Elevator Group Control System Research Articles

Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems

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.

A Multi-Objective Optimization Approach for Elevator Group Control Systems Based on Particle Swarm Algorithm

Currently, elevator group control systems containing multiple elevators are used in high-rise buildings, where elevator groups are centrally dispatched according to a set dispatching plan to provide vertical transportation services for the passengers. However, if these elevators work independently, once passengers send out a call signal, multiple elevators will respond to the same request at the same time, and the elevator group will repeatedly go back and forth, which seriously affects the system operation efficiency and also increases energy consumption, so it is a key issue to solve the dispatching problem in the elevator group control system. Elevator group control scheduling is an NP-hard problem with explosive combination characteristics. In this paper, a multi-objective model is formulated based on the criteria of average passenger travel time, average waiting time and system energy consumption for the scheduling problem. Then the particle swarm optimization algorithm is proposed to solve the scheduling problem. Finally, the performance of the proposed algorithm is compared with the genetic algorithm-based elevator group control scheduling, the simulation results show that the proposed algorithm with fast convergence while the waiting time of passengers is significantly decreased.

Metaheuristic algorithms for elevator group control system: a holistic review

Metaheuristic algorithms for elevator group control system: a holistic review

Improving waiting time and energy consumption performance of a bi-objective genetic algorithm embedded in an elevator group control system through passenger flow estimation

Improving waiting time and energy consumption performance of a bi-objective genetic algorithm embedded in an elevator group control system through passenger flow estimation

Elevator Group Control System Based on Fuzzy Reasoning

Due to the characteristics of the elevator system itself, traditional control algorithms are difficult to apply to elevator dispatching. However, the fuzzy control algorithm does not require the establishment of an accurate mathematical model, and can be well integrated with the elevator system. Based on fuzzy control, the elevator dispatch can be optimized. The traffic fuzzy inference controller is designed to identify the elevator traffic mode according to the flow of people; the elevator fuzzy inference device is designed to construct the objective function according to the evaluation index. Siemens S7-1200 series PLC is used to control the elevator, the elevator model uses Elevator Simulation Pro for simulation debugging, and Portal WINCC is used as the host computer for debugging to monitor.

Elevator group control as a constrained multiobjective optimization problem

Modern elevator systems are controlled by the elevator group controllers that assign moving and stopping policies to the elevator cars. Designing an adequate elevator group control (EGC) policy is challenging for a number of reasons, one of them being conflicting optimization objectives. We address this task by formulating a corresponding constrained multiobjective optimization problem, and, in contrast to most studies in this domain, approach it using true multiobjective optimization methods capable of finding approximations for Pareto-optimal solutions. Specifically, we apply five multiobjective optimization algorithms with default constraint handling techniques and demonstrate their performance in optimizing EGC for nine elevator systems of various complexity. The experimental results confirm the scalability of the proposed methodology and suggest that NSGA-II equipped with the constrained-domination principle is the best performing algorithm on the test EGC systems. The proposed problem formulation and methodology allow for better understanding of the EGC design problem and provide insightful information to the stakeholders involved in deciding on elevator system configurations and control policies.

Double deck elevator group control systems using evolutionary algorithms: Interfloor and lunchpeak traffic analysis

The continuous development of high-rise buildings around the world requires the installation of efficient elevator systems able to vertically transport the different passengers along the buildings in their daily journeys. Double deck elevators can increase the efficiency of these vertical transportation systems. Double deck elevators consist of two adjacent cabins that are joined and travel together along the same shaft, so the handling capacity of the system can be improved by allowing the dispatch of passengers with destination to two consecutive floors at the same instant. This type of architecture emerges as especially appropriate for uppeak traffic conditions. However, its suitability has not been sufficiently analysed for non-dominant (up or down) traffic patterns, such as interfloor and lunchpeak traffic. Our paper deals with conventionally controlled double deck elevators, where the Elevator Group Control System (EGCS) requires specific car-landing call allocation algorithms able to manage such special car architectures. Along this line, we propose a genetic algorithm that demonstrated a good performance when compared to a tabu search algorithm that was used as benchmark for comparison, taking into account different fitness evaluation functions (overall dispatching time and nearest call). The analysis was undertaken for interfloor and lunchpeak traffics and the average waiting, transit and journey times, and the energy consumption are reported as performance indexes of the vertical transportation system. The algorithms produced efficient results outperforming the considered benchmark and emerged as very competitive algorithms considering all the performance indexes as a whole. Results were tested using ELEVATE, the standard simulation software for vertical transportation.

