基于Monte Carlo指数平滑订单预测与决策分析
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摘要
目的为了解决电商企业订单到达的不确定性和仓储运营的特殊性造成的人员配置不合理问题,提出一种构建基于订单预测和产线平衡为目标的Monte Carlo季节指数平滑和作业产能模型。方法应用概率统计方法解决信息不完全下订单预测问题。通过该方法对季节指数平滑法中的平滑系数进行优化,以修正预测模型,然后用Crystal ball软件对预测值进行产线调度优化。结果算例分析表明,使用该方法进行预测时,精度提高了45%,并将预测值用于拣选作业产能安排,确定了最优人员数量和工时分配方案。结论可以为电商类企业提供准确的订单预测信息,以及合理的作业人员配置方案,提高了企业的运行效率。
The paper aims to propose a Monte Carlo seasonal exponential smoothing and job capacity model based on order forecasting and production line balance, to solve the problem of unreasonable staffing caused by the uncertainty of the order arrival of e-commerce enterprises and the particularity of warehousing operations. The probabilistic statistical method was used to solve the problem of incomplete ordering. The smoothing coefficient in the seasonal exponential smoothing method was optimized by this method to modify the prediction model. And then the software-Crystal ball was used to optimize the production line scheduling. The analysis of the example showed that when this method was used for prediction, the accuracy was improved by 45%. The predicted value was used for the sorting operation capacity arrangement and the optimal number of people and the distribution of working hours were determined. It can provide accurate forecasting information for e-commerce companies, as well as reasonable staffing solutions to improve business efficiency.
The paper aims to propose a Monte Carlo seasonal exponential smoothing and job capacity model based on order forecasting and production line balance, to solve the problem of unreasonable staffing caused by the uncertainty of the order arrival of e-commerce enterprises and the particularity of warehousing operations. The probabilistic statistical method was used to solve the problem of incomplete ordering. The smoothing coefficient in the seasonal exponential smoothing method was optimized by this method to modify the prediction model. And then the software-Crystal ball was used to optimize the production line scheduling. The analysis of the example showed that when this method was used for prediction, the accuracy was improved by 45%. The predicted value was used for the sorting operation capacity arrangement and the optimal number of people and the distribution of working hours were determined. It can provide accurate forecasting information for e-commerce companies, as well as reasonable staffing solutions to improve business efficiency.
引文
[1]华晓晖,闫秀霞.基于神经网络的订单预测研究[J].华东经济管理,2007,21(2):108-110.HUA Xiao-hui,YAN Xiu-xia.The Study of Order s Forecast Based on Neural Network[J].East China Economic Management,2007,21(2):108-110.
[2]葛彦强,汪向征,王爱民.改进灰色神经网络的冰箱订单需求预测研究[J].计算机仿真,2012,29(5):219-222.GE Yan-qiang,WANG Xiang-zheng,WANG Ai-min.Research on Forecasting Demand of Refrigerator Order Based on Improved Gray Neural Network[J].Computer Simulation,2012,29(5):219-222.
[3]王旭坪,张珺,易彩玉.B2C电子商务环境下订单拣选与配送联合调度优化[J].中国管理科学,2016,24(7):101-109.WANG Xu-ping,ZHANG Jun,YI Cai-yu.Joint Scheduling Optimization of Order Picking and Distribution in B2C E-commerce Environment[J].Chinese Management Science,2016,24(7):101-109.
[4]詹卫许,钱淑钗,印鉴.月度用电量灰色预测改进模型[J].南方电网技术,2012,6(5):92-96.ZHAN Wei-xu,QIAN Shu-chai,YIN Jian.Monthly Electricity Consumption Gray Prediction Improvement Model[J].Southern Power System Technology,2012,6(5):92-96.
[5]KIN K L,YU L,WANG S Y.Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication[J].Lecture Notes in Computer Science,2006(6):28-31.
[6]龚晓,郭进利.基于三次指数平滑法的沪牌拍卖月均价预测[J].上海理工大学学报,2018,40(1):27-32.GONG Xiao,GUO Jin-li.Prediction of Monthly Average Price of Shanghai Brand Auction Based on Cubic Exponential Smoothing Method[J].Journal of Shanghai University of Technology,2018,40(1):27-32.
