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Showing 3 results for Sadeghian

R. Sadeghian, G.r. Jalali-Naini, J. Sadjadi, N. Hamidi Fard ,
Volume 19, Issue 4 (IJIE 2008)
Abstract

  In this paper Semi-Markov models are used to forecast the triple dimensions of next earthquake occurrences. Each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. Semi-Markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. In Semi-Markov models each zone can be considered as a state of proposed Semi-Markov model. At first proposed Semi-Markov model is explained to forecast the three mentioned dimensions of next earthquake occurrences. Next, a zoning method is introduced and several algorithms for the validation of the proposed method are also described to obtain the errors of this method.


Ramin Sadeghian,
Volume 21, Issue 1 (IJIEPR 2010)
Abstract

The Materials Requirements Planning (MRP) method that is applied in production planning and management has some weaknesses. One of its weaknesses is that the time in MRP method is discrete, and is considered as time period. Hence we are not able to order our requirements at irregular time moments or periods. In this paper, a new form of MRP is introduced that is named Continuous Materials Requirements Planning (CMRP) approach. We discuss the disadvantages of Discrete MRP (DMRP) approach and analyze the conditions, applications and the manner of applying CMRP approach in our problems.
Ramin Sadeghian,
Volume 27, Issue 2 (IJIEPR 2016)
Abstract

Generally ordering policies are done by two methods, including fix order quantity (FOQ) and fix order period (FOP). These methods are static and either the quantity of ordering or the procedure of ordering is fixing in throughout time horizon. In real environments, demand is varying in any period and may be considered as uncertainty. When demand is variable in any period, the traditional and static ordering policies with fix re-order points cannot be efficient. On the other hand, sometimes in real environments some costs may not be well-known or precise. Some costs such as holding cost, ordering cost and so on. Therefore, using the cost based inventory models may not be helpful. In this paper, a model is developed which can be used in the cases of stochastic and irregular demand, and also unknown costs. Also some attributes consisting of expected positive inventory level, expected negative inventory level and inventory confidence level are considered as objective functions instead the objective function of total inventory cost. A numerical example is also presented for more explanation.



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