A Heuristic One-Dimensional Blanking Method Based on Hierarchical Random Search Algorithm

Abstract

The invention discloses a heuristic one-dimensional blanking method based on a stratified random search algorithm. The method includes: A, using a parameterized model to signify one-dimensional blanking problems; B, preprocessing billets for combination; C, obtaining various stock layout modes through the stratified random search algorithm combined by random search and deep search; D, picking out an optimal stock layout mode according to a heuristic rule; E, adding the optimal stock layout mode and a time which does not exceed the current needed maximum time of reusing the billets into a current stock layout scheme, and updating a billet set to be laid; F, repeating processes of C, D and E until the total length of the billets to be laid less than the length of raw materials, and outputting the current stock layout scheme; and G, repeating processes of B, C, D, E and F for many times, and then performing compared screening on all stock layout schemes so as to obtain an optimal stock layout scheme. The heuristic one-dimensional blanking method based on the stratified random search algorithm can avoid blindness of a traditional random search algorithm and is high in computation speed, and the obtained stock layout scheme adapts to practical production needs.

Description

technical field [0001] The invention belongs to the field of integer nonlinear programming, and in particular relates to a heuristic one-dimensional blanking method based on a layered random search algorithm. technical background [0002] In modern industrial production, including steel, building materials, paper rolls, films and other industries, there is a problem of how to arrange and blank materials, among which the one-dimensional blanking problem is the most common problem faced in production. The so-called one-dimensional blanking problem refers to how to optimize the cutting and blanking under the known order requirements and raw material data when the dimensions of the raw materials and the required materials are one-dimensional, so that the raw materials can be fully utilized and the cost can be saved as much as possible. planning issues. [0003] The problem of cutting and ordering is an NP problem, and it is generally believed that there is no method that can de...

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Problems solved by technology

[0005] (1) The generated solution is generally not an integer solution, and rounding optimization is required, but after rounding optimization, it is easy to cause the number of blanks generated by blanking to exceed the original demand;

[0006] (2) The generated solution contains a large number of layout methods, which makes it necessary to adjust the cutting machine multiple times in actual production;

[0007] (3) When there are many types of blanks required in the order, the calculation time of the algorithm increases sharply, making it difficult to adapt to actual needs

[0010] The Chinese patent application with the application number of 201110139183.1 "Optimized cutting method for intelligent sieve steel bars" adopts the traversal method when obtaining the combination of steel bar cuttings, which can be suitable when there are relatively few types of sample blanks, but when the types of sample blanks increase, the number of cutting combinations increases exponentially, obviously this method is not suitable for solving the problem of large-scale cutting

For the traditional heuristic random search algorithm, refer to "One-Dimensional Optimal Cutting Based on Gene Population" Journal of Shanghai Jiaotong University, Issue 6, 2006. The sum of the lengths of parts or blanks exceeds its length limit, that is, it is easy to have an unfavorable situation of infeasible solutions; when the scale of the material increases, because the random search domain is much larger than the feasible solution domain, the blindness of the traditional random search algorithm It will take a lot of computing time to initialize the feasible solution set, which is not conducive to the application in actual production

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