HIZLI FOURIER DÖNÜŞÜMÜ ALGORİTMASI İLE DÜZENSİZ TALEP ALTINDA İŞ YÜKÜ ÖNGÖRÜSÜ

Öz

Düzensiz talep altında öngörüde bulunma oldukça zahmetli bir süreç olup, klasik öngörü yöntemlerinden verim almak oldukça zordur. Bu çalışmada incelenen firmanın üretim talep yapısı da düzensiz seyretmektedir ve firma vazgeçilmez süreç geliştirme faaliyetlerini planlayabilmek için iş yükü öngörüsüne ihtiyaç duymaktadır. Öngörülecek iki parametre toplam üretim hacmi ve fason üretim miktarının toplam üretime oranı şeklinde belirlenmiştir. Göreceli daha düzenli bir yapı sergileyen fason üretim oranı öngörüleri durağan öngörü yöntemiyle gerçekleştirilmiştir. Daha kritik olan düzensiz toplam üretim miktarı ise Hızlı Fourier Dönüşüm algoritmasıyla hesaplanmıştır. Toplam üretim parametresi ayrıca tahmin güvenilirliği ölçümü amacıyla bir yıl boyunca gözlenmiştir. Gözlenen Ortalama Mutlak Hata Yüzdeleri %11 (fason üretim oranı) ve %14-%22 (toplam üretim miktarı) olarak hesaplanmış ve düzensiz talep göz önüne alındığında kabul edilebilir olarak değerlendirilmiştir. 

Anahtar Kelimeler

• Öngörü,düzensiz talep,kesikli fourier dönüşümü

WORKLOAD FORECASTING UNDER ERRATIC DEMAND USING FAST FOURIER TRANSFORM ALGORITHM

Öz

Forecasting process may become cumbersome under erratic demand structures where no conventional methodology seems to provide plausible outcomes. The company investigated in this paper is subject to this condition and needs to forecast the workload in order to plan the indispensable process enhancement activities. Two parameters to be forecasted are the total production volume and the ratio of in-house production ratio with respect to the total production. The required forecasts for relatively regular structured in-house production to total production ratio were computed via static forecasting. The more important erratic structured total production volume forecasts were computed using FFT (Fast Fourier Transform) algorithm. Total production was further observed during the next year to check the reliability of the forecasts. The resulting respective MAPEs (Mean Absolute Percentage Errors) of approximately 11% (in-house ratio) and 14-22% (total production) were considered acceptable under the erratic demand structure.

Anahtar Kelimeler

• Forecasting,erratic demand,discrete fourier transform

Kaynakça

1. Amjady, N., Keynia, F., Zareipour, H., 2010, Short-term load forecast of microgrids by a new bilevel prediction strategy, IEEE Transactions on Smart Grid, 1 (3), 286-294.
2. Beiraghi, M., Ranjbar, A.M., 2011, Discrete Fourier transform based approach to forecast monthly peak load. Asia-Pacific Power and Energy Engineering Conference, 1 – 5.
3. Bitaraf, H., Rahman, S., Pipattanasomporn, M., 2015, sizing energy storage to mitigate wind power forecast error impacts by signal processing techniques. IEEE Transactions on Sustainable Energy, 6 (4), 1457-1465.
4. Bracewell, R., 1965, The Fourier Transform and its applications. McGraw-Hill.
5. Brentan, B.M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., Pérez-García, R., 2017, Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541.
6. Dong, S., Sun, L., Chang, T., Lu, H., 2011, Combined short-term traffic flow forecast model for Beijing traffic forecast system. Proceedings of the 14th International IEEE Conference on Intelligent Transport Systems. 638-643.
7. Ferbar Tratar, L., Mojškerc, B., Toman, A., 2016, Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162-173.
8. Ferbar Tratar, L., Strmčnik, E., 2016, The comparison of Holt-Winters method and multiple regression method: A case study. Energy, 109, 266-276.Fumi, A., Pepe, A., Scarabotti, L., Schiraldi, M.M., 2013, Fourier analysis in demand forecasting in a fashion company. International Journal of Engineering Business Management, 5, 1-10.

9. González-Romera, E., Jaramillo-Morán, M.A., Carmona-Fernández, D., 2008, Monthly electric energy demand forecasting with neural networks and Fourier series. Energy Conversion and Management, 49, 3135-3142.
10. Ichinose, T., Hirobayashi, S., Misawa, T., Yoshizawa, T., 2012, Forecast of stock market based on nonharmonic analysis used on NASDAQ since 1985. Applied Financial Economics, 22, 197-208.
11. Kochak, A., Sharma, S., 2015, Demand forecasting using neural network for supply chain management. International Journal of Mechanical Engineering and Robotics Research, 4 (1), 96-104.
12. Malliavin, P., Mancino, M.E., 2002, Fourier series method for measurement of multivariate volatilities. Finance and Stochastics, 6, 49-61.
13. Mollina, A., Ponte, B., Parreño, J., De la Fuente, D., Costas, J., 2016, Forecasting erratic demand of medicines in a public hospital: A comparison of artificial neural networks and ARIMA models. Proceedings of the International Conference on Artificial Intelligence, 401-406.
14. Onyeocha, C.E., Khoury, J., Harik, R.F., 2014, Evaluation of forecasting and inventory control in multi-productmanufacturing systems operating under erratic demand: a case study in the automotive domain. Computer Science and Applications, 1 (1), 31-47.
15. Shang, K.H., 2012, Single-stage approximations for optimal policies in serial inventory systems with nonstationary demand. Manufacturing & Service Operations Management, 14 (3), 414-422.
16. Song, J. S., Zipkin, P., 1993, Inventory control in a fluctuating demand environment. Operations Research, 41 (2), 351–370.
17. Treharne, J.T., Sox, C.R., 2002, Adaptive inventory control for nonstationary demand and partial information. Management Science, 48, 607-624.
18. Yu, X., Zhang, X., Qin, H., 2018, A data-driven model based on Fourier transform and support vector regression for monthly reservoir in flow forecasting. Journal of Hydro-environment Research, 18, 12-24.
19. Yukseltan, E., Yucekaya, A., Bilge, A.H., 2017, Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation. Applied Energy, 193, 287-296.
20. Van Loan, C., 1992, Computational frameworks for the Fast Fourier Transform. SIAM.
21. Zhang, R., Bao, Y., Zhang, J., 2010, Forecasting erratic demand by support vector machines with ensemble empirical mode decomposition. Proceedings of the 3rd International Conference on Information Sciences and Interaction Sciences, 567-571.
22. Zong-chang, Y., 2013, Fourier analysis-based air temperature movement analysis and forecast. IET Signal Processing, 7 (1), 14-24.