Optimization Configurator Templates for Rapid Simulation Decisions
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Solution Overview
Problem
Existing simulation-based optimization methods for supply chain management require significant time and effort to develop scripts for multiple optimization algorithms across various simulators, especially when managing multiple real-world systems, due to the need for manual configuration and evaluation.
Innovation Solution
A simulation-based optimization configurator that automates the process of selecting and evaluating optimization algorithms by generating an optimization template with user-defined configurations, including simulators, objective functions, variables, and constraints, and performing automated optimization to determine optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual script development is used for each optimization algorithm and simulator combination, then customization and control are improved, but time consumption and effort increase significantly
Solution Approach 1:
The patent creates a reusable template that captures the structure and logic of optimization scripts. This template can be copied and instantiated multiple times for different optimization algorithms and simulators, eliminating the need to manually write scripts from scratch each time. The template includes predefined configurations, variable declarations, and optimization logic that can be automatically instantiated.
Solution Approach 2:
The system enables self-service by allowing users to define optimization configurations through a simplified interface rather than requiring manual script writing. The configurator automatically generates the necessary scripts and configurations based on user selections, making the system serve itself by automating the script generation process.
2Measurement precision
If multiple optimization algorithms are evaluated across multiple simulators, then optimal solution quality is improved, but computational complexity and time requirements increase
Solution Approach 1:
The patent segments the evaluation process into independent, modular components. Each optimization algorithm can be evaluated separately against each simulator through standardized interfaces. This segmentation allows the complex multi-algorithm, multi-simulator evaluation to be broken down into manageable units that can be executed independently and whose results are then synthesized to determine the optimal configuration.
Solution Approach 2:
The patent creates a universal evaluation framework that can handle multiple optimization algorithms and simulators through a common interface. This universal configurator design allows the same evaluation infrastructure to work with different algorithms (e.g., genetic algorithms, simulated annealing) and different simulators without requiring separate custom evaluation systems for each combination.
3Reliability
If comprehensive optimization configurations are created for multiple systems, then solution optimality is improved, but configuration management complexity increases
Solution Approach 1:
The patent introduces an intermediary configurator layer that sits between the user and the complex optimization systems. This intermediary automatically manages the configurations, translating high-level user requirements into detailed optimization settings. It handles the complexity of coordinating multiple algorithms, simulators, and parameters by serving as a mediator that automates configuration generation and management.
Solution Approach 2:
The system performs preliminary actions by pre-defining optimization configurations, variable relationships, and constraint structures in templates. This preliminary setup work is done once and can be reused across multiple optimization evaluations, eliminating the need to manually configure each optimization problem from scratch and reducing the complexity of managing comprehensive configurations.
Data Source
AI summary
Conventional simulation-based optimization, even when automated, requires substantial user-involved development time. Accordingly, embodiments are disclosed to automate various aspects of simulation-based optimization. In particular, an optimization configurator and associated data structures are disclosed for generating and running optimization templates that can be easily constructed (e.g., via lists of available components), revised, evaluated, and re-run as needed. The optimization templates may comprise a plurality of optimization configurations that each define and pair an optimization algorithm with a simulator of a real-world system. Embodiments can reduce development time, are applicable to various domains, can be used by novice users without specialized knowledge, and can improve the overall quality of optimization for the operations of real-world systems, such as supply chains.