Abstract
In this paper, we show an approach to acquire a rule which is capable of achieving effective elevator services. The allocation of elevators to hall calls is taken as the decision variable and the Pitt approach of a Genetics-Based Machine Learning (GBML) method is designed according to the representation and the characteristics of the problem. We use a rule architecture in which a combination of some measures is directly encoded as a unit-rule and an elevator allocation procedure is represented as a rule set composed of some unit-rules. Five measures are developed based on the results of the preliminary experiment and it is shown that the rule as the weighted summation of the measures (called WS) is more effective than a conventional elevator allocation rule. In computational experiments, the up-peak, down-peak and two-way traffic patterns are considered and three rule sets are acquired by applying the GBML method. Based on the results, we can confirm that the GBML method can acquire some unit-rules appropriate for the targeting traffic patterns, and we can mention that, for some presumed traffic patterns, the possibility of constructing an effective rule set by combining some specific rule sets.
