Discrete Event Simulation with Constraint-Based Scheduling
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Solution Overview
Problem
Current discrete event simulation methods for manufacturing processes do not effectively accommodate and optimize the scheduling of tasks, leading to incomplete modeling and analysis of resource utilization and variability.
Innovation Solution
A computer-implemented method and apparatus that integrates discrete event simulation with scheduling analysis, allowing for iterative optimization of schedules within the simulation model, using a communications bridge to leverage data and parameters between simulation and analysis tools, enabling improved task sequencing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current discrete event simulation methods are used for manufacturing processes, then the simulation can model basic process flows, but the scheduling of tasks cannot be effectively optimized and resource utilization analysis remains incomplete
Solution Approach 1:
The patent combines discrete event simulation with scheduling analysis into a single integrated system. The simulation engine and scheduling analysis engine work together through a shared database, allowing task scheduling optimization to be performed within the simulation framework rather than as separate processes. This merging enables simultaneous optimization of process efficiency and scheduling complexity.
Solution Approach 2:
The integrated system performs multiple functions: it conducts discrete event simulation, performs scheduling analysis, optimizes task sequences, and analyzes resource utilization all within a single platform. The system can handle both simulation modeling and scheduling optimization without requiring separate tools, making it universally applicable to manufacturing process analysis.
2Adaptability or versatility
If traditional scheduling analysis is performed separately from simulation, then basic task sequencing can be established, but iterative optimization of schedules cannot be achieved
Solution Approach 1:
The system implements feedback loops where scheduling analysis results are fed back into the simulation model for re-evaluation. The simulation engine executes processes with optimized schedules, generates performance data, which is then used to further refine scheduling parameters. This iterative feedback process enables continuous schedule optimization while managing integration complexity through automated data exchange.
Solution Approach 2:
A communications bridge or interface layer acts as an intermediary between the simulation engine and scheduling analysis engine. This intermediary manages data exchange, parameter passing, and result transmission between the two components, enabling iterative optimization without requiring direct complex integration between the engines themselves.
3Measurement precision
If discrete event simulation models basic process behaviors, then time-based dynamics can be observed, but resource utilization and idle time analysis remain inaccurate
Solution Approach 1:
The system segments resource tracking into distinct components: resource allocation monitoring, utilization measurement, and idle time detection. Each aspect is handled by specific modules within the simulation and scheduling analysis engines, allowing precise measurement of resource metrics while managing modeling complexity through modular design.
Data Source
AI summary
A computer implemented method, apparatus, and computer usable program code for simulating a process. Data is received describing a process to form received data. A current discrete event simulation model is formed from the received data in a discrete event simulation engine, wherein the current discrete event simulation model includes a current schedule having a plurality of ordered tasks. A simulation of the current discrete event simulation model is performed in the discrete event simulation engine, wherein results are generated from running the current discrete event simulation model. Finally, a new schedule is generated from the current schedule and the results using schedule analysis tool.