Optimal Elevator Group Control via Deep Asynchronous Actor-Critic Learning.

In this article, a new deep reinforcement learning (RL) method, called asynchronous advantage actor-critic (A3C) method, is developed to solve the optimal control problem of elevator group control systems (EGCSs). The main contribution of this article is that the optimal control law of EGCSs is designed via a new deep RL method, such that the elevator system sends passengers to the desired destination floors as soon as possible. Deep convolutional and recurrent neural networks, which can update themselves during applications, are designed to dispatch elevators. Then, the structure of the A3C method is developed, and the training phase for the learning optimal law is discussed. Finally, simulation results illustrate that the developed method effectively reduces the average waiting time in a complex building environment. Comparisons with traditional algorithms further verify the effectiveness of the developed method.

Dynamic scheduling of elevators by the Elevator Group Control System using a hybrid Approach

Dynamic scheduling of elevators by the Elevator Group Control System using a hybrid Approach

Design and Research of Elevator Group Control System Based on PLC

This paper is based on the research of elevator group control system based on PLC, aiming at the phenomenon of elevator in the running process of long time elevator phenomenon, on the basis of improving the utilization of elevator, put forward the overall design structure of elevator group control system, through the communication and control between group control and single elevator, to achieve the unified coordination of elevator scheduling. Considering the riding comfort of passengers, the shortest waiting time of passengers is shortest, and the algorithm of elevator group control scheduling system is analyzed according to the time delay algorithm. At the same time, the elevator control system is analyzed in detail, using PLC to achieve the hardware circuit design, complete the elevator control signal processing, through the inverter control motor operation, to achieve the elevator uplink and downlink. In the design of elevator control software, the overload detection and the delay treatment of the switch door are set up to improve the safety of the elevator, so that the elevator can develop in the direction of intelligent.

Elevator Group Control System EGCS with Risk Management

Elevator Group Control System EGCS with Risk Management

Arrival Probability Based Parking Algorithm for Elevator Group Control Systems

A main concern in elevator group control system (EGCS) is quality of service in terms of average passenger waiting time. If no parking algorithm is applied, when an elevator enters an idle state, it remains at its current floor. An additional enhancement for the system can be using parking algorithms and sending idle elevators to park floors to reduce passenger waiting times. In elevator traffic, passengers are considered in three groups according to their travel intentions as incoming, inter-floor and outgoing passengers. Traffic components representing the ratios of these groups are major factors in determining traffic pattern. Overall passenger arrival probabilities can be described by weighted sum of incoming, inter-floor and outgoing arrival distributions with corresponding traffic components. Then, arrival probabilities can be used to obtain a general adaptive parking algorithm. In this study we propose a parking algorithm for EGCS based on arrival probabilities and investigate average waiting time under varying traffic conditions.

Traffic-based floor preference for the scheduling of elevators in elevator group control system

<p align="JUSTIFY"><span style="font-family: Arial, serif;"><span style="font-size: small;">Modern high-rise buildings require complex yet efficient Elevator Group Control Systems (EGCSs). In vertical transportation through an elevator, a passenger must make a hall call by pressing a landing call button installed at each floor and located near the cars of the elevator group. Conventionally, the EGCS allocates one of the cars for each hall call. Waiting time for the arrival of car and journey time inside a car are two parameters, which provide a suitable measure for quality and efficiency of EGCSs. The proposed system deals with this car-call allocation problem</span></span><span style="color: #505050;"><span style="font-family: Arial, serif;"><span style="font-size: small;">. </span></span></span><span style="font-family: Arial, serif;"><span style="font-size: small;">The proposed work analyzes the generated traffic patterns to dispatch a certain number of cars to certain floors in order to reduce overall wait time of passengers. The proposed algorithm is simulated for high-rise building with 20 floors and provides a better result with the reduced wait time for more number of passengers.</span></span></p>

A model for the visualization and analysis of elevator traffic

ABSTRACTAnalysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.

Simulation Based Elevator Group Control System for Multi-Storey Building

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.

