What are the actions that must be done when the lift arrives at a floor?

For each one of the elevator zones, when the lift arrives at a floor the subsequent actions must be checked and done if necessary: lift waits for calls, passengers leaves the lift, passengers come into the lift, lift allocation in case of full capacity, cabin call allocation and call evaluation.

How long does it take to implement a genetic algorithm in real?

the implementation of such type of algorithms in real controllers has to be done carefully in order to maintain bounded the response time of the algorithm.

How many people go out for lunch?

Most of the workers go out for lunch during the interval [14:00,15:00] hours, returning to the building during [15:20,16:00] hours.

How many workers are in each floor?

There are 30 workers in each of the building floors excepting the 7th floor (the administration department with 60 workers) and the 12th floor (the manager department with 15 workers).

What is the replacement probability of the worst individual?

the individual in ranking position-i, have a replacement probability equal to q(1-q)i, being q the replacement probability of the worst individual.

Open AccessBook Chapter10.1007/3-540-44869-1_40

A Genetic Algorithm for Controlling Elevator Group Systems

Q: What is the main reason for the installation of synchronized elevator groups in professional use buildings?

The large utilisation ratio of the elevators makes necessary the implementation of such systems in order to give quality to the users-passengers and energetic efficiency to the building managers.

Pablo Cortés, +2 more

- 05 Jun 2009

- pp 313-320

TL;DR: A genetic algorithm is presented that is compared with traditional controller algorithms in industry applications and reaches a better performance attending to the system waiting times than THV algorithm.

Abstract: The efficient performance of elevator group system controllers becomes a first order necessity when the buildings have a high utilisation ratio of the elevators, such as in professional buildings. We present a genetic algorithm that is compared with traditional controller algorithms in industry applications. An ARENA simulation scenario is created during heavy lunchpeak traffic conditions. The results allow us to affirm that our genetic algorithm reaches a better performance attending to the system waiting times than THV algorithm.

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Most frequently asked questions

1. What is the function of the mutation operator?

The mutation operator replaces a hall call allocation from the individual chromosome by changing the genes from 01 to 10 or viceverse.

2. What is the typical type of controllers used in the industry?

The traditional type of controllers implemented in the industry follows simple dispatch rules that make use of an IF-ELSE logical commands set.

3. What is the function that is used to evaluate the elevator?

When the lift arrives at a floor, the Arrival_Evaluation submodule presents three options: the lift continues up, the lift continues down or the lifts starts the deceleration process (preparing to stop).

4. What is the main purpose of the elevator system?

The installation of synchronized elevator groups in professional use buildings (offices, hospitals or hotels) is an usual practice.

Fig. 1. Individual chromosome for a twelve floors case building corresponding to one specific elevator of the group and its associated physical button box

Table 1. THV and Genetic Algorithm system waiting time statisitical results

Fig. 2. Possible elevator streams to estimate the fitness

Fig. 6. Comparison of system waiting times by floors. The graphics depict the waiting times by floors as well as the average value. Note how the THV results are distributed between 0 and 1000 seconds, and the genetic algorithm results are distributed between 0 and 700 seconds.

Citations

Journal Article•10.1109/TASE.2007.895217

Group Elevator Scheduling With Advance Information for Normal and Emergency Modes

Peter B. Luh, +2 more

- 04 Apr 2008

- IEEE Transactions on Automation Science ...

TL;DR: Numerical testing results demonstrate near-optimal solution quality, computational efficiency, the value of future traffic information, and the potential of using elevators for emergency evacuation.

...read moreread less

•Journal Article•10.1016/J.ASOC.2012.11.023

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Berna Bolat, +2 more

- 01 May 2013

TL;DR: The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, and results show that the proposedPSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches.

...read moreread less

Journal Article•10.1109/TASE.2009.2024242

Optimization of Group Elevator Scheduling With Advance Information

Jin Sun, +2 more

- 01 Apr 2010

- IEEE Transactions on Automation Science ...

TL;DR: A hybrid nested partitions and genetic algorithm method for passenger-to-car assignment which can be extended to solve a generic class of sequential decision problems and a new door action control method is suggested to increase the flexibility of elevators.

...read moreread less

An Indexed Bibliography of Genetic Algorithms in Control

Jarmo T. Alander

- 01 Jan 2015

Proceedings Article•10.1109/ROBOT.2005.1570314

Group Elevator Scheduling with Advanced Traffic Information for Normal Operations and Coordinated Emergency Evacuation

Bo Xiong, +2 more

- 18 Apr 2005

TL;DR: This paper presents the optimized scheduling of a group of elevators with advanced traffic information for normal operations and coordinated emergency evacuation and results demonstrate that near-optimal solutions are obtained for problems of moderate sizes under selected traffic patterns.

...read moreread less

...

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References

Elevator traffic analysis, design and control

Gina Barney, +1 more

- 01 Jan 1985

157

Proceedings Article•10.1109/FUZZY.1998.687533

A fuzzy elevator group controller with linear context adaptation

Ricardo R. Gudwin, +2 more

- 04 May 1998

TL;DR: A fuzzy elevator group controller is introduced using a linear context adaptation technique and the focus is on the comparison between the standard fuzzy controller and the fuzzy controller withlinear context adaptation.

...read moreread less

Book Chapter•10.1007/978-3-7091-7535-4_104

Elevator group control using distributed genetic algorithm

Jarmo T. Alander, +2 more

- 01 Jan 1995

TL;DR: This work has studied the applicability of genetic algorithms to the optimization of elevator group control parameters to minimize the average passenger waiting time in a simulated office building.

...read moreread less

•Proceedings Article•10.1109/ICEC.1994.349964

Genetic algorithms and discrete event systems: an application

Ricardo R. Gudwin, +1 more

- 27 Jun 1994

TL;DR: An approach for discrete event systems control optimization, based on the theory developed by P.J. Ramadge and W.M. Wonham and on the limited lookahead policy strategy proposed by Sheng-Luen Chung and S. Lafortune, is proposed.

...read moreread less

Journal Article•10.1109/3468.668960

Design and implementation of a fuzzy elevator group control system

ChangBum Kim, +3 more

- 01 May 1998

TL;DR: A control strategy generation method, a hall call assignment method based on the fuzzy theory, and then the fuzzy elevator group control system (FEGCS), which shows better results than the conventional methods in simulations.

...read moreread less

A Genetic Algorithm for Controlling Elevator Group Systems

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AI Agents for this Paper

Most frequently asked questions

1. What is the function of the mutation operator?

2. What is the typical type of controllers used in the industry?

3. What is the function that is used to evaluate the elevator?

4. What is the main purpose of the elevator system?

5. What are the actions that must be done when the lift arrives at a floor?

6. What is the main reason for the installation of synchronized elevator groups in professional use buildings?

7. How long does it take to implement a genetic algorithm in real?

8. How many people go out for lunch?

9. How many workers are in each floor?

10. What is the replacement probability of the worst individual?

Figures

Citations

Group Elevator Scheduling With Advance Information for Normal and Emergency Modes

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Optimization of Group Elevator Scheduling With Advance Information

An Indexed Bibliography of Genetic Algorithms in Control

Group Elevator Scheduling with Advanced Traffic Information for Normal Operations and Coordinated Emergency Evacuation

References

Elevator traffic analysis, design and control

A fuzzy elevator group controller with linear context adaptation

Elevator group control using distributed genetic algorithm

Genetic algorithms and discrete event systems: an application

Design and implementation of a fuzzy elevator group control system

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