Group Supervisory Control Research Articles

Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

Genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.

Analysis of energy consumption of elevator group supervisory control system based on genetic network programming

AbstractElevator Group Supervisory Control System (EGSCS) is a traffic system, which provides the transportation services for passengers in modern buildings. As the elevator systems include uncertainty due to the future arrival of the passengers, it difficult to model, analyze, and optimize the elevator group supervisory control system. Recently, artificial intelligence technology has been used in such complex systems. Genetic Network Programming(GNP), a graph‐based evolutionary method extended from genetic algorithm and genetic programming, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as one of the criteria of the elevator operations. The elevators with maximum energy efficiency are therefore required. In this paper, the GNP is used to solve EGSCS with energy consumption (EC). Moreover, the idle car assignment has been embedded in the proposed method. Finally, the simulations show that some factors should be introduced into GNP in order to deal with the higher EC in the light traffic of the elevator systems. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multicar elevator group supervisory control system using genetic network programming

AbstractElevator group control systems are the transportation systems for handling passengers in the buildings. With the increasing demand for high‐rise buildings, it becomes important to improve the elevator service. The multicar elevators consist of plural cars in a single elevator shaft. It contributes to the improvement in passengers' handling capacity, while allowing the reduction of the space occupied in the building. In contrast with traditional elevator systems, the cars can no longer operate freely, where there are several restrictions on their available movements. This requires a more difficult control including stochastic scheduling with high combinatorial complexity in order to make the system more flexible. At present, lots of buildings with more than 40 floors are being built, which are usually divided into several zones served by local elevator groups. In addition, the cars should be operated at equal time intervals, especially in such a building with multicar elevator systems (MCES) in order to obtain its good performance. Genetic network programming (GNP), one of the evolutionary computations, can realize a rule‐based MCES due to its directed graph structure of the individual, which makes the system more flexible. This paper discusses MCES using GNP in high‐rise buildings. Also, the positions of elevators are considered to avoid the bunching phenomenon. The performance of MCES is studied and compared with single‐deck elevator system (SDES) and double‐deck elevator system (DDES). © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Pso-Based Real-Time Scheduling For Elevator Group Supervisory Control System

Abstract Elevator group supervisory control system (EGSCS) is an important and essential component in an industrial elevator system. This paper will introduce particle swarm optimization (PSO) as a heuristic intelligent search method to optimize in real time elevator traffic scheduling solution of EGSCS. The proposed PSO-based scheduling approach will be applied to effectively handle the peak elevator traffic. The overall experimental results will be used to validate its scheduling efficiency and effectiveness.

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

A Double-Deck Elevator Group Supervisory Control System with Destination Floor Guidance System Using Genetic Network Programming

The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two elevators are connected with each other, serve passengers at two consecutive floors simultaneously. Double-deck Elevator systems (DDES) become more complex in their behavior than conventional single-deck elevator systems (SDES). Recently, Artificial Intelligence (AI) technology has been used in such complex systems. Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator when it performs crossover and mutation operations to judgment nodes and processing nodes. Meanwhile, Destination Floor Guidance System (DFGS) is installed in DDES, so that passengers can also input their destinations at elevator halls. In this paper, we have applied GNP to DDES and compared DFGS with normal systems. The waiting time and traveling time of DFGS are all improved because of getting more information from DFGS. The simulations showed the effectiveness of the double-deck elevators with DFGS in different building traffics.

Elevator Group Supervisory Control System Using Genetic Network Programming with Macro Nodes and Reinforcement Learning

Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.

Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization

Genetic Network Programming (GNP) having a directed graph structure has been proposed as a new method of evolutionary computation. Recently, GNP has been applied to elevator group supervisory control system (EGSCS), a real-world problem, to demonstrate its applicability and effectiveness. Its previous study considers the known and fixed traffic flow, however, it is changed dynamically with time in real elevator systems. Therefore, an EGSCS with dynamic adaptive control considering such changes should be studied for practical applications. In this paper, we have applied GNP with functional localization to an EGSCS to construct such an adaptive system. In our proposal, the switching GNP can switch the functionally localized GNPs (assigning GNPs) based on the special traffic. Simulation confirmed the adaptability and effectiveness of our proposal in daily office-building traffic.

