimize processes and improve efficiency.
Problems Solved
This technology addresses the issue of delays in processes by identifying the critical path causing the delays and optimizing tasks to mitigate them, leading to improved productivity and performance.
Benefits
The benefits of this technology include increased efficiency, reduced delays, improved process optimization, and enhanced overall performance in various industries.
Potential Commercial Applications
Potential commercial applications of this technology include process optimization software, consulting services for efficiency improvement, and integration into existing project management systems for enhanced performance.
Possible Prior Art
One possible prior art could be traditional critical path analysis methods used in project management to identify the sequence of tasks that determine the overall duration of a project. Another could be machine learning algorithms applied to process optimization in various industries.
Unanswered Questions
How does the twin computing simulation model differ from traditional simulation models in process optimization?
The patent application does not provide a detailed comparison between the twin computing simulation model and traditional simulation models in the context of process optimization.
What specific machine learning algorithms are employed to identify the optimized task in the contextual situation?
The patent application does not specify the exact machine learning algorithms used to determine the optimized task for mitigating delays in the process.
Original Abstract Submitted
A critical path based proactive optimization that includes collecting data on the tasks of contextual situation for performing a process and training a twin computing simulation model using the collected data for each task in the process. A contextual situation simulation is run using the simulation models for each task in the process to determine a critical path that causes delay in the process. An optimized task is determined from the tasks of the contextual situation using machine learning employing the collected data, wherein the optimized task mitigates delay in the process from the critical path.
