An iterative method for forecasting most probable point of stochastic demand

Subject Areas : Mathematical Optimization

J. Behnamian ¹ , S. M. T. Fatemi Ghomi ² , B. Karimi ³ , M. Fadaei Moludi ⁴

1 - Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
2 - Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 15916-34311, Tehran, Iran
3 - Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 15916-34311, Tehran, Iran
4 - Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 15916-34311, Tehran, Iran

Received: 2020-10-02 Accepted : 2020-10-02 Published : 2014-06-01

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

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.