| 引用本文: | 杨超,侯兴明,陈小卫,等.新型小规模装备备件品种确定的犹豫模糊粗糙集决策方法.[J].国防科技大学学报,2022,44(3):201-210.[点击复制] |
| YANG Chao,HOU Xingming,CHEN Xiaowei,et al.Hesitant fuzzy rough set decision-making method for determining spare parts variety of new small-scale equipment[J].Journal of National University of Defense Technology,2022,44(3):201-210[点击复制] |
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| 新型小规模装备备件品种确定的犹豫模糊粗糙集决策方法 |
| 杨超1,侯兴明2,陈小卫2,秦海峰3,张琳琳2 |
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(1. 航天工程大学 航天指挥学院, 北京 101416;2. 航天工程大学 航天保障系, 北京 101416;3. 陆军装甲兵学院士官学校 指挥管理系, 吉林 长春 130117)
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| 摘要: |
| 针对新型小规模装备备件品种确定过程中决策信息犹豫性和模糊性特点突出、难以运用传统备件品种确定方法进行决策的问题,提出一种基于犹豫模糊粗糙集的备件品种确定方法。利用风险偏好系数对不完备犹豫模糊信息进行数值延拓,为构建不同风险偏好下备件品种确定的犹豫模糊决策信息系统奠定基础；考虑得分函数和数值延拓边界的综合因素影响,给出改进的包含度计算公式,并基于包含度定义进行了证明；给出基于改进包含度计算的备件品种决策属性的约简条件和规则获取方法,实现了犹豫模糊决策信息的深度挖掘和有效利用。研究结果表明:通过该法能够有效处理犹豫模糊决策信息,获取精简实用的备件品种决策规则集,验证了方法的可行性。 |
| 关键词: 犹豫模糊集 粗糙集 新型小规模装备 备件品种 属性约简 决策规则 |
| DOI：10.11887/j.cn.202203024 |
| 投稿日期：2020-09-29 |
| 基金项目:装备军内科研计划资助项目(TJ20172B05001)；全军军事类研究生资助项目(JY2018C210) |
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| Hesitant fuzzy rough set decision-making method for determining spare parts variety of new small-scale equipment |
| YANG Chao1, HOU Xingming2, CHEN Xiaowei2, QIN Haifeng3, ZHANG Linlin2 |
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(1. Space Command Academy, Space Engineering University, Beijing 101416, China;2. Department of Space Support, Space Engineering University, Beijing 101416, China;3. Department of Command and Management, NCO Institute of Army Armored Academy, Changchun 130117, China)
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| Abstract: |
| Aiming at the problem that the prominent features of hesitancy and fuzziness of decision information and it′s difficult to use the traditional method to determine the varieties of spare parts for new equipment of small scale, a method of determining the varieties of spare parts based on hesitating fuzzy rough sets was proposed. The risk preference coefficient was used to extend the incomplete hesitancy information, which laid a foundation for establishing the hesitancy information system for different risk preference. Considering the influence of the score function and the numerical continuation boundary, an improved inclusion degree formula was given and proved on the basis of the definition of inclusion degree. The reduction condition and rule acquisition method of spare parts varieties decision attribute based on the improved inclusion calculation were given, which realized the depth mining and effective utilization of hesitancy and decision information. The results show that this method can deal with hesitancy and decision information effectively, and obtain a simplified and practical decision rule set of spare parts varieties, and the feasibility of the method was verified. |
| Keywords: hesitant fuzzy set rough set new equipment of small scale varieties of spare parts attribute reduction decision rule |
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