Forecasting Optimal Production Program Using Profitability Optimization by Genetic Algorithm and Neural Network
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32769
Forecasting Optimal Production Program Using Profitability Optimization by Genetic Algorithm and Neural Network

Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

Abstract:

In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network.

The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters.

Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output.

This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc.

From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: Project profitability, multi-objective optimization, genetic algorithm, Pareto set, Neural Networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1092473

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2009

References:


[1] Hiew, M. and G. Green, "Beyond Statistics. A Forecasting System That Learns," The Forum, 1992, Vol. 5, pp. 1 and 6.
[2] Hawley, D.D., Johnson, J.D. and Raina, D. (1990), "Artificial Neural Systems: A New Tod for Financial Decision-Making," Financial Analysts Journal, (November-December) 63- 72.
[3] Huntley, D.G. (1991), "Neural Nets: An Approach to the Forecasting of Time Series," Social Science Computing Review, 9 (1), 27-38.
[4] Rumelhart, D. and J. McClelland, Parallel Distributed Processing, Cambridge: MIT Press, 1986.
[5] Wasserman, P.D., Neural Computing: Theory and Practice, Van Nostrand Reinhold: New York, 1989.
[6] Hornik, K., M. Stinchcombe, and H. White, "Multilayer Feed forward Networks are Universal Approximators," Neural Networks, 1989, 2(5), 359-366.
[7] Connor, 1988; Donaldson, Kamstra and Kim, 1993, "Artificial neural network models for forecasting and decision making”.
[8] Kang, S., An Investigation of the Use of Feed forward Neural Networks for Forecasting, Ph.D. Dissertation, Kent State, 1991.
[9] Marquez, L., Function Approximation Using Neural Networks: A Simulation Study, Ph.D. Dissertation, University of Hawaii, 1992.
[10] B. Scholz-Reiter, T. Hamann, H. Höhns, and G. Middelberg, ‘Decentral closed loop control of production systems by means of artificial neural networks’, in Proceedings of the 37th CIRP-International Seminar on Manufacturing Systems, pp. 199 – 203, (2004).
[11] S.F. Crone, ‘Forecasting in inventory management using artificial neuronal networks - a novel approach through asymmetric cost functions’, in Einsatz von Fuzzy-Sets, Neuronalen Netzen und Künstlicher Intelligenz in industrieller Produktion und Umweltforschung, 59 – 69, VDI - Verlag, Düsseldorf, (2003).
[12] N. Wang and J. Yu, ‘Neuron based nonlinear pid control’, PRICAI 2006: Trends in Artificial Intelligence, 4099, 1089–1093, (2006).