Research On Multi-cycle Cutting Algorithm Of Circular Items With Leftover Strips Based On T-shape Pattern

The cutting stock problem of two-dimensional circular items has a wide range of industrial application background,which generally exists in glass processing,metal processing,clothing manufacturing and other manufacturing industries.Among them,in the motor manufacturing and other industries,the orders produced by enterprises have the characteristics of cycle continuity,less types of items and large demand.To solve this special problem,it can be considered to allow active generation of leftover materials for subsequent orders to reduce the cost of materials for the entire multi-cycle orders.For solving the problem,this thesis uses T-Shape pattern to form the cutting plan.Based on the management strategy of the stock of the standard leftover materials,the effect of considering the leftover materials in different positions of rectangular plates is studied.The sequential value correction method and adaptive genetic algorithm are mixed to solve the problem of multi-cycle circular items with leftover materials.The following is a summary of the main research work of this thesis:(1)The Sequential Value Correction(SVC)algorithm of multi-cycle cutting stock problem of circular items based on T-Shape pattern with considering leftovers: Firstly,the concepts of T-Shape pattern is introduced,and it is proposed that T-Shape pattern can be regenerated after the standard leftover strips are generated on the upper edge or the right edge of the sheet.Because there are many breakpoints in T-Shape pattern and many kinds of standard leftover strips need to be considered,a multi-thread method is adopted to speed up the generation of patterns.Compared with the T-Shape patterns with different positions of leftover strips and with other literatures,the algorithm proved to be effective.(2)Multi-cycle cutting algorithm of circular items based on SVC and adaptive genetic algorithm with considering leftover strips: A dynamic hybrid strategy was used to control the execution of SVC and adaptive genetic algorithm.Firstly,the initial population of the adaptive genetic algorithm is generated by the SVC method,and then it is solved by the adaptive genetic algorithm.Finally,the dynamic hybrid strategy alternately executes the SVC method and the adaptive genetic algorithm.When the iteration stop condition is reached,the algorithm will be terminated and then final solution will be output.In the process of implementing the adaptive genetic algorithm,the dynamic evaluation strategy is introduced to determine the evolution state of the genetic algorithm,and then decide whether to jump out of the genetic algorithm and turn to the SVC algorithm for local optimization search.Compared with other algorithms and literature,the algorithm can reduce the cutting cost.(3)Adopt Java language to realize the system prototype of multi-cycle cutting stock of circular items with considering leftover strips optimization algorithm,and the experimental comparison test and analysis of the above two algorithms are carried out.Experimental results show that the proposed algorithm can effectively reduce the cost of cutting materials,improve the efficiency of enterprises,and guide the actual manufacturing process of enterprises by generating layout patterns.