Elevator Group Control System Research Articles

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.

Optimizing the Intelligent Elevator Group Control System by Using Genetic Algorithm

Optimizing the Intelligent Elevator Group Control System by Using Genetic Algorithm

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

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.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

Elevator Group Control for Reducing Total Running Distance

Elevator group control systems decide which car gets allocated to a new hall call in some elevators, when a passenger in the elevator hall pushes the hall call button. The primary purpose of the group control system is to maximize the transport efficiency and reduce the waiting time of passengers. Recently, there has been increasing interest in energy-saving performance. Considerable efforts have been made toward reducing the number of available cars and limiting the use of lights and air conditioners in cars for saving energy; however, this leads to a decrease in the transport efficiency and comfort for passengers.This paper presents an elevator group control method for reducing total running distance. This method is used to calculate the cost from the estimated running distance of a car when the car to be allocated to a new hall call is decided. The weight coefficient of the cost varies dynamically depending on the traffic. Simulation results show that the average waiting time in the conventional method and that in the present method are not significantly different. The improvements in the total running distance and power consumption are also demonstrated.

Application of Multi-Objective Artificial Immune Optimal Algorithm in the Elevator Group Control System

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.

Traffic Regularity Evaluation for Elevator Vertical Traffic System

To evaluate the regularity strength of elevator traffic (RSET) is the prerequisites to efficiently design scheduling strategies of elevator group control system (EGCS). In view of the characteristics of the uncertainty of elevator traffic, a quantitative method to evaluate RSET is proposed by estimating the confidence interval of 5min passenger arrival rate on observed data, which provides important support for forecasting elevator traffic and adjusting elevator group scheduling strategies. Simulation results of an applied example show the validity of the proposed method.

Elevator Group Control System Based on Fuzzy Single-Chip Computer

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.

Technical Transition of Elevator Group Control System

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An Elevator Group Control System With a Self-Tuning Fuzzy Logic Group Controller

This paper presents a new group controller's adaptation mechanism with fuzzy logic for elevator group control system (EGCS) applications. Instead of depending heavily on the predicted passenger traffic pattern for adaptation, the fuzzy logic group controller (FLGC) adjusts itself to suit the system's environment through a self-tuning scheme. The average-waiting-time data that reflect the measured performance results of the EGCS are exploited to tune the membership functions of the input parameters and to modify the fuzzy rule sets employed by the FLGC, thus leading to better controller's performance and improved EGCS service quality. Computer simulation shows satisfactory improvements made in passenger waiting time and passenger riding time as compared to the performance of the conventional group controllers in three traffic peaks. The hardware implementation of the self-tuning FLGC on a TMS320C6416T digital signal processor further demonstrates its practicality.

Adaptive multi-objective optimization based on feedback design

The problem of adaptive multi-objective optimization (AMOO) has received extensive attention due to its practical significance. An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions. In this paper, a feedback structure for AMOO is designed. Moreover, the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions. Finally, the proposed approach is applied to the optimization design problem of an elevator group control system. Simulation results show that AMOO has the best average performance at up-peak traffic profile, and its average waiting time reaches 22 s. AMOO is suitable for various traffic patterns, and it is also superior to the majority of algorithms at down-peak traffic profile.

Optimal Car Dispatching For Elevator Groups Using Genetic Algorithms

Abstract The caz dispatching problem in an elevator group consists of assigning cars to the hall calls at the same time that car call are served. The problem needs to coordinate the movements of individual cars with the objective of operating efficiently the whole group. In this paper, we propose an elevator group control system based on a genetic algorithm which makes use of a novel fitness function to evaluate the individuals. The fitness function allows a quick execution of the algorithm. Tests are provided forvarious types of high-rise buildings to assess the elevator service performance. Comparative simulations show that our genetic algorithm outperforms traditional conventional algorithms developed in the industry. It is important to note that the algorithm is quickly evaluated allowing areal-life implementation.

Multiple allocation algorithm based on machine vision for elevator group control system and simulation system design

To increase the efficiency of elevator group system, the paper introduced a new designed simulation system based on non-stationary Poisson process and Model/View/Controller (MVC) pattern. At the same time, a new dispatch algorithm was proposed based on machine vision to inspect the hall traffic. The proposed algorithm can serve one hall-call by allocating more than one elevator. Finally simulation results show that the new method outperforms the minimum long-waiting method in terms of average waiting time and left-waiting people.

