Website Optimization Design to Cut Stock Problems in 1 and 2 Dimensions with Genetic Algorithms

Abstract

Many industries produce waste that cannot be reused, especially in the process of cutting raw materials. The use of raw materials that are not optimal indicates that the pattern of item placement on raw materials produces waste which cannot be used for other needs. This problem is known as cutting stock problem. This study aims to reduce the waste that arises when cutting raw material sheets into small pieces needed in the industry by developing a website as a tool in completing the placement of cutting patterns on raw materials. The developed website describes the results of optimal placement patterns in the form of images for 1 and 2 dimensions with the genetic algorithm method as an optimization algorithm using rectangle packing sequence in codification, cornerset modification algorithm on decoding, parameterized uniform crossover on crossover, roulette wheel on selection, and random generated mutations. Website development is made with hypertext markup language to display various information needed, cascading style sheets to arrange components in structured and uniform websites, and javascript to create genetic algorithm logic. This research resulted in the placement of cutting patterns on sheets of raw material on the canvas by labeling the names of each item.