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A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm
Daisuke Takeyasu,Kazuhiro Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11
In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is executed to the data of Wheelchairs for three cases (Sum total data of Wheelchairs, Manually propelled wheelchairs and Electric wheelchairs). Genetic Algorithm is utilized to search the optimal weight for the weighting parameters of linear and non-linear function. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.
Intermittent Demand Forecasting in the Case of Medical Apparatus By Improving Forecasting Accuracy
Daisuke Takeyasu,Asami Shitara,Kazuhiro Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11
Intermittent data are often seen in industries. But it is rather difficult to make forecasting in general. In recent years, the needs for intermittent demand forecasting are increasing because of the constraints of strict Supply Chain Management. How to improve the forecasting accuracy is an important issue. There are many researches made on this. But there are rooms for improvement. In this paper, a new method for cumulative forecasting method is proposed. The data is cumulated and to this cumulated time series, the following method is applied to improve the forecasting accuracy. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The forecasting result is compared with those of the non-cumulative forecasting method. The new method shows that it is useful for the forecasting of intermittent demand data.
Kazuhiro Takeyasu,Hirotake Yamashita,Daisuke Takeyasu 대한산업공학회 2015 대한산업공학회 추계학술대회논문집 Vol.2015 No.11
In Supply Chain Management, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. “a day of the week index” is newly introduced to the daily shipping data of sanitary materials and we have obtained good result.