Flat Sheet Trim Planning With Knapsack Loss Minimization
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Solution Overview
Problem
Current trimming algorithms in the flat sheet industry focus on minimizing the number of master rolls used to fulfill customer demands, leading to suboptimal solutions and practical unusability, as they do not effectively address trim loss and yield optimization.
Innovation Solution
A method that employs a Knapsack algorithm with an objective function minimizing trim loss, incorporating both upper and lower bound demand constraints, and using column generation to optimize trimming patterns for primary and secondary cutting processes, integrating with quality control systems to generate efficient cutting solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional trim algorithms focus on minimization of master rolls used to fulfill customer demands, then the number of master rolls is reduced, but trim loss increases and solutions become suboptimal
Solution Approach 1:
The patent changes the optimization parameter from minimizing master rolls to minimizing trim loss directly. The objective function is reformulated to calculate and minimize the total area of waste material generated during trimming operations, rather than simply counting the number of master rolls used. This parameter transformation leads to more economically optimal solutions.
Solution Approach 2:
The patent replaces traditional mechanical trimming optimization approaches with an AI-based system that uses machine learning models and advanced algorithms. The system incorporates quality control data, machine capabilities, and order requirements to generate optimized trimming patterns that minimize waste, substituting manual or rule-based methods with intelligent automation.
2Adaptability or versatility
If multiple trimming steps are used to handle different dimensions, then dimension flexibility is improved, but process complexity increases
Solution Approach 1:
The patent implements dynamic optimization that adapts trimming patterns based on real-time inputs including order requirements, machine capabilities, and quality constraints. The system dynamically adjusts cutting patterns, roll selections, and processing sequences to handle varying dimensions optimally in a single integrated process, reducing the need for multiple fixed-step procedures.
Solution Approach 2:
The patent creates a universal trimming optimization system that can handle multiple product types, dimensions, and quality requirements through a single integrated platform. The AI model is designed to accommodate various machine configurations and order specifications, providing a multi-functional solution that reduces process complexity while maintaining versatility.
3Loss of substance
If manual iterations are performed to achieve desired trim loss, then trim efficiency can be improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements a self-service optimization system where the AI algorithm automatically generates and refines trimming patterns without requiring manual intervention. The system self-adjusts based on quality control feedback and machine performance data, continuously optimizing trim loss while eliminating the time-consuming manual iteration process. The automated system performs what would traditionally require repeated manual calculations and adjustments.
Data Source
AI summary
Planner insight analytics identifies orders and sizes customers can execute to achieve business and operational efficiency. Generating optimal trimming patterns for trimming raw rolls and/or sheets of a flat sheet stock/customer orders includes: (a) receiving customer orders, primary and secondary machine specifications, warehouse inventory, and trade constraints; and (b) generating solutions for (i) order quantity fulfillment, (ii) a primary cutting pattern for the primary machine, (iii) a secondary cutting pattern for the secondary machine, and (iv) inventory details, wherein the solutions are generated with consideration of the initial trade constraints; (c) executing a batch and generating suggestions; (d) generating modified solutions for the parameters in step (b) using revised trade constraints derived from the suggestions generated that override the initial trade constraints in (c); and (e) operating a cutting apparatus. Trim optimization knapsack algorithm with objective function with term relating to trim loss which is eventually minimized is employed.