[7]贡文伟,黄晶.基于灰色理论与指数平滑法的需求预测综合模型[J].统计与决策,2017(1):72-76.GONG Wen-wei,HUANG Jing.Comprehensive Model of Demand Forecast Based on Grey Theory and Exponential Smoothing Method[J].Statistics and Decision,2017(1):72-76.
[8]孟吉伟,王少华,陈晓,等.基于蒙特卡洛的串行生产系统订单交货期可靠性研究[J].机械设计与制造,2013(7):245-246.MENG Ji-wei,WANG Shao-hua,CHEN Xiao,et al.Research on the Reliability of Order Delivery Time of Serial Production System Based on Monte Carlo[J].Mechanical Design and Manufacturing,2013(7):245-246.
[9]LI J H,CHEN W Y.Forecasting Macroeconomic Time Series:LASSO-based Approaches and Their Forecast Combinations with Dynamic Factor Models[J].International Journal of Forecasting,2014,30(4):96-105.
[10]刘飏,戴敏,张志胜,等.基于滑动平均订单预测的半导体生产库存模型[J].计算机集成制造系统,2010,16(2):324-330.LIU Yang,DAI Min,ZHANG Zhi-sheng,et al.Semiconductor Production Inventory Model Based on Moving Average Order Forecasting[J].Computer Integrated Manufacturing Systems,2010,16(2):324-330.
[11]KATHLEEN S.Factors that Affect the Improvement of Demand Forecast Accuracy Through Point-of-sale Reporting[J].European Journal of Operational Research,2017(260):171-182.
[12]吴利丰,刘思峰,方志耕,等.基于新贴近度的产品成本测算指数平滑模型[J].计算机集成制造系统,2014,20(3):555-558.WU Li-feng,LIU Si-feng,FANG Zhi-geng,et al.Product Cost Estimation Exponential Smoothing Model Based on New Closeness[J].Computer Integrated Manufacturing Systems,2014,20(3):555-558.
[13]李根,赵金楼,苏屹.基于ARMA模型的世界集装箱船手持订单量预测研究[J].科技管理研究,2012,32(16):217-221.LI Gen,ZHAO Jin-lou,SU Yi.Research on Forecast of Handled Order Quantity of World Container Ships Based on ARMA Model[J].Science and Technology Management Research,2012,32(16):217-221.
[14]张标,张领先,傅泽田,等.基于季节指数的蔬菜价格变动趋势分析及预测[J].北方园艺,2017(18):185-191.ZHANG Biao,ZHANG Ling-xian,FU Ze-tian,WANGJie-qiong.Analysis and Forecast of Vegetable Price Change Trend Based on Seasonal Index[J].Northern Horticulture,2017(18):185-191.
[15]廖耀华,粟时平,康军胜,等.基于改进指数平滑法和马尔科夫模型的风速预测研究[J].电力科学与技术学报,2016,31(1):85-89.LIAO Yao-hua,SU Shi-ping,KANG Jun-sheng,et al.Research on Wind Speed Prediction Based on Improved Exponential Smoothing Method and Markov Model[J].Journal of Electric Power Science and Technology,2016,31(1):85-89.
[16]刘晨,蒙丹花,方玮宸,等.多品种小批量订单型企业生产调度优化[J].包装工程,2016,37(11):93-99.LIU Chen,MENG Dan-hua,FANG Wei-chen,et al.Optimization of Production Scheduling for Multi-variety and Small-lot Order Enterprises[J].Packaging Engineering,2016,37(11):93-99.
[2]葛彦强,汪向征,王爱民.改进灰色神经网络的冰箱订单需求预测研究[J].计算机仿真,2012,29(5):219-222.GE Yan-qiang,WANG Xiang-zheng,WANG Ai-min.Research on Forecasting Demand of Refrigerator Order Based on Improved Gray Neural Network[J].Computer Simulation,2012,29(5):219-222.
[3]王旭坪,张珺,易彩玉.B2C电子商务环境下订单拣选与配送联合调度优化[J].中国管理科学,2016,24(7):101-109.WANG Xu-ping,ZHANG Jun,YI Cai-yu.Joint Scheduling Optimization of Order Picking and Distribution in B2C E-commerce Environment[J].Chinese Management Science,2016,24(7):101-109.
