An iterative method for forecasting most probable point of stochastic demand

Journal of Industrial Engineering International

دوره 10، شماره 2، شهریور 2014   

نویسندگان

J. Behnamian¹؛ S. M. T. Fatemi Ghomi²؛ B. Karimi²؛ M. Fadaei Moludi²

¹Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

²Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 15916-34311, Tehran, Iran

چکیده

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.

کلیدواژه‌ها

Uncertainty  First؛ order Taylor series expansion  State space models  Most probable point  Forecasting practice  Demand forecasting