Optimization of Forecasting Moving Average Error in Probabilistic Demand Using Genetic Algorithm Based Fuzzy Logic

Chairul Saleh; Muhammad Ridwan Andi Purnomo; Hayati Mukti Asih

doi:10.4028/www.scientific.net/AMR.576.710

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 576Optimization of Forecasting Moving Average Error...

Optimization of Forecasting Moving Average Error in Probabilistic Demand Using Genetic Algorithm Based Fuzzy Logic

Abstract:

Demand forecasting is one of the most critical factors in production planning. The uncertain demand, which is the basic idea of planning the production level, nowadays is one of serious problems. The inaccurate demand forecasting could affect to excess production or shortage stocks which led to lost sales. Usually, the forecasted result is hard to represent real condition. Some studies already conducted related to fuzzy time series, each of them has its own advantages and disadvantages compared to other approaches. This research presents the combination of simple moving average forecasting and fuzzy logic model to demand forecast. Then, genetic algorithm (GA) is applied to optimize the mean square error (MSE) inside the fuzzy system. The MSE before and after GA optimization is 0,2192 and 0,1821, respectively.

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Periodical:

Advanced Materials Research (Volume 576)

Pages:

710-713

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.576.710

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Online since:

October 2012

Authors:

Chairul Saleh, Muhammad Ridwan Andi Purnomo, Hayati Mukti Asih

Keywords:

Fuzzy Logic, Genetic Algorithm (GA), MSE, SMA

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References

[1] L. Aburto and R. Weber. Improved Supply Chain Management based on Demand Forecast. Applied Soft Computing. 7 (2007) 136 - 144.

DOI: 10.1016/j.asoc.2005.06.001

Google Scholar

[2] F. Campuzano, J. Mula and D. Peidro, Fuzzy Estimation and System Dynamics for Improving Supply Chains. Fuzzy Sets and System, 161 (2010) 1530 - 1542.

DOI: 10.1016/j.fss.2009.12.002

Google Scholar

[3] L.B. Mendelsohn, Trend Forecasting With Technical Analysis: Unleashing the Hidden Power of Intermarket Analysis to Beat the Market. United States of America: Marketplace Books, (2000).

Google Scholar

[4] A.K., Palit and D. Popovic, Computational Intelligence in Time Series Forecasting: Theory and Engineering Application. United States of America: Springer-Verlag London, (2005).

Google Scholar

[5] D. Peidro, J. Mula, R. Poler. and J. Veerdegay, Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems. 160 (2009) 2640 - 2657.

DOI: 10.1016/j.fss.2009.02.021

Google Scholar

[6] M. Negnevitsky, Artifial Intelligence: A Guide to Intelligent System. England: Pearson Education, (2002).

Google Scholar

[7] Goldberg, E. David, Genetic Algorithm: In Search, Optimization and Machine Learning. Boston: Addison Wesley, (1989).

Google Scholar