Discrete Event Simulation Control for Production Plan Optimization
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Solution Overview
Problem
Conventional discrete simulation methods are inefficient and costly for optimizing production plans in small-volume, high-variety production environments, requiring extensive hardware resources and complex logic development, and often fail to provide optimal solutions under all conditions.
Innovation Solution
An operational plan optimization device with an integrated control processing unit that manages a network of simulators to recognize varying processing capacities, transmit simulation requests, and evaluate results to identify optimal operation conditions efficiently, utilizing parallel simulation operations and refinement conditions to reduce hardware costs and optimize production planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional discrete simulation methods are used to optimize production plans, then simulation accuracy is improved, but hardware resource requirements and costs increase significantly
Solution Approach 1:
The patent segments the simulation system into multiple independent simulators that can operate in parallel, each handling specific simulation tasks. This segmentation allows the system to distribute computational load across multiple units, reducing the hardware burden on any single device while maintaining overall simulation accuracy through coordinated operation of the segmented components.
2Measurement precision
If conventional discrete simulation methods are used to optimize production plans, then simulation accuracy is improved, but execution speed decreases due to complex logic development
Solution Approach 1:
The patent implements preliminary action by pre-defining simulation conditions, parameters, and logic structures that can be reused across multiple simulation runs. This preliminary setup reduces the need for complex logic development for each individual simulation, thereby maintaining high simulation accuracy while significantly improving execution speed through template-based and parameterized simulation approaches.
3Productivity
If scheduler with detailed logics is used to optimize production planning, then production optimization is improved, but development time and costs increase enormously
Solution Approach 1:
The patent uses copying by creating reusable simulation models and logic templates that can be replicated and adapted for different production scenarios. Instead of developing detailed scheduler logics from scratch for each optimization task, the system copies and modifies existing simulation frameworks, significantly reducing development time while maintaining production optimization capabilities through parameter adjustment rather than logic reconstruction.
4Adaptability or versatility
If conventional simulation methods are used with a large number of assumed cases, then solution comprehensiveness is improved, but hardware costs and processing requirements increase
Solution Approach 1:
The patent applies dimensionality change by transitioning from sequential simulation processing to parallel processing across multiple simulators operating simultaneously. This dimensional shift from one-dimensional sequential execution to multi-dimensional parallel execution enables the system to handle a large number of assumed cases comprehensively while distributing the computational burden, thereby maintaining solution comprehensiveness without proportionally increasing hardware costs.
Data Source
AI summary
The integrated control processing unit is configured to recognize a varying processable amount over time within a predetermined period of each of the simulators; 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 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; 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 can perform various simulation operations at high speed at lower cost and recognize an optimal operation efficiently using hardware resources when performing operation simulation.


