基于模糊认知图的物流需求预测模型研究
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摘要
准确地预测社会物流需求,在政府对物流行业政策制定、企业物流活动规划中有着重要意义.本文提出一种基于模糊认知图的物流需求预测模型构建方法,综合考虑国内生产总值、进出口总额等五个经济要素与物流需求之间的相互影响关系,通过对历史数据机器学习获得相互影响权重,构建了物流需求预测模型,可对未来物流需求进行推算和预测.实验证明,该模型对物流需求的预测精度较高,效果较好.
Accurate prediction of social logistics demands is essential to government's policy formulation for the logistics industry as well as to enterprise's logistics activity planning.In this paper,a logistics demand prediction model construction method based on the fuzzy cognitive map(FCM)is proposed.This method comprehensively considers the mutual influence between five economic elements(GDP,total import and export volume,etc.)and logistics demands,and acquires the mutual influence weight through machine learning of historical data.Finally,a logistics demand prediction model is built,which can realize accurate prediction of the future logistics demands.The experimental results provide solid evidence for high precision and favorable performance of the model in predicting logistics demands.
Accurate prediction of social logistics demands is essential to government's policy formulation for the logistics industry as well as to enterprise's logistics activity planning.In this paper,a logistics demand prediction model construction method based on the fuzzy cognitive map(FCM)is proposed.This method comprehensively considers the mutual influence between five economic elements(GDP,total import and export volume,etc.)and logistics demands,and acquires the mutual influence weight through machine learning of historical data.Finally,a logistics demand prediction model is built,which can realize accurate prediction of the future logistics demands.The experimental results provide solid evidence for high precision and favorable performance of the model in predicting logistics demands.
引文
[1]刘源.基于灰色预测模型的物流需求分析[J].物流技术,2012,31(11):59-61.Liu Y.Analysis of logistics demand based on gray forecasting model[J].Logistics Technology,2012,31(11):59-61.
[2]肖和英.基于过程化的第三方物流企业服务质量评价指标体系研究[J].物流技术,2018,37(5):54-59.Xiao H Y.Study on process-based service quality evaluation[J].Logistics Technology,2018,37(5):54-59.
[3]方威,肖衡,任湘郴,基于线性回归模型的物流需求预测分析[J].生产力研究,2009(12):100-101.Fang W,Xiao H,Ren X C.Logistics demand forecasting analysis based on linear regression model[J].Productivity Research,2009(12):100-101.
[4]李捷,陈彦如,杨璐.基于两阶段组合预测模型的区域物流需求预测[J].信息与控制,2018,47(2):247-256.Li J,Chen Y R,Yang L.Regional logistics demand forecasting based on two-stage combination prediction model[J].Information and Control,2018,47(2):247-256.
[5]李进.基于可信性的低碳物流网络设计多目标模糊规划问题[J].系统工程理论与实践,2015,35(6):1482-1492.Li J.Credibility-based multi-objective fuzzy programming problem for low-carbon logistics network design[J].Systems Engineering—Theory&Pracitce,2015,35(6):1482-1492.
[6]刘明,曹杰.考虑多重不确定性的托管药房物流调度优化[J].系统工程理论与实践,2017,37(12):3160-3169.Liu M,Cao J.Optimization of logistics scheduling for hospital pharmacy trusteeship under hybrid uncertainty[J].Systems Engineering—Theory&Practice,2017,37(12):3160-3169.
[7]吴洁明,李余琪,万励.物流需求预测算法的仿真研究[J].计算机仿真,2011,28(9):246-249.Wu J M,Li Y Q,Wan L.Simulation research on forecast algorithm of logistic demand[J].Computer Simulation,2011,28(9):246-249.
[8]贾海成,秦菲菲.区域物流需求预测研究——以江苏省为例[J].中国物流与采购,2012(3):68-69.Jia H C,Qin F F.Regional logistics demand forecasting research—A case study of Jiangsu province[J].China Logistics&Purchasing,2012(3):68-69.
[9]黄敏珍,冯永冰.基于灰色-马尔可夫链的区域物流需求预测[J].统计与决策,2009(16):166-168.Huang M Z,Feng Y B.Regional logistics demand prediction based on gray-Markov chain[J].Statistics&Decision,2009(16):166-168.
[10]邱慧,黄解宇,董亚兰.基于灰色系统模型的山西省物流需求预测分析[J].数学的实践与认识,2016,46(13):66-70.Qiu H,Huang J Y,Dong Y L.Analysis of logistics demand forecast in Shanxi province based on grey system model[J].Mathematics in Practice and Theory,2016,46(13):66-70.
[11]谢晓燕,韦学婷,王霖.基于指数平滑法的呼、包、鄂三角区物流需求量预测[J].干旱区资源与环境,2013,27(1):58-62.Xie X Y,Wei X T,Wang L.Prediction of logistics demand based on exponential smoothing model for Hohhot,Baotou and Ordos[J].Journal of Arid Land Resources and Environment,2013,27(1):58-62.
