Explore the vast range of titles available.

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

Energy-saving elevator dispatching has been recognized as a challenging issue in building transportation, and we develop a novel energy-saving dispatching strategy for regenerative group-elevator system. Group-elevator dispatching is a typical combinatorial optimization problem, and three keys of the dispatching optimization are optimization method, objective, and model. The three keys of energy-saving-oriented elevator dispatching are studied in this paper. First, robust optimization method is introduced to handle dispatching optimization under uncertain elevator traffic flows; uncertain flows influence energy-saving dispatching seriously. Second, dispatching energy-objective function for regenerative group-elevator system is derived both schedule energy for four traffic patterns (up-peak, down-peak, up/down-mixed, and night) and return energy for two peak patterns (up-peak and down-peak) are considered. Third, four robust optimization-dispatching models for four traffic patterns are built, and optimization objectives of four models are minimizing the energy-objective function. Moreover, because we cannot solve robust optimization models with uncertain parameters directly, model counterpart transformation is studied. Finally, we solve the four transformed models by Linear Interactive and General Optimizer software and obtain robust optimization-dispatching solutions. In practice, four energy-saving-dispatching robust optimization models are switched according to real-time traffic patterns, and elevators are dispatched based on the dispatching solutions. We reduce elevator system-energy consumption effectively and keep average waiting time of the passengers acceptable under multi-traffic patterns. Simulation results demonstrate the validity of our strategy. Practical application: Group-elevator system spends much unnecessary energy because of the uncertainty of elevator passenger-traffic flows. This paper develops an energy-saving elevator-dispatching optimization strategy, which is immune to the uncertainty of four typical traffic flows. In practice, we update the group-elevator controller by our algorithm to realize energy-saving dispatching of regenerative group-elevator system under multi-traffic patterns.

The conventional elevator arrives at the floor in response to the user request and where is to dispatch an elevator car. There is one big problem that when system dispatches the elevator to the button responding floor, but corridor passenger is no there. In this paper, we present a novel elevator group control algorithm based on binocular-cameras corridor passenger detection and tracking, aiming to overcome the difficulty in the conventional elevator group control system. In the above proposed system, the corridor passengers are detected using Haar-like features based on binocular-cameras, and Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. A new elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator service. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation and realistic condition.

In this work fuzzy logic based controller for Elevator Group Control System (EGCS) is proposed. The most complex problem is to control the multi-elevator in a multi-storey building and there are different approaches to solve the problem. The approach used based on the Simulink model and the fuzzy logic controller which is proposed to choose the most appropriate elevator for a hall call generated by passenger/user. The simulation was carried out by giving the input which was used to calculate the input for fuzzy logic controller. The data obtained from the simulation model and the input source provided to the fuzzy based controller priority of each elevator car can be calculated. The maximum priority elevator car is responded for its hall call operation. The input was based on hall call assignment method. This work can assist in analyzing the performance of the Elevators.

Elevators are the essential transportation tools in high buildings so that Elevator Group Control System (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rules based intelligent elevator group control system has been proposed in which the structure of rules including the related parameters are generated optimally based on the traffic data so as to maximize service quality. In literature, the fuzzy related approaches have been applied in EGCS but the fuzzy rules were all pre-defined. However, how to create the most suitable fuzzy rule set in EGCS for dispatching elevators more efficiently and economically are never discussed in literature. The aim of the proposed approach is to minimize the average waiting time at peak hours as well as to minimize the power energy at off-peak hours by using the proposed fuzzy rule based ECGS. Moreover, there are many decision variables are considered in the GCGS to provide the most appropriate elevator assignment whenever any hall call is given. These variables include the number of elevators, traffic flow, direction, passenger preferences (for instance, department stores, hospitals, hotels, and office buildings), congestion and VIP priority floor, etc. In this study, a fuzzy rule based elevator-dispatching approach has been proposed for the EGCS in which the fuzzy rules and related parameters are derived optimally by using genetic algorithm based on the historical elevator transportation data. The experimental results show that the performance of the proposed approach is superior to these of traditional approaches in literatures.

The conventional elevator group control system is based on the button as a response and where to dispatch an elevator car. There is one big problem that when system dispatches the elevator to that button responding floor, but no passenger there and causing a waste of time and energy. In this paper, we present a novel intelligent elevator group control algorithm based on corridor passenger detection and tracking. In the above proposed system, after the corridor passengers are detected using binocular-cameras, Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. At same time, a novel intelligent elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation condition.

What the group control of the elevator traffic system deal with is a complex, random, multi-objective, nonlinear, uncertain decision-making problem. Good effect of controlling elevators can’t be achieved with traditional control methods. Elevator group control system based on fuzzy single chip computer is introduced in this paper. Fuzzy inference can be carried out based on control rules that are programmed based experts’ experience. Then elevators are allocated in the light of inference result in order to achieve ideal control effect.

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal process. This paper analyzes both the advantages and the disadvantages of artificial immune algorithm and gradient descent algorithm, optimizes artificial immune algorithm, then proposes a novel optimal hybrid algorithm; at the same time, uses this hybrid algorithm in the elevator group control system combined with Pareto solution set. Making a comparison between the hybrid algorithm and the standard artificial immune algorithm, it’s clear that this hybrid algorithm has certain feasibility and superiority, and to some extent, has improved the overall performance and service quality of the elevator group control system. This paper has provided a new method and a new thought on determination of the multi-objective weighted values in the elevator group control system.

A multi-car elevator system is an elevator system that has more than one car installed in each elevator shaft. This system enables us to improve the transportation capability without increasing the occupied floor space. The primary purpose of this study is to consider the group control problem of operating cars efficiently without collision nor reversal in a multi-car elevator system, based on a detailed and realistic model where floor stoppage time of cars cannot be known in advance. In the context of elevator systems, reversal means that a car travels in the direction opposite to the desired direction of on-board passengers, which is prohibited because it makes passengers uncomfortable. We first propose an optimization-based collision and reversal avoidance method to operate cars in the same shaft. Next, we construct simple methods to allocate calls to individual cars under immediate and delayed guidance policies. Under the immediate guidance policy a call is allocated to a car immediately after it is registered, while under the delayed guidance policy the allocation may be changed until it is actually served. The effectiveness of the proposed group control method is examined by computer simulation.