多目标一维下料决策方法研究

中国机械工程 ›› 2013, Vol. 24 ›› Issue (07): 951-955,963.

多目标一维下料决策方法研究

刘林;葛菲菲;刘心报

合肥工业大学过程优化与智能决策教育部重点实验室,合肥,230009

出版日期:2013-04-10 发布日期:2013-04-27
基金资助:
国家自然科学基金资助项目（71171071）；安徽省高校省级自然科学研究重点项目（KJ2011A215）；合肥工业大学博士学位专项基金资助项目（GDBJ2010001）
National Natural Science Foundation of China（No. 71171071）

Research on Decision-making Method of One-dimensional Cutting Stock Problem Based on Multi-objective Optimization

Liu Lin;Ge Feifei;Liu Xinbao

Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education,Hefei University of Technology,Hefei,230009

Online:2013-04-10 Published:2013-04-27
Supported by:

National Natural Science Foundation of China（No. 71171071）

摘要/Abstract

摘要：

研究目标为最小化原料浪费、最小化下料方式数和最小化可用余料返回的多目标优化下料问题。运用多目标优化和多属性决策相结合的方法设计下料决策方法,即先用改进的非支配排序启发式进化算法求出问题的Pareto最优解集,再采用综合主客观赋权法计算各优化目标的权重,最后运用多属性决策方法选出一个满意解作为下料方案。实验结果证实所提方法对多目标下料决策是有效的。

关键词: 下料问题, 多目标优化, 多属性决策, 综合赋权法

Abstract:

A multi-objective cutting stock problem was studied. The objects were to minimize trim loss, the number of cutting patterns and usable leftovers. A decision-making method for this cutting stock problem was designed. First, the Pareto-optimal set was gotten by using the improved non-dominated sorting heuristic evolutionary algorithms. Then the weights of the every object were calculated by combining the subjective and objective method. Finally, a satisfactory solution, which was regarded as cutting scheme, was finded from the Pareto-optimal set by employing the multi-attribute decision making method. The experimental results show that the proposed method is effective. 

Key words: cutting stock problem, multi-objective optimization, multi-attribute decision making, combining weighting method

中图分类号:

TP18

刘林, 葛菲菲, 刘心报. 多目标一维下料决策方法研究[J]. 中国机械工程, 2013, 24(07): 951-955,963.

LIU Lin, GE Fei-Fei, LIU Xin-Bao. Research on Decision-making Method of One-dimensional Cutting Stock Problem Based on Multi-objective Optimization[J]. China Mechanical Engineering, 2013, 24(07): 951-955,963.

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