106 かご運行軌跡の時間的等間隔化を狙いとしたエレベーター群管理制御 : 将来予測目標ルート制御

Elevator group supervisory control systems are required to serve many passengers with small variation of waiting time, especially in heavy traffic. In order to equalize and shorten passengers' waiting time, it is known that cars should be operated at equal time intervals. In this paper, first, we describe a concept of an elevator group supervisory control system on which we focus on future trajectories of cars. Features of this control concept are as follows: (1) the system controls future trajectories of cars by referring to reference trajectories of them, and (2) the reference trajectories of cars are set to distribute the cars in the near future term. Second, we describe implementation algorithm for a microcomputer based on the above control concept. In this algorithm, the system controls time intervals among predicted trajectories of cars. Results of computer simulations of this control method show good performance especially in heavy traffic.

ランキング処理とノード関数最適化を考慮したGenetic Network Programmingによるエレベータ群管理システム

ランキング処理とノード関数最適化を考慮したGenetic Network Programmingによるエレベータ群管理システム

105 Genetic Network Programmingによるエレベータ群管理制御の最適化

105 Genetic Network Programmingによるエレベータ群管理制御の最適化

Basic Study of Elevator Group Supervisory Control System using Genetic Network Programming

Genetic Network Programming (GNP) has been proposed as a new method of evolutionary computations and its basic characteristics are studied. GNP is constructed using graph structures whose gene consists of directed graphs, so it is possible to search solutions effectively especially for the dynamic real world problems due to the implicit memory function of its structure. Also GNP can easily implement a priori knowledge into its structure as functional nodes. There has been no example where GNP is applied to real world systems until now. In this paper, Elevator Group Supervisory Control System (EGSCS) which is a typical real world system is studied using GNP. From the simulations, the availability of GNP to EGSCS is confirmed and it is clarified that the proposed method can show better performances than other conventional methods.

The Fuzzy Logic Inference Decision System of Elevator Group Supervisory Control

This paper gives the fuzzy control block diagram of elevator group supervisory control expert system and describes the application of fuzzy logic theory in computer elevator group supervisory control system. In the paper, the fuzzy control block diagram is compared with the overall system block diagram characterized statistics. The theory of fuzzy inference system discussed in this paper is the basis to establish modern elevator group supervisory control system.

Dynamical Complex System. A Parameter Tuning Method for Elevator Group Supervisory Control System By Using Genetic Algorithms.

This paper describesa new parameter tuning method for an elevator group control system. In an elevator group control system tuning parameters describing elevator use and the building environment are important. The new tuning method uses improved genetic algorithms to follow environment changes and to give robustness. Keeping diversity is important in following environment changes. The paper also describes the adding random chromosomes method and adding other estimationsmethod to keep diversity. Their effects are shown by simulations.

Characterized Supervisory Control System for Elevators and its Support Equipment.

An elevator group supervisory control system is used to supervise multiple elevators, ensuring that they are operated efficiently. In a conventional system which adopts the viewpoint of improving service to passengers, occurrence of hall calls is supervised and a call is assigned to the optimum elevator in consideration of the entire service condition, thereby reducing average waiting time.Recently, requests by elevator users have focused not only on reducing average waiting time, but also on reducing riding time, the number of passengers, etc.This paper describes a new elevator supervisory control system which improves plural control objects (reducing waiting time, riding time, number of passengers, etc.), according to owners' or managers' requests.This system consists of a supervisory controller which supervises elevators, and support equipment. This support equipment receives' owners or managers' requests, and products a control method which satisfies the control objects by using an expert system, and estimates results by simulation.

Optimal hall call assignment method of elevator group supervisory control system

Optimal hall call assignment method of elevator group supervisory control system

Simulation of Elevator System for World's Tallest Buildings

The design of the world's two tallest buildings, each 110 stories high, calls for a unique elevator system utilizing 95 local and express passenger elevators per building. Conventional elevator systems' evaluation criteria were supplemented by traffic data and the use of simulation to determine accurately the expected service levels throughout the building. In order to measure the design system's capabilities and to develop and evaluate changes in the elevator specifications, a second-by-second simulation of an entire tower building's elevator operation has been developed, programmed, validated, and applied. Data were collected to develop expected passenger arrival and departure distributions and to determine elevator characteristics—acceleration and deceleration, loading and unloading times. Traffic input data for each of the individual local and express elevators was generated by a series of programs. The computations and logic of the group supervisory control systems for local and express banks are incorporated in simulation programs.