Behnamian, J. Ghomi, S.M.T. Fatemi Karimi, B. Moludi, M. Fadaei
Year of Publication:
[Journal:] Journal of Industrial Engineering International [ISSN:] 2251-712X [Volume:] 10 [Year:] 2014 [Pages:] 1-9
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.
Uncertainty First-order Taylor series expansion State space models Most probable point Forecasting practice Demand forecasting