Stochastic Simulation Stream Management via Design of Experiments
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Solution Overview
Problem
Discrete event simulations struggle to effectively model and analyze systems with significant random components, as analytical models are often impractical or impossible, and existing methods lack efficient ways to generate realistic data without real-world observation.
Innovation Solution
The system and method for executing stochastic discrete event simulations involve associating streams of random numbers with stochastic model components, using a design of experiments approach to manage these streams and ensure reproducible results by resetting and advancing substreams during simulation, allowing for the study of system performance under various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If discrete event simulation is used to model systems with random components, then realistic performance data can be generated without real-world observation, but the complexity of managing random number streams and ensuring reproducible results increases
Solution Approach 1:
The random number generator is segmented into multiple independent streams, with each stream assigned to specific stochastic model components. This segmentation allows for organized management of randomness sources and enables selective control over different parts of the simulation, reducing overall management complexity while maintaining statistical validity.
Solution Approach 2:
Streams and substreams are pre-configured and associated with stochastic model components before simulation execution. The design of experiments framework pre-defines the structure of random number generation, allowing for efficient execution without complex runtime management decisions.
2Measurement precision
If multiple replicates are used in design of experiments to ensure statistical validity, then more comprehensive system performance analysis is achieved, but the computational time and resources increase
Solution Approach 1:
Multiple replicates are merged into a unified design of experiments framework that manages random number streams systematically. By combining the execution of multiple replicates under a single coordinated structure, the system achieves comprehensive statistical analysis while optimizing resource utilization and reducing redundant computations.
Solution Approach 2:
The simulation execution maintains continuous useful action by efficiently cycling through replicates and design points without interruption. The stream management system ensures that random number generation continues seamlessly across replicates, maximizing computational efficiency while maintaining statistical rigor.
3Adaptability or versatility
If streams of random numbers are associated with specific stochastic model components, then more precise control over simulation variability is achieved, but the complexity of stream management and coordination increases
Solution Approach 1:
The stream management system introduces an additional organizational dimension by implementing a hierarchy of streams and substreams. This dimensional structure allows for precise control over simulation variability at different levels, enabling fine-grained adaptation while managing complexity through structured organization rather than flat coordination.
Solution Approach 2:
The design of experiments framework acts as an intermediary layer between the random number generator and stochastic model components. This intermediary manages the complexity of stream coordination by providing a standardized interface and coordination mechanism, allowing precise control without direct complex interactions between all components.
4Reliability
If the random number generator is reset for each new design point to ensure reproducibility, then statistical validity is maintained, but the computational overhead increases
Solution Approach 1:
The reset operation is applied locally and selectively rather than globally. Only the specific stream or substream associated with a new design point is reset, while other streams continue without interruption. This localized approach maintains reproducibility for each design point while minimizing computational overhead by avoiding unnecessary resets of unrelated streams.
Data Source
AI summary
Computer-implemented systems and methods for executing stochastic discrete event simulations for a stochastic model through a design of experiments approach. Streams of random numbers are associated with stochastic model components that are sources of random variation within the stochastic model. A system is configured to manage the streams while the stochastic model is being executed during a simulation.