A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm: An Application to the Data of Processed Cooked Rice

Takeyasu, Hiromasa; Higuchi, Yuki; Takeyasu, Kazuhiro

doi:10.7232/iems.2013.12.3.244

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…From (4) (5) (8) (11), we get (12) If we set (13) the following equation is derived (14) We can get as follows (15) In order to have real roots, must satisfy (16) From invertibility condition, must satisfy…”

Section:  (Ijacsa) International Journal Of Advanced Computer Sciencmentioning

confidence: 99%

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A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm –An Application to the Data of Operating equipment and supplies

Takeyasu¹,

Takeyasu²

2013

IJACSA

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Abstract-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. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Generally, smoothing constant is selected arbitrarily. But in this paper, we utilize above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants. Thus theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removing by the combination of linear and 2 nd order non-linear function and 3 rd order non-linear function is executed to the data of Operating equipment and supplies for three cases (An injection device and a puncture device, A sterilized hypodermic needle and A sterilized syringe). The weights for these functions are set 0.5 for two patterns at first and then varied by 0.01 increment for three patterns and optimal weights are searched. 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.

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Section:  (Ijacsa) International Journal Of Advanced Computer Sciencmentioning

confidence: 99%

“…From (14), using the next relation, (17) which satisfies above condition. Thus we can obtain a theoretical solution by a simple way.…”

Section:  (Ijacsa) International Journal Of Advanced Computer Sciencmentioning

confidence: 99%

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm –An Application to the Data of Operating equipment and supplies

Takeyasu¹,

Takeyasu²

2013

IJACSA

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A Hybrid Method to Improve Forecasting Accuracy in the Case of Sanitary Materials Data

Takeyasu¹,

Yamashita²,

Takeyasu³

2014

IJACSA

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Sales forecasting is a starting point of supply chain management, and its accuracy influences business management significantly. In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. In this paper, a hybrid method is introduced and plural methods are compared. Focusing that the equation of exponential smoothing method (ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by Takeyasu et.al. which satisfies minimum variance of forecasting error. Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants. In this paper, combining the trend removing method with this method, we aim to improve forecasting accuracy. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order nonlinear function is carried out to the manufacturer's data of sanitary materials.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.

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Genetic Algorithm-Based Coordinated Replenishment in Multi-Item Inventory Control

Nagasawa¹,

Irohara²,

Matoba³

et al. 2013

Industrial Engineering and Management Systems

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We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

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A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm: An Application to the Data of Processed Cooked Rice

Cited by 6 publications

References 11 publications

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm –An Application to the Data of Operating equipment and supplies

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm –An Application to the Data of Operating equipment and supplies

A Hybrid Method to Improve Forecasting Accuracy in the Case of Sanitary Materials Data

Genetic Algorithm-Based Coordinated Replenishment in Multi-Item Inventory Control

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