[4]詹卫许,钱淑钗,印鉴.月度用电量灰色预测改进模型[J].南方电网技术,2012,6(5):92-96.ZHAN Wei-xu,QIAN Shu-chai,YIN Jian.Monthly Electricity Consumption Gray Prediction Improvement Model[J].Southern Power System Technology,2012,6(5):92-96.
[5]KIN K L,YU L,WANG S Y.Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication[J].Lecture Notes in Computer Science,2006(6):28-31.
[6]龚晓,郭进利.基于三次指数平滑法的沪牌拍卖月均价预测[J].上海理工大学学报,2018,40(1):27-32.GONG Xiao,GUO Jin-li.Prediction of Monthly Average Price of Shanghai Brand Auction Based on Cubic Exponential Smoothing Method[J].Journal of Shanghai University of Technology,2018,40(1):27-32.
[7]贡文伟,黄晶.基于灰色理论与指数平滑法的需求预测综合模型[J].统计与决策,2017(1):72-76.GONG Wen-wei,HUANG Jing.Comprehensive Model of Demand Forecast Based on Grey Theory and Exponential Smoothing Method[J].Statistics and Decision,2017(1):72-76.
[8]孟吉伟,王少华,陈晓,等.基于蒙特卡洛的串行生产系统订单交货期可靠性研究[J].机械设计与制造,2013(7):245-246.MENG Ji-wei,WANG Shao-hua,CHEN Xiao,et al.Research on the Reliability of Order Delivery Time of Serial Production System Based on Monte Carlo[J].Mechanical Design and Manufacturing,2013(7):245-246.
[9]LI J H,CHEN W Y.Forecasting Macroeconomic Time Series:LASSO-based Approaches and Their Forecast Combinations with Dynamic Factor Models[J].International Journal of Forecasting,2014,30(4):96-105.
[10]刘飏,戴敏,张志胜,等.基于滑动平均订单预测的半导体生产库存模型[J].计算机集成制造系统,2010,16(2):324-330.LIU Yang,DAI Min,ZHANG Zhi-sheng,et al.Semiconductor Production Inventory Model Based on Moving Average Order Forecasting[J].Computer Integrated Manufacturing Systems,2010,16(2):324-330.
[11]KATHLEEN S.Factors that Affect the Improvement of Demand Forecast Accuracy Through Point-of-sale Reporting[J].European Journal of Operational Research,2017(260):171-182.
[12]吴利丰,刘思峰,方志耕,等.基于新贴近度的产品成本测算指数平滑模型[J].计算机集成制造系统,2014,20(3):555-558.WU Li-feng,LIU Si-feng,FANG Zhi-geng,et al.Product Cost Estimation Exponential Smoothing Model Based on New Closeness[J].Computer Integrated Manufacturing Systems,2014,20(3):555-558.
[13]李根,赵金楼,苏屹.基于ARMA模型的世界集装箱船手持订单量预测研究[J].科技管理研究,2012,32(16):217-221.LI Gen,ZHAO Jin-lou,SU Yi.Research on Forecast of Handled Order Quantity of World Container Ships Based on ARMA Model[J].Science and Technology Management Research,2012,32(16):217-221.
[14]张标,张领先,傅泽田,等.基于季节指数的蔬菜价格变动趋势分析及预测[J].北方园艺,2017(18):185-191.ZHANG Biao,ZHANG Ling-xian,FU Ze-tian,WANGJie-qiong.Analysis and Forecast of Vegetable Price Change Trend Based on Seasonal Index[J].Northern Horticulture,2017(18):185-191.
[15]廖耀华,粟时平,康军胜,等.基于改进指数平滑法和马尔科夫模型的风速预测研究[J].电力科学与技术学报,2016,31(1):85-89.LIAO Yao-hua,SU Shi-ping,KANG Jun-sheng,et al.Research on Wind Speed Prediction Based on Improved Exponential Smoothing Method and Markov Model[J].Journal of Electric Power Science and Technology,2016,31(1):85-89.
[16]刘晨,蒙丹花,方玮宸,等.多品种小批量订单型企业生产调度优化[J].包装工程,2016,37(11):93-99.LIU Chen,MENG Dan-hua,FANG Wei-chen,et al.Optimization of Production Scheduling for Multi-variety and Small-lot Order Enterprises[J].Packaging Engineering,2016,37(11):93-99.