Bottleneck Capacity Scheduling for Balanced Parallel Production
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Solution Overview
Problem
Existing production scheduling methods fail to optimize the capacity of bottleneck apparatuses, leading to unbalanced load rates and underutilization of low-load rate apparatuses, especially in parallel production environments, resulting in inefficiencies and increased need for subcontracting or outsourcing.
Innovation Solution
A method and device for optimizing production scheduling by identifying bottleneck apparatuses with excessive load rates, reallocating spare working hours to other apparatuses with lower load rates, and utilizing deep learning to balance load rates and maximize capacity utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing production scheduling methods are used, then production tasks are arranged according to master production schedule, but bottleneck apparatus load rates become unbalanced and capacity is underutilized
Solution Approach 1:
The patent segments bottleneck apparatuses into different clusters based on their load rates and production relationships. High-load bottleneck apparatuses are separated from low-load ones, allowing independent optimization strategies to be applied to each segment. This segmentation enables targeted capacity allocation without requiring complete reconfiguration of the entire production system.
Solution Approach 2:
The patent implements dynamic production scheduling that adjusts work hours and task allocation in real-time based on current bottleneck apparatus load rates. The system continuously monitors load rates and dynamically reallocates production tasks from high-load to low-load apparatuses, creating a flexible responsive scheduling system that adapts to changing production conditions.
2Productivity
If bottleneck apparatus load rate is increased to meet production demand, then production output improves, but load rate becomes excessively high and balance deteriorates
Solution Approach 1:
The patent establishes a feedback mechanism that continuously monitors bottleneck apparatus load rates and uses this information to adjust production scheduling decisions. When load rates exceed optimal thresholds, the system automatically triggers reallocation of production tasks to maintain balance. This closed-loop feedback ensures both high output and reliable load rate balance are achieved simultaneously.
Solution Approach 2:
The patent changes key parameters such as work hours, task allocation ratios, and production schedules based on real-time load rate measurements. By dynamically adjusting these parameters, the system optimizes the balance between production output and load rate stability, preventing both excessive overload and underutilization of bottleneck apparatuses.
3Productivity
If production schedule is optimized to balance load rates, then capacity utilization improves, but scheduling complexity and computational requirements increase
Solution Approach 1:
The patent applies different optimization strategies to different local contexts within the production system. High-load bottleneck apparatuses receive one type of optimization treatment while low-load apparatuses receive another. This localized approach allows complex optimization to be applied only where necessary, reducing overall system complexity while maintaining high capacity utilization.
4Reliability
If spare working hours of bottleneck apparatus are reallocated to other apparatuses, then load rate balance improves, but coordination and management complexity increases
Solution Approach 1:
The patent creates a universal coordination mechanism that handles multiple functions: monitoring load rates, calculating optimal reallocation, executing task transfers, and verifying results. This multi-functional system reduces the need for separate specialized systems for each coordination task, simplifying overall management complexity while achieving improved load rate balance through spare working hour reallocation.
Data Source
AI summary
The provided are a method and device for optimizing production scheduling, and a medium. The method includes: determining an apparatus that has a highest apparatus load rate as a bottleneck apparatus to be adjusted, and obtaining a plurality of producible product models corresponding to the bottleneck apparatus to be adjusted and a plurality of bottleneck apparatuses to be allocated that perform co-production with the bottleneck apparatus to be adjusted; determining an apparatus cluster and a product cluster corresponding to the bottleneck apparatus to be adjusted; and obtaining a spare working hour of each bottleneck apparatus to be adjusted and a total surplus working hour thereof, determining an adjustment rate of the bottleneck apparatus to be adjusted according to the spare working hour, and allocating working hours of the producible product models based on the adjustment rate and the total surplus working hour.