Cutting stock problem: A survey of evolutionary computing based solution

The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting of the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. The objective of this paper is to present a comparison of various meta-heuristic techniques for solving cutting stock problem based on accuracy and speed of convergence. It also focuses on new chromosome representation using co-operative co-evolutionary Genetic Algorithm (CCEGA) for multiple stock size cutting stock problem (MSSCSP). The main challenge in solving cutting stock problem is to develop chromosome representation for MSSCSP in GA. Moreover, this paper also presents detailed study of the existing chromosome representations for CSP problem with their loopholes and summary of the referred papers based on meta-heuristic techniques. To overcome these loopholes, we finally present the cutting pattern based chromosome representation using co-evolutionary GA.