David Creyke Reedman

Genetic Algorithm Coding Methods for Leather Nesting

The problem of placing a number of specific shapes in order to minimise waste is commonly encountered in the sheet metal, clothing and shoe-making industries. The paper presents genetic algorithm coding methodologies for the leather nesting problem which involves cutting shoe upper components from hides so as to maximise material utilisation. Algorithmic methods for computer-aided nesting can be either packing or connectivity driven. The paper discusses approaches to how both types of method can be realised using a local placement strategy whereby one shape at a time is placed on the surface. In each case the underlying coding method is based on the use of the no-fit polygon (NFP) that allows the genetic algorithm to evolve non-overlapping configurations. The packing approach requires that a local space utilisation measure is developed. The connectivity approach is based on an adaptive graph method. Coding techniques for dealing with some of the more intractable aspects of the leather nesting problem such as directionality constraints and surface grading quality constraints are also discussed. The benefits and drawbacks of the two approaches are presented.

Genetic algorithms applied to leather lay plan material utilization

Abstract This paper presents a genetic algorithm method for the leather-nesting problem that involves cutting shoe upper components from hides so as to maximize material utilization. A significant proportion of the manufacturing cost of a pair of shoes is invested in the natural raw material, and so the efficient utilization of this resource is of prime importance. Consequently, the part nesting and cutting process is one of the most important stages in the manufacture of leather shoes. The genetic algorithm method presented for leather lay-planning is capable of handling some of the more intractable aspects of the problem, namely multiple non-convex shapes, irregularly shaped hides, directionality constraints and surface grading quality issues. The underlying encoding method is based on the use of the no-fit polygon (NFP), lay angles and directionality angle constraints. The NFP allows the genetic algorithm to evolve non-overlapping configurations. Lay-plan results are presented using standard shoe component shapes and scanned hide input data conforming to a grading scale commonly used in shoemaking.

Genetic Algorithm Optimisation of Part Placement Using a Connection-Based Coding Method

The problem of placing a number of specific shapes in order to minimise material waste is commonly encountered in the sheet metal, clothing and shoe-making industries. It is driven by the demand to find a layout of non-overlapping parts in a set area in order to maximise material utilisation. A corresponding problem is one of compaction, which is to minimise the area that a set number of shapes can be placed without overlapping. This paper presents a novel connectivity based approach to leather part compaction using the no-fit polygon (NFP). The NFP is computed using an image processing method as the boundary of the Minkowski sum, which is the convolution between two shapes at given orientations. These orientations along with shape order and placement selection constitute the chromosome structure.

Genetic Algorithms Applied to Material Utilisation in Leather Shoe Manufacturing

A significant proportion of the manufacturing cost of a pair of shoes is invested in the natural raw material and so the efficient utilisation of this resource is of prime importance. Consequently, the part nesting and cutting process is one of the most important stages in the manufacture of leather shoes. This paper presents a genetic algorithm based nesting strategy for optimising the placement of shoe component parts on a leather hide. It is capable of handling some of the more intractable aspects of the problem, namely multiple nonconvex shapes, irregularly shaped hides, directionality constraints and surface grading quality issues. The underlying coding method is based on the use of the nofit polygon that allows the genetic algorithm to evolve non-overlapping configurations. Lay-plan results are presented using standard shoe component shapes and scanned hide input data conforming to a grading scale commonly used in shoemaking.