Parallel Simulation Control for Production Plan Optimization
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Solution Overview
Problem
Conventional discrete simulation methods face challenges in efficiently optimizing operational plans for small-volume production in diverse varieties, requiring high-performance hardware resources and being costly, especially when dealing with a large number of cases, and struggle to guarantee optimal solutions under all conditions.
Innovation Solution
A production plan optimization device and method utilizing a parallel simulation operation mechanism with an integrated control processing unit, multiple simulators, and a refinement control component, which sets and adjusts simulation conditions to efficiently utilize hardware resources, recognize optimal operations, and reduce costs by generating simulation conditions based on operation state information and refinement conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional discrete simulation is used to optimize operational plans for small-volume production in diverse varieties, then simulation accuracy is maintained, but hardware resource costs increase and processing speed decreases when dealing with an enormous number of cases
Solution Approach 1:
The patent divides the enormous number of simulation cases into multiple groups that can be processed in parallel. The simulation system is segmented into multiple processing units that simultaneously evaluate different production plans, thereby maintaining accuracy while significantly improving processing speed and reducing hardware resource costs.
Solution Approach 2:
The patent performs preliminary filtering and evaluation of simulation conditions before executing full simulations. By pre-processing and eliminating obviously suboptimal plans, the system reduces the number of cases requiring full simulation evaluation, thus maintaining accuracy while improving processing efficiency.
2Measurement precision
If conventional discrete simulation is used to optimize operational plans for small-volume production in diverse varieties, then simulation accuracy is maintained, but hardware resource costs increase
Solution Approach 1:
The patent segments the simulation workload into multiple parallel processing tasks that can be distributed across available hardware resources. This segmentation allows the system to maintain simulation accuracy while utilizing existing hardware more efficiently, reducing the need for high-performance expensive hardware.
Solution Approach 2:
The patent uses simplified simulation models or approximations for preliminary evaluations, creating copies of the simulation process at different levels of detail. This allows rapid filtering of candidates using less resource-intensive models before applying full-accuracy simulations only to promising cases.
3Productivity
If scheduler is used to optimize production planning, then production optimization is attempted, but development costs incur and adaptability to changes decreases
Solution Approach 1:
The patent implements a dynamic simulation-based optimization system that can adapt to changes in production conditions, product mix, and facility configurations. Unlike static scheduler logics, the simulation model can be reconfigured to reflect actual production line changes, maintaining adaptability while providing optimization.
Solution Approach 2:
The patent uses simulation parameters to represent production conditions, allowing easy adjustment of product types, production volumes, and facility characteristics. By changing parameters rather than rewriting logic, the system adapts to production changes efficiently without requiring specialized scheduling knowledge.
4Productivity
If scheduler is used to optimize production planning, then production optimization is attempted, but development time and costs incur
Solution Approach 1:
The patent leverages existing production line data and simulation models that can be copied and reused for different optimization scenarios. Rather than developing custom scheduler logic for each production line, the system uses standardized simulation templates that can be quickly adapted, significantly reducing development time.
Solution Approach 2:
The patent replaces complex scheduler logic with a simulation-based approach that automatically evaluates production plans. This substitution eliminates the need for manual logic development and debugging, reducing development time while maintaining optimization capability.
Data Source
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AI summary
An operational plan optimization device 1 includes: an integrated control processing unit 2; and a plurality of simulators 3 communicably connected to and controlled integrally by the integrated control processing unit 2. The integrated control processing unit 2 is configured to: recognize a varying processable amount over time within a predetermined period of each of the simulators 3; transmit a simulation request for a simulation process within an optimal processing amount within the recognized processable amount of each of the simulators to each of the simulators 3 together with an operation state information group and a simulation condition; receive a plurality of evaluation value groups as a simulation process result based on the operation state information group and the simulation condition from each of the simulators 3; and recognize a highest evaluation value group based on an operation objective function among a plurality of received evaluation value groups. The operational plan optimization device 1 can perform various simulation operations at high speed at lower cost and recognize an optimal operation efficiently using hardware resources when performing operation simulation.