In this paper, the authors propose an integer linear programming (ILP) model for static multi-car elevator operation problems. Here, “static” means that all information which make the behavior of the elevator system indeterministic is known before scheduling. The proposed model is based on the trip-based ILP model for static single-car elevator operation problems. A trip of an elevator is a one-directional movement of that elevator, which is labaled upward or downward. In the trip-based ILP model, an elevator trajectory is scheduled according to decision variables which determine allocations of trips to users of an elevator system. That model has such an advantage that the difficulty in solving ILP formulations resulted by that model does not depend on the length of the planning horizon nor the height of the considered building, thus is effective when elevator trajectories are simple. Moreover, that model has many variables relevant to elevators' positions. The proposed model is resulted by adding 3 constraints which are basically based on those variables and make it possible to prevent elevators in a same shaft from interfering. The first constraint simply imposes the first and last floors of an upper trip to be above those of its lower trip. The second constraint imagines the crossing point between upper and lower trips and imposes it ahead of or behind the lower trip according to their directions. The last constraint estimates future positions of elevators and imposes the upper trip to be above floors of passengers on the lower trip. The basic validity of the proposed model is displayed by solving 90 problem instances and examining elevator trajectories generated from them, then comparing objective function values of elevator trajectories on a multi-car elevator system with those on single-car elevator systems.

In this letter, we introduce a knowledge reuse method to improve the performance of a learning algorithm developed to prevent interference in multi-car elevators. This method enables the algorithm to use its previously acquired experience in new learning processes. The simulation results confirm the improvement achieved in the algorithm's performance.

In this letter, we present the evaluation of an option-based learning algorithm, developed to perform a conflict-free allocation of calls among cars in a multi-car elevator system. We evaluate its performance in terms of the service time, its flexibility in the task-allocation, and the load balancing.

In automated transport applications, the design of a task allocation policy becomes a complex problem when there are several agents in the system and conflicts between them may arise, affecting the system's performance. In this situation, to achieve a globally optimal result would require the complete knowledge of the system's model, which is infeasible for real systems with huge state spaces and unknown state-transition probabilities. Reinforcement Learning (RL) methods have done well approximating optimal results in the processing of tasks, without requiring previous knowledge of the system's model. However, to our knowledge, there are not many RL methods focused on the task allocation problem in transportation systems, and even fewer directly used to allocate tasks, considering the risk of conflicts between agents. In this paper, we propose an option-based RL algorithm with conditioned updating to make agents learn a task allocation policy to complete tasks while preventing conflicts between them. We use a multicar elevator (MCE) system as test application. Simulation results show that with our algorithm, elevator cars in the same shaft effectively learn to respond to service calls without interfering with each other, under different passenger arrival rates, and system configurations.

Recently multi-car elevator (MCE) consisting of several elevator cars in a single elevator shaft received great interest as transportation systems for high-rise buildings. Algorithms for efficiently controlling elevator cars are necessary to put MCEs to practical use. We propose an algorithm for controlling MCEs to reduce passenger-waiting time. A feature of our algorithm is the introduction of a simple function estimating efficiency of passenger transport and a procedure checking assignability of a car. We evaluate the performance of our algorithm using a simulation and show that it performs better compared with a previous algorithm.

Recently, multi-car elevator (MCE) system has captured attention as an effective mean for the improvement of transportation capability in a high-rise building. The MCE has two or more cars in one shaft. In this paper, we propose an algorithm for group controllers of MCE system, and show the effectiveness of our algorithm through computer simulation.

Multi-Car Elevator (MCE) systems, which consist of several independent cars built in the same shaft, are being considered as the elevators of the next generation. In this paper, we present MceSim, a simulator of MCE systems. MceSim is an open source software available to the public, and it can be used as a common testbed to evaluate different control methods related to MCE systems. MceSim was used in the group controller performance competition: CST Solution Competition 2007. This experience has proven MceSim to be a fully functional testbed for MCE systems.