基于灰色-马尔科夫链的冷链物流需求量预测——以昆明市鲜切花为例
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摘要
目前以昆明为主的云南鲜切花在国内市场占有率超过了70%,昆明市也因此成为全国最大的鲜切花供应产地,每年昆明市的鲜切花大约有90%以上外销到世界各地,"昆花出省"离不开冷链物流,而鲜花界素来就有"鲜切花品质50%取决于种植,50%来自采后处理和冷链运输"的说法,文中为了获得未来昆明市鲜切花冷链物流的需求状况,以2005年-2017年的鲜切花产量为依据,采用灰色-马尔科夫链模型对未来5年昆明市鲜切花的冷链物流需求量进行预测,预测结果显示其精确度达到了99. 032%,因此能够为当地政府提供可靠的参考。
At present,the market share of yunnan fresh cut flowers,mainly in kunming,exceeds 70% in China,Kunming has also become the largest producer of fresh cut flowers in the country,more than 90% of the fresh cut flowers in kunming are exported to the world every year."Kunming flower out of province"cannot be separated from cold chain logistics,and in the flower world,it has always been said that the quality of fresh cut flowers is 50% determined by planting and 50% by post-harvest processing and cold chain transportation. In order to get the future cold chain logistics demand of fresh cut flowers in kunming,based on the output of fresh cut flowers from 2005 to 2017,using the gray-markov chain to predict the demand for the next five years of the supply of cold chain logistics in kunming,the prediction results show that the accuracy reaches 99. 032%.Therefore,it can provide a reliable reference for the local government.
At present,the market share of yunnan fresh cut flowers,mainly in kunming,exceeds 70% in China,Kunming has also become the largest producer of fresh cut flowers in the country,more than 90% of the fresh cut flowers in kunming are exported to the world every year."Kunming flower out of province"cannot be separated from cold chain logistics,and in the flower world,it has always been said that the quality of fresh cut flowers is 50% determined by planting and 50% by post-harvest processing and cold chain transportation. In order to get the future cold chain logistics demand of fresh cut flowers in kunming,based on the output of fresh cut flowers from 2005 to 2017,using the gray-markov chain to predict the demand for the next five years of the supply of cold chain logistics in kunming,the prediction results show that the accuracy reaches 99. 032%.Therefore,it can provide a reliable reference for the local government.
引文
[1]相静,孔杨等.基于灰色系统GM(1,1)模型的山东省卫生总费用预测研究[J].中国卫生统计,2016,33(4):653-656.
[2]朱念,陈东升,等.基于灰色GM(1,N)模型的广西北部湾港口物流预测研究[J].数学的实践与认识,2017(23):303-310.
[3]陈昌源,戴冉,等.改进灰色模型在海上交通综合安全指数预测应用[J].中国航海,2017,40(1):58-63.
[4]向雪燕,张立杰.基于马尔科夫链的棉花价格预测[J].中国棉花,2016,43(10):1-6.
[5]徐彭娜,林志兴,等.基于马尔科夫模型的就餐人数预测[J].计算机系统应用,2017,26(4):212-217.
[6]王利莎,梁开宏.基于灰色马尔科夫模型的陕西省铁路客运量预测[J].数学的实践与认识,2018(8).
[7]樊超,郭亚菲,等.基于灰色-马尔科夫模型的粮食产量预测[J].江苏农业科学,2018,46(9):346-349.
[8]刘春月.物流需求量灰色-马尔科夫链预测模型分析研究[J].物流科技,2016,39(10):11-14.
[9]程霄,陈玉鹏,等.基于无偏灰色马尔科夫模型的新疆物流需求量预测[J].物流技术,2016,35(11):132-135.
[10]周倩倩,彭本红,等.基于灰色马尔科夫链的江苏城镇居民冷链物流需求量预测[J].物流科技,2015,38(6):21-25.
[11]吴家麒,何梅,等.四川省十三五农产品冷链物流需求预测--基于灰色-马尔可夫链模型[J].物流工程与管理,2018(2):38-41.
[12]孙飞燕.云南鲜花冷链物流发展研究[J].物流技术,2015(17):67-69.
[13]宋志兰,王冬岚,等.基于灰色GM(1,1)模型的云南花卉物流需求预测[J].物流技术,2015,34(3):148-151.
[14]袁孝勇,赵艳东.基于灰色马尔科夫链预测模型的换热站供水温度预测[J].电子测量技术,2015,38(4):32-34.
[15]赵振武,麻建军.基于灰色马尔科夫模型的机场安检危险品数量预测[J].安全与环境学报,2017,17(1):51-53.
[16]李华,胡奇英.预测与决策[M].西安:西安电子科技大学出版社,2005:135-138.
[2]朱念,陈东升,等.基于灰色GM(1,N)模型的广西北部湾港口物流预测研究[J].数学的实践与认识,2017(23):303-310.
[3]陈昌源,戴冉,等.改进灰色模型在海上交通综合安全指数预测应用[J].中国航海,2017,40(1):58-63.
[4]向雪燕,张立杰.基于马尔科夫链的棉花价格预测[J].中国棉花,2016,43(10):1-6.
[5]徐彭娜,林志兴,等.基于马尔科夫模型的就餐人数预测[J].计算机系统应用,2017,26(4):212-217.
[6]王利莎,梁开宏.基于灰色马尔科夫模型的陕西省铁路客运量预测[J].数学的实践与认识,2018(8).
[7]樊超,郭亚菲,等.基于灰色-马尔科夫模型的粮食产量预测[J].江苏农业科学,2018,46(9):346-349.
[8]刘春月.物流需求量灰色-马尔科夫链预测模型分析研究[J].物流科技,2016,39(10):11-14.
[9]程霄,陈玉鹏,等.基于无偏灰色马尔科夫模型的新疆物流需求量预测[J].物流技术,2016,35(11):132-135.
[10]周倩倩,彭本红,等.基于灰色马尔科夫链的江苏城镇居民冷链物流需求量预测[J].物流科技,2015,38(6):21-25.
[11]吴家麒,何梅,等.四川省十三五农产品冷链物流需求预测--基于灰色-马尔可夫链模型[J].物流工程与管理,2018(2):38-41.
[12]孙飞燕.云南鲜花冷链物流发展研究[J].物流技术,2015(17):67-69.
[13]宋志兰,王冬岚,等.基于灰色GM(1,1)模型的云南花卉物流需求预测[J].物流技术,2015,34(3):148-151.
[14]袁孝勇,赵艳东.基于灰色马尔科夫链预测模型的换热站供水温度预测[J].电子测量技术,2015,38(4):32-34.
[15]赵振武,麻建军.基于灰色马尔科夫模型的机场安检危险品数量预测[J].安全与环境学报,2017,17(1):51-53.
[16]李华,胡奇英.预测与决策[M].西安:西安电子科技大学出版社,2005:135-138.