On solving single elevator-like problems using a learning automata-based paradigm

Abstract

This paper concentrates on a host of problems with characteristics similar to those that are related to moving elevators within a building. These are referred to as Elevator-like problems (ELPs), and their common phenomena will be expanded on in the body of the paper. We shall resolve ELPs using a subfield of AI, namely the field of learning automata (LA). Rather than working with the well-established mathematical formulations of the field, our intention is to use these tools to tackle ELPs, and in particular, those that deal with single “elevators” moving between “floors”. ELPs have not been tackled before using AI. In a simplified domain, the ELP involves the problem of optimizing the scheduling of elevators. In particular, we are concerned with determining the elevators’ optimal “parking” location. In our case, the objective is to find the optimal parking floors for the single elevator scenario, so as to minimize the passengers’ average waiting time (AWT). Apart from proposing benchmark solutions, we have provided two different novel LA-based solutions for the single-elevator scenario as the multi-elevator setting is more complicated. The first solution is based on the well-known $$L_{RI}$$ scheme, and the second solution incorporates the Pursuit concept to improve the performance and the convergence speed of the first solution, leading to the $$PL_{RI}$$ scheme. The simulation results presented demonstrate that our solutions performed better than those used in modern-day elevators, and provided results that are near-optimal, yielding a performance increase of up to 90%. The solutions presented for real elevators are directly applicable for the entire family of ELPs.