2008 Volume 44 Issue 2 Pages 174-182
In the present paper, a dynamic programming (DP) approach for elevator operation problems is reported, in which two kinds of the design of DP algorithms are introduced. One (Value Iteration; VI) is rather naive imple-mentation of DP concept to the problems, and the other (Dynamic Programming on Reduced Models; DPRM) isan improved version in computational efficiency by reducing the accuracy of the utilized state transition model. The basic idea of this reduction is as follows: it is expected that dropping some situational-inputs, which rep-resent the possible causes for each state transition, with low occurring probabilities may reduce the size of the state transition model without losing the essence of a system's behavior.
In computational illustrations, the VI and the DPRM are applied to two types of the problems of small sized, and it is observed that the computational time required in applying the DPRM is much less than that in applying the VI for each problem, while the results obtained by both methods are almost equal with respect to not only averaged but also maximum waiting time over passengers. Furthermore, the optimal and quasi-optimal plicies obtained by the VI and DPRM are compared with an existing elevator allocation rule (Call Dispatching; CD), and the results show both policies are statistically better than the CD.
