Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/157403
Authors: 
Behnamian, J.
Ghomi, S.M.T. Fatemi
Karimi, B.
Moludi, M. Fadaei
Year of Publication: 
2014
Citation: 
[Journal:] Journal of Industrial Engineering International [ISSN:] 2251-712X [Volume:] 10 [Year:] 2014 [Pages:] 1-9
Abstract: 
The demand forecasting is essential for all production and non-production systems. However, nowadays there are only few researches on this area. Most of researches somehow benefited from simulation in the conditions of demand uncertainty. But this paper presents an iterative method to find most probable stochastic demand point with normally distributed and independent variables of n-dimensional space and the demand space is a nonlinear function. So this point is compatible with both external conditions and historical data and it is the shortest distance from origin to the approximated demand-state surface. Another advantage of this paper is considering ndimensional and nonlinear (nth degree) demand function. Numerical results proved this procedure is convergent and running time is reasonable.
Subjects: 
Uncertainty
First-order Taylor series expansion
State space models
Most probable point
Forecasting practice
Demand forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/2.0/
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.