Modular Discrete Event Simulator for Flexible Model Reuse
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Solution Overview
Problem
Existing discrete event simulators face challenges in implementing changes to assumptions and reusing event models across different contexts due to hard-coded event flows and duplicated components, which hinders flexibility and efficiency in simulation analysis.
Innovation Solution
A discrete event simulator with a modular design that enables dynamic selection of event modules, uses object-oriented programming to link events, and facilitates plain-text configuration for easy addition or subtraction of components, along with Monte Carlo sampling for uncertainty analysis, allowing for rapid development and sensitivity testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded event flows and duplicated components are used in discrete event simulators, then implementation is straightforward, but flexibility and efficiency in simulation analysis deteriorate
Solution Approach 1:
The patent segments the discrete event simulator into modular event modules, each representing a distinct event type with its own parameters and logic. This segmentation allows individual modules to be independently developed, tested, and reused across different simulation contexts, thereby improving flexibility without complicating implementation.
Solution Approach 2:
The patent creates universal event module templates that can be configured through plain-text files to represent different event types. These templates define common structures and behaviors that can be instantiated multiple times with different parameters, enabling the same modular framework to handle diverse simulation scenarios efficiently.
2Ease of manufacture
If hard-coded event flows and duplicated components are used in discrete event simulators, then implementation is straightforward, but reusability of event models deteriorates
Solution Approach 1:
The patent implements event modules as reusable templates that can be copied and instantiated multiple times with different configurations. Each event module template contains the essential logic and structure that can be replicated across different simulation contexts, eliminating the need to rewrite event handling code and improving reusability while maintaining implementation simplicity.
Solution Approach 2:
The patent creates universal event module templates that serve multiple purposes across different simulation scenarios. These templates are designed to be configurable through plain-text parameters, allowing the same modular framework to adapt to various event types and simulation contexts, thereby enhancing reusability without complicating implementation.
3Reliability
If traditional discrete event simulation is used, then basic simulation functionality is achieved, but ability to analyze uncertainty and perform sensitivity testing deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where simulation results are systematically collected and analyzed to quantify uncertainty. The system performs multiple simulation runs with varied parameters and uses the results to compute statistical measures of uncertainty, providing feedback that enhances the analysis capability while maintaining the core simulation functionality.
Solution Approach 2:
The patent introduces dynamic parameter sampling through Monte Carlo methods, where parameters are randomly varied across simulation runs according to specified distributions. This dynamic approach allows the system to explore the impact of parameter variations on simulation outcomes, enabling uncertainty analysis and sensitivity testing while preserving the fundamental discrete event simulation functionality.
Data Source
AI summary
In discrete event simulation (DES), a timepoint for the DES is incremented. At the increments, event modules are executed to process members of at least one population class according to flow of the members amongst the event modules as defined by application programming interfaces (APIs). The event modules transform attributes of the members according to probabilistic event models defined by event model parameter files. DES data comprising attributes of the members at end-of-simulation are stored. In another aspect, simulation of a system model having parameters with associated probability density functions (PDFs) includes M outer loops each including: for each parameter, randomly drawing a parameter value with replacement from a set of discrete sampling points distributed over the PDF of the parameter; and running N simulations of the system using the system model with the randomly drawn parameter values and storing the simulation results annotated by the randomly drawn parameter values.