Multi-dimensional Resource Optimization for 2D Element Manufacturing
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Solution Overview
Problem
Manufacturing plants face inefficiencies in resource optimization due to the lack of automated methods for balancing competing factors such as raw material use, machinery, and labor, leading to suboptimal production plans and increased waste, particularly when dealing with varying raw material sizes and inventory changes.
Innovation Solution
A computer-based method and system that combines customer order information with product design and resource data to automate the production planning process, optimizing resource allocation and scheduling by computing optimal nests and resource usage, while adjusting rules and objectives to ensure efficient production plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single-unit nest is stored and reused for multiple orders, then human and hardware resources for computing nests are conserved, but raw material is wasted by repeating the waste inherent to the single-unit nest
Solution Approach 1:
The patent merges multiple nest computations into a single integrated optimization problem. Instead of computing separate nests for each order quantity, the system combines all orders and computes one optimal nest arrangement that serves all orders simultaneously, thereby eliminating redundant material waste while maintaining computational efficiency
Solution Approach 2:
The patent introduces dynamic nest computation based on actual order quantities rather than static pre-computed nests. The system dynamically adjusts nest configurations according to real-time order data, allowing optimal material utilization for each specific production requirement rather than relying on fixed templates
2Loss of substance
If multiple separate nests are computed for different orders, then raw material use is optimized for each order, but human and software resources are consumed by redesigning nests
Solution Approach 1:
The patent consolidates multiple nest computation tasks into a single integrated optimization problem. By merging all orders and computing one comprehensive nest arrangement, the system reduces total computation time while achieving optimal material utilization across all orders simultaneously
Solution Approach 2:
The patent performs preliminary nest computation for combined orders rather than computing nests on-demand for each individual order. This pre-computation approach eliminates repeated calculations and reduces overall processing time while maintaining optimization quality
3Loss of substance
If nests are redesigned to accommodate varying raw material sizes and inventory changes, then material utilization is optimized, but resources are consumed by determining the best way to redesign nests
Solution Approach 1:
The patent implements parameter changes in the nest optimization algorithm to adapt to varying raw material dimensions and inventory conditions. The system automatically adjusts nest configurations based on actual material parameters, eliminating the need for manual redesign while optimizing material utilization for each specific scenario
Solution Approach 2:
The patent enables the nest computation system to self-adjust and self-optimize based on varying input conditions. The algorithm automatically adapts to different raw material sizes and inventory states without requiring human intervention or manual redesign, reducing resource consumption while maintaining optimization quality
Data Source
AI summary
The present invention enables the efficient use of resources in the manufacture of substantially two-dimensional elements through multi-dimensional resource optimization subject to rules and objectives. The invention may be embodied as a computer-based method, a computerized system that performs the method, or a machine readable storage medium containing instructions that when executed cause performance of the method using the computerized system. To fulfill customer orders, product design data, resource data, and rules and objective data are accessed to determine the efficient use of the resources, such as consumption of particular units of raw material and utilization of production stations. For a given set of customer orders, multiple scenarios of production plans are computed and recomputed until an efficient production plan is achieved. The production plan can be generated at a location remote from the manufacturing site, and it can then be sent in an automated fashion to the production stations.