A genetic algorithm based solution for the online cutting problem with defects

Abstract

In flat glass production, glass products of various sizes and quality classes are cut from a continuously moving glass ribbon. If a product is placed on a part of the glass ribbon that has more defects than allowed by that product's quality class, it is discarded as scrap. Scrap glass is sent back to the furnace to be recycled. This process creates additional production costs and reduces the productivity of the line. Therefore, the main objective of the cutting process is the minimization of scrap glass. In this study, we propose an online solution approach that uses a look-ahead mechanism to make cutting decisions in real time. This approach solves a series of static cutting problems over a rolling horizon using a genetic algorithm. The proposed approach has been tested successfully on realistic problem instances that are based on parameters of contemporary glass production lines.

Simulated Annealing Algorithm and Implementation Software for Fabric Cutting Problem

“…In the study of Afsharian et al [9], these problem variants are called as "more specialized, less standardized" because of considering large defective objects. For the glass cutting problem, which can be classified in this category, the researchers developed a dynamic programming algorithm [17], mixed integer programming based algorithm [18], and genetic algorithm based solution [19]. The paper of Rönnqvist [20] provided an overview of wood flow in the forestry industry and some optimization models, including cutting problems.…”