A Grouping Optimization Method for Solving Large-Scale Cutting Stock Problem Based on the Similarity of Parts

A new grouping optimization method based on the similarity of parts is proposed,which is intended to solve the low-efficiency and local optimization problems in large-scale cutting stock problem （LCSP）. By using HCM algorithm,all the parts are clustered at first. Then,the part-clusters are recombined into several part-groups by considering the computer hardware conditions and the optimization algorithm’s characteristics.The LCSP is decomposed into several smal-l scale cutting stock problem （SCSP）. After that,all the SCSPs are solved separately.For every pair of two adjacent SCSPs,a compensation strategy is adopted to adjust parts in different groups.Eventually,the result of the LCSP is obtained by combining all the results of the SCSPs.Compared with the general optimization algorithms,the proposed method is highly effective both in time-efficiency and utilization ratio of materials.