Document Type : Original Article (Quantified)

Authors

1 1 Department of Management, Ayendang Institute of Higher Education, Tonkabon, Iran

2 Management, Ayendag Institute of Higher Education, Tonkabon, Iran

Abstract

In this article, how to use the artificial neural network metamodel to analyze the sensitivity of the order economic value model in the case of two-factor interactive effects is presented and it is shown that the use of this metamodel for the sensitivity analysis of the economic value of the order compared to the current method (one factor in each load) is more suitable. For this purpose, a back-propagation forward neural network with a hidden layer, sigmoid driving functions in the hidden layer, factors affecting EOQ as input and economic order value as output of the model have been used. The sensitivity criteria are defined based on connection weights and the methodology procedure is shown in a numerical example. With the emergence of intelligent systems, data processing and models related to them such as artificial neural networks, genetic algorithm, fuzzy logic and the like, which are designed and modeled with inspiration from a corner of nature, an important progress has been made in data analysis. One of the main features of the network Artificial neural networks, inspired by the structure and function of natural neural networks, are parallel processing of input information by neural processing units.

Keywords

سرور خواه. علی، متدولوژی تحلیل حساسیت EOQ با استفاده از شبکه‌های عصبی مصنوعی، پایان نامه کارشناسی ارشد، 1389، 81-78
سید حسینی. محمد، صفاکیش. سعید، مبانی جامع و پیشرفته مدیریت تولید و عملیات، جلد دوم، انتشارات سازمان مدیریت صنعتی، تهران،1384،-55-53
Cybenco, G. (1989), "Approximation by superpositions of a sigmoidal function", Mathematics of control,Signals and Systems, 2, 303-314.
Harris, F. (1915), "Operations and Cost factory management series, A.W. Shaw Co, Chicago, 8-52.
Kilmer, R. A., and Smith, A. E., and Shuman, L. J. (1994), "Neural network as a metamodelling technique for discrete even stochastic simulation", Intelligent Engineering Systems Through Artificial Neural Networks, Vol. A, 631-636.
Kleijnen, J. P., and Sargent, R.G. (2000), "A methodology for fitting and validating metamodels in simulation", European journal of operational research, 120, 14-29.
Liao, S. H. (2005), "Expert system methodologies and applications:A decade review from 1995 to 2004, Expert systems with applications, 28(1), 93-1 03.
Miesel,W. S., and Collins, D. C. (1973), "Repro-modelling approach to efficient model utilization and interpretation",IEEE transaction of systems,Man and Cybemetics,SMC, 3, 349-358.
Olden, J. D., and Joy, K., and Death, R.G. (2004), "An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data", Ecological Modeling, 78, 389-397.
Padget, M., and Roppel,T. A. (1996), "Neural network and simulation:modeling for applications", Mathematical and Computer modeling, 23, 91-99.
Pierval, H., and Hunstiger, R. C., (1989), "An investigation on neural networks capabilities as simulation metamodels", Proceeding of the 1989 winter simulation conference,702-710.
Rumelhart, D. E., and Hilton, G. E., and Williams, R. J., (1986), "Learning representations by backpropagation error", Nature, 323,PP. 533-536.
 Sartio, D. E., and Smith, A. E. (1997), "A metamodel approach to sensitivity analysis of capital project valuation",The Engineering
Economic, 43(1), 1-24.
Wray, B., and Palmer, A., and Bejou, D. (1994), "Using neural network analysis to evaluate buyer-seller relationships", European Journal of Marketing, 28(l) 32-48.