基于改进FMEA的冷链物流配送过程风险分析
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摘要
随着生活水平的不断提高,人们对安全卫生和营养健康的生鲜食品的需求不断提高,从而促使了冷链物流的迅速发展。然而,冷链物流在配送过程中存在诸多潜在风险,如温度失控、车辆故障、包装不当等。因此,如何分析和预防冷链配送过程风险、确保商品质量,是冷链物流企业面临的一个非常重要的问题。针对传统故障模式及影响分析(FMEA)方法存在的缺陷,提出了一种基于区间直觉模糊集(IVIFS)和多属性边界近似区域比较(MABAC)方法的改进FMEA模型,并运用到实际冷链物流配送过程的风险分析中,以期为冷链物流企业保证商品质量、提升配送服务水平提供依据和参考。
In recent years, cold chain logistics develops rapidly with the improvement of living standards and the increase of peoples demand for the healthy, safe and nutritious fresh food. However, there exist many potential risks in the cold-chain logistics distribution process, such as temperature runaway, vehicle failure, improper packaging.Thus, risk assessment and analysis plays a crucial role in ensuring the goods quality for a cold-chain logistics enterprise. Considering the inherent shortcomings of the traditional failure mode and effect analysis(FMEA), in this study, we propose an improved FMEA model based on interval-valued intuitionistic fuzzy sets(IVIFS) and multi-attributive border approximation area comparison(MABAC) method, and apply it to analyze the risk of cold-chain logistics distribution process, which will provide basis and references for cold-chain logistics enterprises in ensuring product quality and enhancing service levels.
In recent years, cold chain logistics develops rapidly with the improvement of living standards and the increase of peoples demand for the healthy, safe and nutritious fresh food. However, there exist many potential risks in the cold-chain logistics distribution process, such as temperature runaway, vehicle failure, improper packaging.Thus, risk assessment and analysis plays a crucial role in ensuring the goods quality for a cold-chain logistics enterprise. Considering the inherent shortcomings of the traditional failure mode and effect analysis(FMEA), in this study, we propose an improved FMEA model based on interval-valued intuitionistic fuzzy sets(IVIFS) and multi-attributive border approximation area comparison(MABAC) method, and apply it to analyze the risk of cold-chain logistics distribution process, which will provide basis and references for cold-chain logistics enterprises in ensuring product quality and enhancing service levels.
引文
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[14]郜军艳,王博.基于直觉模糊集理论的TOPSIS多项目投标决策模型[J].水电能源科学,2012,30(5):87-89.
[15]李侠,邹斌.区间直觉模糊集的多属性综合评价新方法[J].合肥学院学报(自然科学版),2013,23(1):4-7.
[16]XUE Y X,YOU J X,LAI X D,et al.An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information[J].Applied Soft Computing,2016,38:703-713.DOI:10.1016/j.asoc.2015.10.010.
[17]谭吉玉,朱传喜,张小芝,等.基于TOPSIS的区间直觉模糊数排序法[J].控制与决策,2015,30(11):2014-2018.DOI:10.13195/j.kzyjc.2014.1433.
[18]李梅,吴冲,孙明明.基于风险偏好的区间直觉模糊多属性决策方法[J].东北石油大学学报,2015,39(5):119-124.DOI:10.3969/j.issn.2095-4107.2015.05.015.
[19]高红云,曲强,刘洋.企业中FMEA方法应用研究[J].新技术新工艺,2011(12):105-107.DOI:10.3969/j.issn.1003-5311.2011.12.036.
[20]刘胧,刘虎沉,林清恋.基于模糊证据推理和灰色关联理论的FMEA方法[J].模糊系统与数学,2011,25(2):71-80.
[2]周路.中国与加拿大两国农产品冷链物流的比较分析[J].对外经贸实务,2015(1):85-88.DOI:10.3969/j.issn.1003-5559.2015.01.023.
[3]陈镜羽,黄辉.我国生鲜农产品电子商务冷链物流现状与发展研究[J].科技管理研究,2015(6):179-183.DOI:10.3969/j.issn.1000-7695.2015.06.034.
[4]陈红丽,张利瑶,张欣.基于FAHP的生鲜食品冷链物流服务质量模型的评价[J].物流技术,2015,34(7):142-145,189.DOI:10.3969/j.issn.1005-152X.2015.07.046.
[5]LU L X,ZHENG W Z,LV Z Y,et al.Development and application of time-temperature indicators used on food during the cold chain logistics[J].Chinese Packaging Institute——2012Conference,2013,26(S1):80-90.DOI:10.1002/pts.2009.
[6]LI X F,WANG Y,CHEN X.Cold chain logistics system based on cloud computing[J].Concurrency and Computation:Practice and Experience,2012,24(17):2138-2150.DOI:10.1002/cpe.1840.
[7]林懿.我国冷链物流发展现状与对策研究[J].产业与科技论坛,2016,15(3):17-18.DOI:10.3969/j.issn.1673-5641.2016.03.007.
[8]LIU H C,LI P,YOU J X,et al.A novel approach for FMEA:Combination of interval 2-tuple linguistic variables and gray relational analysis[J].Quality and Reliability Engineering International,2015,31(5):761-772.DOI:10.1002/qre.1633.
[9]LIU H C,YOU J X,CHEN S M,et al.An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty[J].IIE Transactions,2016,48(11):1027-1042.DOI:10.1080/0740817X.2016.1172742.
[10]李辰,张帅,范英,等.基于FMEA改进方法的场内机动车辆风险评价研究[J].太原科技大学学报,2013,34(6):425-428.DOI:10.3969/j.issn.1673-2057.2013.06.006.
[11]胡定,于潇,郑永泉,等.基于FMEA法的预评估失效模式分析及改进方法[J].气象研究与应用,2015,36(2):100-102.DOI:10.3969/j.issn.1673-8411.2015.02.023.
[12]曹嘉容,王瑛,支乐.基于二元语义与灰色关联决策的改进FMEA方法[J].空军工程大学学报(自然科学版),2014,15(5):92-95.DOI:10.3969/j.issn.1009-3516.2014.05.021.
[13]罗小芳,李柏洲,白旭.FMEA方法改进及其在技术创新风险管理中的应用[J].运筹与管理,2015,24(4):264-271.DOI:10.3969/j.issn.1007-3221.2015.04.037.
[14]郜军艳,王博.基于直觉模糊集理论的TOPSIS多项目投标决策模型[J].水电能源科学,2012,30(5):87-89.
[15]李侠,邹斌.区间直觉模糊集的多属性综合评价新方法[J].合肥学院学报(自然科学版),2013,23(1):4-7.
[16]XUE Y X,YOU J X,LAI X D,et al.An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information[J].Applied Soft Computing,2016,38:703-713.DOI:10.1016/j.asoc.2015.10.010.
[17]谭吉玉,朱传喜,张小芝,等.基于TOPSIS的区间直觉模糊数排序法[J].控制与决策,2015,30(11):2014-2018.DOI:10.13195/j.kzyjc.2014.1433.
[18]李梅,吴冲,孙明明.基于风险偏好的区间直觉模糊多属性决策方法[J].东北石油大学学报,2015,39(5):119-124.DOI:10.3969/j.issn.2095-4107.2015.05.015.
[19]高红云,曲强,刘洋.企业中FMEA方法应用研究[J].新技术新工艺,2011(12):105-107.DOI:10.3969/j.issn.1003-5311.2011.12.036.
[20]刘胧,刘虎沉,林清恋.基于模糊证据推理和灰色关联理论的FMEA方法[J].模糊系统与数学,2011,25(2):71-80.