A Hybrid Multi-chromosome Genetic Algorithm for the Cutting Stock Problem

This paper presents a hybrid multi-chromosome genetic algorithm (HMCGA) to solve an in integer linear programming formulation of the Cutting Stock Problem (CSP). The CSP is an important class combinatorial problem. It is appropriate to minimize the raw material used by industries for fulfilling customer's demands. In such cases, classic models for solving the cutting stock problem are useless. HMCGA differ from previous application of genetic algorithm to CSP in that our coding contain tow chromosome, one represents the cutting pattern and another represents the frequencies of the cutting patterns in the first chromosome, rather than relying on a traditional genetic algorithm to decode each individual solution. Results obtained from computational experiments for ten benchmarks demonstrate that the performance of HMCGA is compared to that obtained using existing mete-heuristic algorithm.