线性切割问题的遗传算法

R. V. Voronov, A. I. Shabaev, V. V. Klimenko
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摘要

这篇文章讨论的是计算造纸厂产量日历计划的问题。要找到最优计划,需要综合使用以下标准:最小的切边损失、最小的刀具设置变化以及产品等级的平稳过渡。本文介绍了综合使用单纯形法、列生成法、分支和约束法以及贪婪算法的求解算法。考虑到纸张生产和销售的特殊性--客户订单不是以成品的整数单位来衡量的,而是以重量来衡量的,重量是用 "公差 "来规定的。针对纸张生产计划问题,提出了一种遗传类型算法的变体。该算法基于二次或线性规划问题的多重解法,使用特殊的交叉和突变算子。基于俄罗斯欧洲地区一家造纸厂真实生产数据的计算实验结果表明,该算法可有效地用于规划一台造纸机多个班次的工作,或在为一组造纸机设计切割计划时作为辅助工具。所提出的算法可提供不超过 400 个裁切计划的最佳解决方案,这相当于一台造纸机生产相同直径和密度的纸卷时大约需要 8 个订单。也就是说,该算法可用于不超过两天的生产计划。如果要建立更长时间(一周、一个月)的计划系统,则需要开发特殊的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Algorithm for Linear Cutting Problem
The article deals with the problem of calculating the volume calendar plan of a paper mill. To find the optimal schedule, a combination of the following criteria is used: minimal trim loss, minimal changes to the knives setup and smooth transitions by product grades. Solution algorithms are presented that use a combination of the simplex method, the column generation, the branch and bound methods, the greedy algorithm. The specifics of paper production and sales are taken into account — customer orders are measured not in the whole number of units of finished products, but by weight, which is specified with "tolerances". A variant of the genetic type algorithm for the paper production planning problem is proposed. The algorithm uses special crossover and mutation operators based on the multiple solution of quadratic or linear programming problems. The result of a computational experiment based on real production data of a paper mill in European part of Russia shows that the algorithm can be effectively used to plan the work of one paper machine for several shifts or as an auxiliary tool when devising cutting plans for a group of paper machines. The proposed algorithm provides an optimal solution with no more than 400 cutting plans, which corresponds to approximately 8 orders for rolls of the same diameter and density produced on one paper machine. That is, it can be used when planning production for no more than two days. To build planning systems for longer periods of time (week, month), it is necessary to develop special algorithms.
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