Model-approximated dynamic programming based on decomposable state transition probabilities

In this paper, a discrete model of an elevator system and a design of dynamic programming (DP) method based on that model are shown. The method is followed by a modified DP method on approximated state transition models. The discrete model theoretically considers the causes of a system´s probabilistic behavior, and leads such approximation by the reduction of the insignificant constituent of them. In computational illustrations, the two DP methods are applied to two problems of small scale. The results show that the policies obtained by DP methods are both statistically better than an existing elevator allocation rule, and the modified method requires less computational time than the another.