The idea of multicar operation within one hoistway is not new. Two-car systems are currently available on the market, whereby the two cars travel with restricted independence because one car must always remain above the other. With recent advances in linear machines, systems with more than two cars in one hoistway will soon become possible.
In this paper, the authors go one step forward by assuming that multiple cars can move in a two-dimensional plane either attached to the facade of the building or across a vertical slice within the building. The analysis has been restricted at this stage to incoming traffic only. It is assumed that elevator cars can move upwards, downwards as well as sideways. In this way, passengers can exit at a stop very close to their destinations. The foreseeable technology is discussed, and two configurations (denoted as setups A and B) are proposed. The traffic analysis equations for such a system are also derived. A simulation is then carried out for the two setups based on one-car operation. The simulation shows that the proposed two-dimensional elevator system can reduce the total traveling time of a passenger as compared with the conventional one-dimensional setup. The system is described as special because the number of hoistways is restricted (up to a maximum of 2).
Practical application: This paper provides a practical way of evaluating the round trip time for two different two-dimensional elevator applications. It also then compares three different sizes of buildings and shows that the use of two-dimensional elevator arrangements is only feasible for building with more than 30 floors high by 30 rooms wide.
While the car of the conventional elevator system moves only vertically in one dimension (up and down), the car of the three-dimensional elevator system travels in three perpendicular dimensions. The elevator moves through a vertical shaft to a certain floor and then the elevator serves multiple passengers distributed among different rooms at that floor. The controller decides which route should be taken to serve the passengers. This article proposes the use of deep reinforcement learning to select a route for the three-dimensional elevator. Deep reinforcement learning method learns from experiencing a large number of scenarios generated using Monte Carlo simulation offline. Once trained, deep reinforcement learning can select the route online. Numerical experimentations are used to show the superiority of deep reinforcement learning in finding an optimum or near optimum-route instantaneously. Although deep reinforcement learning is closer to finding the optimum route than other methods, finding an optimum route is not always guaranteed. Deep reinforcement learning has some limitations that include the long training time and the difficulties in training the neural networks. Practical application:Multidimensional elevators have been of expanding interest to the elevator industry as well as to traffic analysis engineers. This article demonstrates that deep reinforcement learning surpasses other methods in finding an optimum or near-optimum route for the three-dimensional elevator, and it also overcomes the challenges of the non-intelligent methods. This article can help enterprises that develop multidimensional elevators in overcoming the challenges of the controller in addition to boosting the feasibility of multidimensional elevators.
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