Print Production Scheduler Using Evolutionary Containers
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Solution Overview
Problem
Commercial print shops face challenges in achieving optimal efficiency and effectiveness in production scheduling due to high demand variability, short response times, high fault rates, and increasing customization, which traditional human management and automated rule-based systems are unable to cope with.
Innovation Solution
A production scheduler that uses a population-based, meta-heuristic optimization scheme to sort print requests into containers based on attributes like due date and other factors, employing evolutionary-based computation to generate a globally optimized priority list, and incorporates real-time feedback from the production facility to dynamically adjust scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional human management or rule-based automated systems are used for production scheduling, then the system is simple to implement and operate, but it cannot cope with high demand variability, short response times, and high fault rates, resulting in suboptimal efficiency and effectiveness
Solution Approach 1:
The patent implements a dynamic scheduling system that continuously adapts to changing conditions. The system uses real-time feedback from the production facility and employs evolutionary optimization algorithms that iteratively improve schedules based on current demand variability, response time requirements, and fault rates. This dynamic approach allows the system to handle high variability and short response times while maintaining optimized productivity.
Solution Approach 2:
The system incorporates real-time feedback loops where production status, machine performance, and job completion data are continuously monitored and fed back to the scheduling algorithm. This feedback mechanism enables the system to adjust schedules dynamically in response to high fault rates and changing demand, resolving the contradiction by making the system adaptive rather than static.
2Productivity
If a population-based meta-heuristic optimization scheme is implemented to achieve globally optimized scheduling, then production efficiency and throughput are maximized, but the computational complexity and processing requirements increase significantly
Solution Approach 1:
The patent segments the population-based optimization into multiple independent containers or sub-populations that evolve in parallel. Each container handles a portion of the scheduling problem, allowing the computational workload to be distributed across multiple processing units. This segmentation maintains high throughput optimization while reducing the computational complexity burden on any single system component.
Solution Approach 2:
The system applies partial optimization by focusing computational resources on the most critical scheduling decisions and high-priority jobs. Rather than optimizing every aspect of every job equally, the meta-heuristic algorithm concentrates computational effort where it has the greatest impact on throughput, achieving near-optimal results with reduced computational complexity.
3Adaptability or versatility
If the system processes a large volume of diverse print requests with short response times, then customer service quality improves, but the scheduling system becomes overwhelmed and cannot maintain optimal efficiency
Solution Approach 1:
The patent applies local quality by treating different job types, priorities, and characteristics with specialized handling rules within the optimization framework. Each job container can have customized evaluation criteria and constraints based on its specific requirements, allowing the system to adapt to diverse job types while maintaining overall throughput optimization through the coordinated evolution of multiple containers.
Data Source
Figure 1~2A
Figure 2B~2D
Figure 3A~4A
AI summary
A print production system includes a dispatcher and a task-resource scheduler. The dispatcher sorts print requests for placement among a series of containers to identify relative priorities among all print requests in each container and then merges the containers together to produce a prioritized list of print requests among all containers. Upon release by the dispatcher of a top N print requests from the prioritized list, the scheduler converts the prioritized list into a task- resource schedule for print production.