[12]张国玲,徐学红.一种基于ARIMA-BPNN的物流需求预测模型[J].控制工程,2017,24(5):958-962.Zhang G L,Xu X H.A logistics demand prediction based on ARIMA-BPNN[J].Control Engineering of China,2017,24(5):958-962.
[13]许茂增,余国印.基于C#与MATLAB混合编程的物流需求预测系统的实现[J].重庆交通大学学报(自然科学版),2015,34(4):128-132.Xu M Z,Yu G Y.Implementation of logistics demand forecasting system based on hybrid programming C#and MATLAB[J].Journal of Chongqing Jiaotong University(Natural Science),2015,34(4):128-132.
[14]周岩.基于多模型模糊神经网络的智能天气预报[D].上海:复旦大学,2007.
[15]闫娟.灰色神经网络模型在物流需求预测中的研究[J].计算机仿真,2011,28(7):200-203.Yan J.Application of logistics demand forecasting based on grey neural network[J].Computer Simulation,2011,28(7):200-203.
[16]王新利,赵琨.基于神经网络的农产品物流需求预测研究[J].农业技术经济,2010(2):62-68.
[17]林春梅,何跃,汤兵勇,等.模糊认知图在股票市场预测中的应用研究[J].计算机应用,2006,26(1):195-197.Lin C M,He Y,Tang B Y,et al.Application of fuzzy cognitive map in stock market forecasting[J].Journal of Computer Applications,2006,26(1):195-197.
[18]Poczeta K,Yastrebov A,Papageorgiou E I.Learning fuzzy cognitive maps using structure optimization genetic algorithm[C]//Computer Science and Information Systems,IEEE,2015.
[19]赵彦军,陈玉.时间序列分析方法在物流需求预测中的应用[J].物流科技,2017,40(6):12-14.Zhao Y J,Chen Y.Application of time series analysis in logistics demand forecasting[J].Logistics Sci-Tech,2017,40(6):12-14.
[20]张金良,谭忠富.混沌时间序列的混合预测方法[J].系统工程理论与实践,2013,33(3):763-769.Zhang J L,Tan Z F.Prediction of the chaotic time series using hybrid method[J].Systems Engineering—Theory&Practice,2013,33(3):763-769.
[21]方伟,张龄之.基于多目标演化的模糊认知图学习算法[J].系统工程与电子技术,2018,40(2):447-455.Fang W,Zhang L Z.Learning of fuzzy cognitive maps using multi-objective evolutionary algorithm[J].Systems Engineering and Electronic Technology,2018,40(2):447-455.
[22]Kosko,Bart.Fuzzy cognitive maps[J].International Journal of Man-Machine Studies,1986,24(1):65-75.
[23]唐亮,靖可,何杰.网络化制造模式下基于改进蚁群算法的供应链调度优化研究[J].系统工程理论与实践,2014,34(5):1267-1275.Tang L,Jing K,He J.Supply chain scheduling optimization under networked manufacturing based on improved ant colony optimization algorithm[J].Systems Engineering—Theory&Practice,2014,34(5):1267-1275.
[24]李松,刘力军,翟曼.改进粒子群算法优化BP神经网络的短时交通流预测[J].系统工程理论与实践,2012,32(9):2045-2049.Li S,Liu L J,Zhai M.Prediction for short-term traffic flow based on modified PSO optimized BP neural network[J].Systems Engineering—Theory&Practice,2012,32(9):2045-2049.
[2]肖和英.基于过程化的第三方物流企业服务质量评价指标体系研究[J].物流技术,2018,37(5):54-59.Xiao H Y.Study on process-based service quality evaluation[J].Logistics Technology,2018,37(5):54-59.
[3]方威,肖衡,任湘郴,基于线性回归模型的物流需求预测分析[J].生产力研究,2009(12):100-101.Fang W,Xiao H,Ren X C.Logistics demand forecasting analysis based on linear regression model[J].Productivity Research,2009(12):100-101.
[4]李捷,陈彦如,杨璐.基于两阶段组合预测模型的区域物流需求预测[J].信息与控制,2018,47(2):247-256.Li J,Chen Y R,Yang L.Regional logistics demand forecasting based on two-stage combination prediction model[J].Information and Control,2018,47(2):247-256.
[5]李进.基于可信性的低碳物流网络设计多目标模糊规划问题[J].系统工程理论与实践,2015,35(6):1482-1492.Li J.Credibility-based multi-objective fuzzy programming problem for low-carbon logistics network design[J].Systems Engineering—Theory&Pracitce,2015,35(6):1482-1492.
[6]刘明,曹杰.考虑多重不确定性的托管药房物流调度优化[J].系统工程理论与实践,2017,37(12):3160-3169.Liu M,Cao J.Optimization of logistics scheduling for hospital pharmacy trusteeship under hybrid uncertainty[J].Systems Engineering—Theory&Practice,2017,37(12):3160-3169.
