An analytical approach for column generation for One-Dimensional cutting stock problem

Abstract

One Dimensional cutting stock problem finds a wide applicability in many domains like textile, paper & pulp, wooden manufacturing industries. In this paper, we propose an analytical approach to solve the problem. Our approach reduces the total number of generated cutting pattern, and achieves a minimum trim loss. The analytical approach makes use of two approaches namely placement strategy and greedy approach. The placement strategy is to place each requirement at a time to meet all the required demands. The greedy approach is used to select appropriate requirement in order to minimize the total trim loss. The approach is evaluated on randomly generated data, the result obtained are comparable in terms of execution time, number of iterations and number of generated cutting patterns with the already existing approach. Copyright 2012 ACM.