Ant Colony Optimization for single car scheduling of elevator systems with full information

We concentrate on the single car, full information elevator problem. Here ldquofull informationrdquo means that the arrival time, the origins and destinations of passengers are all assumed known beforehand. The importance of studying full information problem lies in that we can know the value of the future information and evaluate the existing scheduling methods for the elevator system. We aim to find the best solution of serving the passengers, and the performance is measured by the average service time. The problem is modeled into a graph and our goal is converted to finding a path on the graph corresponding to the best performance. An algorithm of ant colony optimization (ACO) is applied. Different from applying ACO to solve the traveling salesman problem, we have the time factor and the constraints in our problem. The modeling of the problem and the handling of the constraints are the contributions of this paper. Our method is compared with branch and bound method (BB) which can obtain the optimal solution and a popular heuristic rule in the literature named selective collective operation. The results of our ACO on small scale problems are very near to optimal, and for middle and large scale problems, our results are much better than the selective collective operation. These results show the effectiveness of our ACO.