ADJUSTMENT OF STOCHASTIC STOCK MODELS WITH LEARNING

Abstract

The algorithms handling the problems of inventory control have already been developed relatively early; the realization of the optimal inventory policy for big production systems and for the trading is helped by known mathematical models. Creation of a mathematical model produces the constraints determining the limits of its validity. If the constraints correspond to the system to be modelled, the parameters of the system can be set on the base of the model. What happens if the conditions determined during the modelling process are not fulfilled or there is no a priori knowledge of the effects influencing the system? This question occurs when the store supply cannot be scheduled and/or the change of the demand is unknown, even the distribution cannot be determined in advance. In the paper we are dealing with a possible solution for the problem of stock manage- ment in the case of unknown and/or unidentifiable input and output effects. The method proposed is the stochastic approximation. Its application possibility will be shown for a periodic stochastic stock control model provided the stationarity of the processes. The requirement of periodicity and stationarity is not strict. The principle of generating the model can be used for other cases, too, e. g. for the solution of nonperiodic problems. We will show how the method can be applied for following the slow changes in nonstationary cases.