Continuous Production Scheduling via Machine Performance Indices
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Solution Overview
Problem
Current production planning scheduling methods in manufacturing and production fields face challenges in reflecting machine performance, leading to inefficiencies and differences between scheduled and actual task results due to the lack of consideration for multiple machines and their varying performance levels, resulting in non-continuous scheduling and local optimization outputs.
Innovation Solution
A multi-machine and performance-based continuous production planning global optimization scheduling method and device that includes data pre-processing to generate a list of performable machines, a scheduling module to set processing times based on machine performance, and a data post-processing module to store and visualize scheduling results, allowing for continuous scheduling that reflects previous results and machine performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scheduling methods are used without considering machine performance, then scheduling simplicity is maintained, but the accuracy between scheduled and actual task results deteriorates
Solution Approach 1:
The patent transforms machine performance from a qualitative concept to a quantifiable parameter by introducing performance indices (0-1 scale) that measure actual task completion versus scheduled task completion. This allows the scheduling system to incorporate performance data as a numerical parameter, improving accuracy while maintaining manageable complexity through standardized measurement.
Solution Approach 2:
The patent replaces traditional heuristic scheduling approaches with a performance-based optimization model that uses mathematical calculations and algorithms. The system substitutes rule-based scheduling with a computational approach that dynamically adjusts scheduling decisions based on quantified machine performance parameters.
2Productivity
If single-machine scheduling is used, then scheduling complexity is reduced, but productivity is limited due to inability to utilize multiple machines
Solution Approach 1:
The patent segments the scheduling problem into machine-specific sub-problems by evaluating each machine's performance independently through performance indices. This segmentation allows the system to handle multiple machines by breaking down the complex multi-machine scheduling into manageable single-machine evaluations that are then integrated, improving productivity while controlling complexity.
Solution Approach 2:
The patent creates a universal scheduling framework that can handle any number of machines through a common performance evaluation mechanism. The performance index system serves as a multi-functional tool that works across different machine types and scheduling scenarios, enabling the system to scale from single-machine to multi-machine environments without requiring fundamentally different approaches.
3Adaptability or versatility
If continuous scheduling is not implemented, then computational resources are conserved, but scheduling responsiveness to performance changes deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where machine performance is continuously monitored through performance indices, and this feedback is used to dynamically adjust scheduling decisions. The system calculates performance after each task completion and uses this information to optimize subsequent scheduling, enabling continuous adaptation without requiring constant full-system re-scheduling, thus balancing responsiveness with computational efficiency.
Solution Approach 2:
The patent performs preliminary performance evaluation and indexing before final scheduling decisions are made. By pre-calculating performance metrics and using them to guide scheduling choices, the system prepares in advance for upcoming scheduling decisions, reducing the need for intensive real-time computational analysis while maintaining high adaptability to performance changes.
4Manufacturing precision
If local optimization methods are used, then computational time is reduced, but scheduling quality deteriorates due to inability to achieve global optimization
Solution Approach 1:
The patent applies local quality optimization by focusing on machine-specific performance characteristics through individual performance indices for each machine. Rather than attempting to optimize all machines simultaneously through a single global model, the system evaluates and optimizes each machine's contribution to overall productivity based on its local performance data, then integrates these local optimizations to achieve global improvement.
Solution Approach 2:
The patent introduces a new dimension of performance indexing that transforms the scheduling problem from a purely temporal optimization to a multi-dimensional optimization incorporating performance quality. By adding the performance index dimension (separate from time), the system can evaluate scheduling decisions based on both time and performance quality, achieving better overall optimization without proportionally increasing computational time.
Data Source
AI summary
A multi-machine and performance based continuous production planning global optimization scheduling method and device are disclosed. The device for multi-machine and performance-based continuous production planning global optimization scheduling may include a data pre-processing module configured to generate a multi-machine list on performable multiple machines through pre-processing scheduling target data, a scheduling module configured to generate a scheduling result by setting a processing time for a machine related to a single machine list, wherein the single machine list is selected from the multi-machine list, and a data post-processing module configured to store the scheduling result in a database.