Elevator group-control policy based on neural network optimized by genetic algorithm

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

Development of a self-tuning fuzzy logic controller for intelligent control of elevator systems

The search for an intelligent group controller that can satisfy multi-criteria requirements of an elevator group control system has become a great challenge for researchers. This paper presents the development of an elevator group controller based on fuzzy logic framework with a self-tuning scheme. Instead of basing on predicted traffic patterns to initiate modifications in the control outputs produced, the proposed group controller utilizes average waiting time (AWT) as the measured performance criterion used to adjust the membership functions and to select appropriate fuzzy rule sets, for the generation of suitable control actions. By comparing the measured performance results with the ones desired, better adjustment of the controller can be achieved to further improve the controller's performance. Computer simulation was carried out for three different cases in three traffic peaks. The results showed considerable overall improvements in the performance criteria evaluated as compared to the performance of conventional group controllers.

A Study of Double-Deck Elevator Systems Using Genetic Network Programming with Reinforcement Learning

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator systems (DDES) is developed as one of the next generation elevator group control systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, has been employed as the elevator group control system controller in some studies of recent years. Moreover, reinforcement learning (RL) has been also found to be useful for more improvements of elevator group control performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group control system of a typical office building to evaluate its applicability and efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.

Dynamic Partition of Elevator Group Control System with Destination Floor Guidance in Up-peak Traffic

This paper presents a new modeling of elevator group intelligent scheduling system with destination floor guidance. The traditional input mode of separate hall call registration and the destination selection is improved to a single-input mode. On this basis, dynamic partition method in up-peak traffic is studied. This method means dynamically adjust division of floor region based on flow rate and distribution of passenger. Dynamic programming algorithm is used to solve this problem. Through regroupment and classification for ensemble of hall call communication, prediction of multi-objective evaluation items is proposed. Fuzzy Neural Network is constructed and applied further to realize the optimal scheduling policy. Simulated results show that the presented elevator model is effective and the optimized scheduling algorithm has advantaged improvement for overall performance of elevator system.

Distributed approach to group control of elevator systems using fuzzy logic and FPGA implementation of dispatching algorithms

Elevator systems are administrated by an elevator group control system (EGCS) and micro-processed sub-systems implementing a local control system (LCS) for each elevator. This paper describes the validation of five dispatching algorithms for elevator systems that were implemented on Spartan 3 FPGA-based boards in an integrated approach reducing the area and improving performance. The overall system is composed of several LCS, which implement the dispatching algorithms, an RS485-based network and a virtual environment called virtual elevator interface system (VEI), which includes a simulator/monitoring system and an EGCS-based on fuzzy logic (FEGCS). The FEGCS runs on a PC and, under different traffic situations, determines the best algorithm to be run in each LCS in order to reduce the user waiting time and the power consumption. The novelty of this approach is that the LCSs are capable to run different dispatching algorithms independently, that are suitable for specific passenger traffic situations, while the FEGCS only must determine the best algorithm to be run in each LCS. The VEI allows the designer to test and validate in a flexible way the algorithm performance for different traffic situations.

Evolutionary bi-objective optimisation in the elevator car routing problem

The paper introduces a genetic algorithms based elevator group control system utilising new approaches to multi-objective optimisation in a dynamically changing process control environment. The problem of controlling a group of elevators as well as the basic principles of the existing single-objective genetic elevator group control method are described. The foundations of the developed multi-objective approach, Evolutionary Standardised-Objective Weighted Aggregation Method, with a PI-controller operating as an interactive Decision Maker, are introduced. Their operation as a part of bi-objective genetic elevator group control is presented together with the performance results obtained from simulations concerning a high-rise office building. The results show that with this approach it is possible to regulate the service level of an elevator system, in terms of average passenger waiting time, so as to bring it to a desired level and to produce that service with minimum energy consumption. This has not been seen before in the elevator industry.

Elevator group control system using multiagent system

AbstractIn this study, the authors proposed an elevator group control system using multiagent system, and verified the efficiency of the system in areas characterized by concentration of large crowds of passengers on the main floor, for example, during morning rush‐hour traffic. Congestion on the main floor in heavy morning rush‐hour traffic areas was previously alleviated using specific traffic control systems providing so‐called morning rush‐hour operation by forced dispatching of multiple elevators to the main floor. However, since forced dispatching on the main floor affects areas other than the main floor and also the subsequent service on the main floor, it makes overall improvement of transportation efficiency a difficult task. There are published reports relating to morning rush‐hour operation, but these deal with centralized control based on overall monitoring and sending instruction commands. Thus, the problem is that if this control system stopped working, it caused overall malfunctioning. In addition, in a system based on overall monitoring and generating optimal control plan in accordance with the received information, it is not easy to generate design updates following a change in the system's design or environment. Therefore, in the current study the authors introduced a multiagent system with distributed processing, in which individual elevators make action decisions by independent learning process. The authors proposed an elevator group control system which uses simplified reasoning to provide for flexible response even in the case of a changed environment, such as when a part of the elevator group has stopped functioning. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 34(1): 45–58, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1183