A Comparison of Cuckoo Search Approach and One-Dimensional Approach for Cutting-Stock Optimization

Cutting-Stock Problem has been commonly used in the Cuckoo Search Approach and One-Dimensional Approach in industry. A linear programming methodology uses these two methods. In the case of cutting paper sheets, wood, scrap, and many other industries, cutting-stock issues occur. This initiative relates to the real issue of cutting the sheets of paper, wood, and waste in the factory manufacturing line. To overcome the losses they are now facing, actual evidence will be gathered and measured by reducing the percentage dependent on each case analysis of the existing content used. The research focuses on discovering suitable cutting patterns for One-Dimensional cutting stock using the simplex algorithm. This can solve sub-problems in the One-Dimensional which to decide the entering column (pattern) by using linear programming to overcome it. The sub-problem is a type of knapsack, and by using linear programming to overcome it. To discover optimum cutting patterns, a computer programming method is created. Using MATLAB, realcut 1d, and GoNest 1D, a pattern generation algorithm was created and coded. This paper also generates and codes using MATLAB for Cuckoo Search to solve the problem of exposed material cutting as an alternative approach to the genetic algorithm, which would also use linear programming to solve the output of the Cuckoo Search algorithm. Result shows the One-Dimensional reached approximately 398 mm, and a waste that can be minimized by Cuckoo Search is 626 mm. In a nutshell, Cuckoo Search is better than One-Dimensional in this case study.