A hybrid metaheuristic for the 2D orthogonal Cutting Stock Problem

A hybrid metaheuristic (HMH) algorithm is developed to solve the 1D and 2D multi-objective orthogonal single stock size cutting stock problem (SSSCSP). The objectives are to minimize the waste and the types of cutting patterns. The hybrid metaheuristic integrates greedy algorithm with genetic algorithms. Unlike traditional genetic algorithm (GA)-based approaches that optimize the whole cutting plan at a time, the greedy algorithm is applied and GA is used for getting one cutting pattern at each step. And the obtained optimal cutting pattern is reused as many times as possible. HMH achieves a good balance between result quality and optimization efficiency. HMH is tested on real life large scale applications. The final high utilization ratio of more than 97% validates the effectiveness of the proposed algorithm. HMH can be implemented easily and runs very fast. It is widely applicable and especially suitable for 1D and 2D cutting stock problems.