Defective Sheet Cutting Layout for Faster Guillotine Optimization
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Solution Overview
Problem
Current methods for cutting stock optimization of defective rectangular sheets, such as those with knots, air bubbles, or contaminated areas, face inefficiencies due to long solution processes and low convergence speeds, especially under guillotine cutting constraints, which hinder material utilization and production efficiency.
Innovation Solution
A cutting stock method that differentiates between non-defective and defective sheets using specific cutting position discrete sets calculated by tailored algorithms, allowing for efficient division into target blocks while avoiding defects, thereby reducing the search space and enhancing convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulated annealing algorithm is used to solve cutting stock problem with defective sheets, then global optimal solution can be obtained, but solution process is long and solution efficiency is low
Solution Approach 1:
The patent segments the search space by dividing the rectangular sheet into multiple regions based on defect locations. It creates a discrete set of cutting positions that are strategically selected to separate defective areas from usable areas. This segmentation reduces the continuous search space into manageable discrete segments, allowing the algorithm to find optimal solutions faster without sacrificing completeness.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating the discrete set of cutting positions based on defect locations and sheet dimensions before the main optimization process. This preliminary discretization of the cutting positions reduces the complexity of the search space and enables more efficient exploration of potential cutting patterns, thereby reducing overall solution time while maintaining optimality.
2Adaptability or versatility
If genetic algorithm is used to solve cutting stock problem with defective sheets, then global search capability is good, but local search capability is poor and convergence speed is slow
Solution Approach 1:
The patent segments the search space into discrete cutting positions based on defect locations. This creates a structured search space where the genetic algorithm can operate more efficiently. The discrete segmentation provides clear boundaries for potential solutions, enabling better local search while maintaining global exploration capability, thus improving convergence speed.
Solution Approach 2:
The patent changes the parameter representation from continuous cutting positions to discrete cutting positions derived from defect locations. This parameter transformation simplifies the search space structure, making it more suitable for genetic algorithms. The discrete parameters enable faster convergence by reducing the complexity of evaluating and mutating potential solutions, while still maintaining the ability to explore the global search space effectively.
3Productivity
If heuristic algorithm is used to solve cutting stock problem with defective sheets, then feasible solution can be obtained within certain period, but solution is unstable and cannot be judged to be optimal
Solution Approach 1:
The patent performs preliminary discretization of the cutting space based on defect locations before applying the optimization algorithm. This preliminary action creates a structured search space that guides the heuristic algorithm toward optimal solutions. By pre-organizing the search space according to defect constraints, the algorithm can achieve both fast solution obtaining and reliable optimality verification.
Data Source
AI summary
A cutting stock method for a rectangular sheet with defects, including: acquiring information of the rectangular defective sheet, including dimension information of the rectangular sheet and target blocks, and location information of the defects; acquiring a cutting position discrete set of the rectangular sheet; cutting the rectangular sheet according to the cutting position discrete set into a plurality of target blocks; calculating a sum of values of the target blocks, and selecting an optimal cutting solution with the largest sum of values; and cutting the rectangular sheet according to the optimal cutting solution.