Corrugator Dry End Schedule Optimization Sorting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing scheduling systems for corrugators face challenges in minimizing short order recovery time at the dry end, which affects the efficiency of order changes and equipment utilization due to limitations in repositioning slit and score heads on the slitter/scorer, particularly with the advent of just-in-time ordering and customized packaging demands.
Innovation Solution
The method of schedule optimization sorting (SOS) identifies short orders and reorganizes the paper group queue to bracket them with orders that have similar tooling requirements, minimizing the recovery time by relocating slit and score heads directly from their current positions to the next required positions, rather than requiring them to be swept to a home position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional scheduling systems are used for corrugator dry end orders, then orders are processed in standard sequence, but recovery time for short orders increases due to unnecessary tool head movements
Solution Approach 1:
The system performs preliminary analysis of the order queue to identify short orders and pre-determine optimal bracketing pairs before production begins. By calculating recovery times and identifying suitable preceding and following orders in advance, the system prepares the scheduling optimization strategy beforehand, allowing immediate implementation when short orders are encountered without real-time decision delays.
Solution Approach 2:
The system dynamically changes the scheduling parameter (order sequence) based on order characteristics. By reorganizing the paper group queue to place identified bracketing pairs around short orders, the system transforms the fixed standard sequence into an optimized variable sequence that minimizes tool head movement distance and recovery time for each specific production scenario.
2Loss of time
If slit and score heads are repositioned directly from current positions to next required positions, then recovery time is minimized, but the scheduling system complexity increases
Solution Approach 1:
The scheduling system performs self-analysis by automatically evaluating its own order queue characteristics, calculating recovery times for different order sequences, and identifying optimal bracketing pairs without external intervention. The system serves itself by generating the optimized schedule based on its own operational data and constraints, reducing the need for complex external scheduling infrastructure.
Solution Approach 2:
The system uses feedback from actual tool head positions and order specifications to continuously optimize the scheduling algorithm. By monitoring the current state of slit and score heads and comparing it with required positions for upcoming orders, the system adjusts the order sequence to minimize movements, creating a closed-loop optimization process that adapts to real-time conditions.
3Productivity
If orders are reorganized to bracket short orders with similar tooling requirements, then equipment utilization improves, but the ease of operation decreases due to dynamic scheduling adjustments
Solution Approach 1:
The system replaces manual scheduling operations with an automated computer-based scheduling algorithm. Instead of operators manually analyzing order queues and creating optimized sequences, the system automatically calculates recovery times, identifies short orders, determines bracketing pairs, and generates optimized schedules, substituting mechanical human operations with automated computational processes.
Solution Approach 2:
The scheduling system performs multiple functions within a single integrated platform: it analyzes order specifications, calculates recovery times, identifies short orders, determines optimal bracketing pairs, and generates reorganized schedules. This multi-functional approach consolidates what would otherwise require separate manual processes into one universal scheduling solution that handles all optimization tasks.
Data Source
AI summary
A method and a non-transient computer readable medium for sorting orders to be run on a multi-section slitter/scorer at a corrugator dry end includes collecting specifications of orders in current, prior, and/or subsequent paper group queues. Run times and slit/score head recovery times for orders in the current paper group queue are calculated using the specifications. Those orders for which recovery time exceeds run time are flagged as short orders. For a given short order, pairs of preceding and following orders that would require recovery times less than the short order's run time are determined and flagged as potential bracketing pairs. The current paper group queue is searched to determine if a bracketing pair can be formed from its orders, and if so, the orders in the current paper group queue are reorganized such that the orders in the bracketing pair immediately precede and follow the given short order.