Fair and Effective Elevator Car Dispatching Method in Elevator Group Control System using Cameras

We propose a control method for an elevator group control system to allocate elevator cars for all types of passengers, including general passengers and special passengers who are likely to be unfairly treated (e.g., with strollers, wheelchairs, or bulky luggage), in order to achieve fair waiting times as well as efficient transportation. Elevators are necessary for people to move vertically within high-rise buildings. Since the number of elevator cars is fixed, they have to be carefully controlled for effective dispatch. Furthermore, due to the limited capacities of elevator cars, some special passengers who require more space are often forced to wait much longer than general passengers for cars with sufficient empty space to arrive. These days, as cameras and other sensors that monitor the environment have become more common, and thanks to the recent advances in computer vision technologies, we can estimate the number of waiting passengers and the size of their belongings in elevator halls. By using such information gathered from muliple agents that monitor a specific elevator car or elevator hall, the proposed control enables effective dispatch for shorter and fairer waiting times. Experimental results using the simulated elevator control showed that our method could make waiting times fairer and achieved total efficiency to carry passengers. We discuss the reasons for the improvement as well as the limitation of our method.

Real-time algorithms for the bilevel double-deck elevator dispatching problem

Double-deck elevators consist of two elevator cars attached together and, thus, save building core space. In the destination control system (DCS), passengers register their destination floors in the elevator lobbies. In return, the DCS immediately assigns an elevator to each transportation request and signals it to the passenger. Based on the additional information, the DCS is able to increase handling capacity under incoming traffic conditions. However, the double-deck DCS does not yet function optimally in mixed lunch traffic, which consists of incoming, outgoing and interfloor traffic. In this paper, the double-deck DCS is further developed by introducing delayed elevator and deck assignments. With the delayed assignments, the serving elevator or deck of each request can be reassigned to another elevator or deck until the last moment. Such a double-deck DCS can better adapt to emerging requests and, as a result, can improve passenger service quality in lunch traffic dramatically. To enable real-time optimization in an elevator group control system, this paper formulates the double-deck elevator dispatching problem as a bilevel optimization problem and solves the problem fast with a genetic algorithm. Numerical experiments show that the bilevel algorithm outperforms the earlier single-level algorithm in both solution quality and computation times.

Modeling uncertain passenger arrivals in the elevator dispatching problem with destination control

An Elevator Group Control System (EGCS) assigns an elevator of a group to each passenger transportation request by solving a snapshot optimization problem, the Elevator Dispatching Problem (EDP). In the destination control, passengers register their destination floors in the elevator lobbies, after which the EGCS completes the assignment at once and is not allowed to change it later. Therefore, the EDP is formulated as a stochastic optimal control problem, where uncertain future passenger arrivals are modeled by a Poisson and a geometric Poisson process. The EDP is considered as a certainty equivalent controller in which the uncertain quantities are replaced by their expected values, and as a robust controller in which they take multiple values according to risk scenarios. Numerical experiments show that the expectations do not accurately predict EDP variables. The modeling with the geometric Poisson process results in better forecasting accuracy than with the Poisson process and many scenarios that closely match the realizations of the variables. Hence, the scenarios can be used as a basis for a robust EDP which simultaneously minimizes a passenger service quality criterion and its variation due to uncertain demand.

Assessing the up-peak performance of destination elevator group control systems using real time allocation of landing calls

The boost in the handling capacity of destination elevator group control systems has been previously calculated under idealised conditions, assuming simultaneously minimum values of the highest reversal floor and of the average number of stops in a round trip. The boost has also been calculated under conditions of allocating the landing calls with full advanced knowledge of the passenger destinations (offline allocation). This paper re-calculates the boost in handling capacity under real time allocation conditions of the landing calls as they are registered. The landing calls are revealed to the controller one at a time, and the controller has to make an irrevocable allocation of the landing call to an elevator. The efficiency drops under real time allocation conditions compared to the other two conditions as expected. However, it still reveals a significant boost in handling capacity that increases with the number of elevators in the group.

Assessing the up-peak performance of destination elevator group control systems using real time allocation of landing calls

The boost in the handling capacity of destination elevator group control systems has been previously calculated under idealised conditions, assuming simultaneously minimum values of the highest reversal floor and of the average number of stops in a round trip. The boost has also been calculated under conditions of allocating the landing calls with full advanced knowledge of the passenger destinations (offline allocation). This paper re-calculates the boost in handling capacity under real time allocation conditions of the landing calls as they are registered. The landing calls are revealed to the controller one at a time, and the controller has to make an irrevocable allocation of the landing call to an elevator. The efficiency drops under real time allocation conditions compared to the other two conditions as expected. However, it still reveals a significant boost in handling capacity that increases with the number of elevators in the group.