[7]吴洁明,李余琪,万励.物流需求预测算法的仿真研究[J].计算机仿真,2011,28(9):246-249.Wu J M,Li Y Q,Wan L.Simulation research on forecast algorithm of logistic demand[J].Computer Simulation,2011,28(9):246-249.
[8]贾海成,秦菲菲.区域物流需求预测研究——以江苏省为例[J].中国物流与采购,2012(3):68-69.Jia H C,Qin F F.Regional logistics demand forecasting research—A case study of Jiangsu province[J].China Logistics&Purchasing,2012(3):68-69.
[9]黄敏珍,冯永冰.基于灰色-马尔可夫链的区域物流需求预测[J].统计与决策,2009(16):166-168.Huang M Z,Feng Y B.Regional logistics demand prediction based on gray-Markov chain[J].Statistics&Decision,2009(16):166-168.
[10]邱慧,黄解宇,董亚兰.基于灰色系统模型的山西省物流需求预测分析[J].数学的实践与认识,2016,46(13):66-70.Qiu H,Huang J Y,Dong Y L.Analysis of logistics demand forecast in Shanxi province based on grey system model[J].Mathematics in Practice and Theory,2016,46(13):66-70.
[11]谢晓燕,韦学婷,王霖.基于指数平滑法的呼、包、鄂三角区物流需求量预测[J].干旱区资源与环境,2013,27(1):58-62.Xie X Y,Wei X T,Wang L.Prediction of logistics demand based on exponential smoothing model for Hohhot,Baotou and Ordos[J].Journal of Arid Land Resources and Environment,2013,27(1):58-62.
[12]张国玲,徐学红.一种基于ARIMA-BPNN的物流需求预测模型[J].控制工程,2017,24(5):958-962.Zhang G L,Xu X H.A logistics demand prediction based on ARIMA-BPNN[J].Control Engineering of China,2017,24(5):958-962.
[13]许茂增,余国印.基于C#与MATLAB混合编程的物流需求预测系统的实现[J].重庆交通大学学报(自然科学版),2015,34(4):128-132.Xu M Z,Yu G Y.Implementation of logistics demand forecasting system based on hybrid programming C#and MATLAB[J].Journal of Chongqing Jiaotong University(Natural Science),2015,34(4):128-132.
[14]周岩.基于多模型模糊神经网络的智能天气预报[D].上海:复旦大学,2007.
[15]闫娟.灰色神经网络模型在物流需求预测中的研究[J].计算机仿真,2011,28(7):200-203.Yan J.Application of logistics demand forecasting based on grey neural network[J].Computer Simulation,2011,28(7):200-203.
[16]王新利,赵琨.基于神经网络的农产品物流需求预测研究[J].农业技术经济,2010(2):62-68.
[17]林春梅,何跃,汤兵勇,等.模糊认知图在股票市场预测中的应用研究[J].计算机应用,2006,26(1):195-197.Lin C M,He Y,Tang B Y,et al.Application of fuzzy cognitive map in stock market forecasting[J].Journal of Computer Applications,2006,26(1):195-197.
[18]Poczeta K,Yastrebov A,Papageorgiou E I.Learning fuzzy cognitive maps using structure optimization genetic algorithm[C]//Computer Science and Information Systems,IEEE,2015.
[19]赵彦军,陈玉.时间序列分析方法在物流需求预测中的应用[J].物流科技,2017,40(6):12-14.Zhao Y J,Chen Y.Application of time series analysis in logistics demand forecasting[J].Logistics Sci-Tech,2017,40(6):12-14.
[20]张金良,谭忠富.混沌时间序列的混合预测方法[J].系统工程理论与实践,2013,33(3):763-769.Zhang J L,Tan Z F.Prediction of the chaotic time series using hybrid method[J].Systems Engineering—Theory&Practice,2013,33(3):763-769.
[21]方伟,张龄之.基于多目标演化的模糊认知图学习算法[J].系统工程与电子技术,2018,40(2):447-455.Fang W,Zhang L Z.Learning of fuzzy cognitive maps using multi-objective evolutionary algorithm[J].Systems Engineering and Electronic Technology,2018,40(2):447-455.
[22]Kosko,Bart.Fuzzy cognitive maps[J].International Journal of Man-Machine Studies,1986,24(1):65-75.
[23]唐亮,靖可,何杰.网络化制造模式下基于改进蚁群算法的供应链调度优化研究[J].系统工程理论与实践,2014,34(5):1267-1275.Tang L,Jing K,He J.Supply chain scheduling optimization under networked manufacturing based on improved ant colony optimization algorithm[J].Systems Engineering—Theory&Practice,2014,34(5):1267-1275.
[24]李松,刘力军,翟曼.改进粒子群算法优化BP神经网络的短时交通流预测[J].系统工程理论与实践,2012,32(9):2045-2049.Li S,Liu L J,Zhai M.Prediction for short-term traffic flow based on modified PSO optimized BP neural network[J].Systems Engineering—Theory&Practice,2012,32(9):2045-2049.