外文翻译运用紧凑相邻法则对非规则零件图样进行大规模编排.docVIP

设计巴巴工作室 Large-scale nesting of irregular patterns using compact neighborhood algorithm S.K. Cheng, K.P. Rao* The typical nesting technique that is widely used is the geometrical tilting of a single pattern or selected cluster step by step from the original position to an orientation of 1808, i.e. orthogonal packing. However, this is a blind search of best stock layout and, geometrically, it becomes inef?cient when several pattern entities are involved. Also, it is not highly suitable for handling patterns with a range of orientation constraints. In this paper, an algorithm is proposed which combines the compact neighborhood algorithm (CNA) with the genetic algorithm (GA) to optimize large-scale nesting processes with the consideration of multiple orientation constraints. # 2000 Elsevier Science S.A. All rights reserved. Keywords: Cutting stock problem; Nesting; Compact neighborhood algorithm; Genetic algorithm; Orientation constraints 1. Introduction The cutting stock problem is of interest to many industries like garment, paper, ship building, and sheet metal indus- tries. Gilmore and Gomory [7] have initiated the research work to solve the rectangular cutting stock problem by using linear programming. For the irregular case, Adamowicz [1] attempted to use a heuristic approach which divides the problem into two sub-problems, called clustering and nest- ing. Clustering is to specify a collection of patterns that ?t well together before nesting onto a given stock. Nesting of patterns or clusters can be broadly divided into two broad categories, namely, small-scale and large-scale. The differ- ence between them is the level of duplication of the cluster on the given stock. For small-scale nesting, we only need to ?nd the inter-orientation relationship between the selected cluster and the given stock [4]. However, the problem becomes more complicated for large-scale nesting since the inter-space relationship between the duplicated clusters should also be considered. T