An elevator group control system with floor attribute control method and system optimization using genetic algorithms

Fujino, Atsuya; Tobita, T.; Segawa, Kazuhiro; Yoneda, K.; Togawa, Akihiko

doi:10.1109/iecon.1995.484173

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Most EGCSs do not offer a particular service for each floor. However, a genetic algorithm is presented in this way [65] for allocating the landing calls while setting preferences for each floor. The EGCS uses an objective function composed of two terms.…”

Section: Approaches Based On Genetic Algorithmsmentioning

confidence: 99%

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Fernández

Cortés

2015

IEEE Control Syst.

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Section: Approaches Based On Genetic Algorithmsmentioning

confidence: 99%

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Fernández

Cortés

2015

IEEE Control Syst.

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“…Well-known criteria as Average and Residual Waiting Time are discussed in [20]. Also, techniques based on discrete-state Markov chains and Markov decision problem formulation have been presented in [20][21][22], local control system (LCS) based Fieldprogrammable Gate Array (FPGA) algorithm using Fuzzy approach is in [23] and others based on AI, neural networks and genetic algorithms in [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Design of fuzzy based controller for modern elevator group with floor priority constraints

Rashid

Kasemi

Faruq

et al. 2011

2011 4th International Conference on Mechatronics (ICOM)

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The elevator technologies are developed to serve the requested passenger's floors with high consideration of passengers' satisfaction and elevator optimal performance. Responding to the issue, this paper focuses on development of an elevator group controller based on fuzzy algorithm. By introducing a fuzzy controller in an elevator system, this project is developed to manage the required passenger traffic density keeping passenger waiting times within reasonable limits. To increase the performance of the elevator group three major linguistic variables are introduced within a set of fuzzy rules. These include the average waiting time (AWT), power consumption (PC), and floor traffic (FT). When the passenger traffic is high, the low fuzzy control values reduce the importance of priority of floors rather the estimated arrival time is to be minimized. The simulation was accomplished to check the priority of fuzzy system graphically while the actual prototype of elevator system is programmed by PIC device as a controller. As a result a set of fuzzy rules was drawn based on practical considerations; mainly minimization of waiting time and energy consumption. When a few hall requests are registered, the elevator controller will decide which one of the elevators will serve which of the incoming hall requests. In order to manifest the work with actual elevator service, a computer simulation of a four-cars-elevator controller in a twenty storied building is accomplished by using MATLAB TM fuzzy logic toolbox and implements the prototype experimental model.

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“…For example, if sufficiently effective rule-bases are available, we can decrease passengers' waiting times by switching such rule-bases by the method reported in [1]. Else if the rule-bases are propitious in their configurations but involve some parameters, the rule-bases can be improved through the adjustment in their parameters by the method reported in [2]. Even though effective configurations of rule-bases are unknown, the method reported in [3] can assemble them from potential fragments.…”

Section: Introductionmentioning

confidence: 99%

Model-approximated dynamic programming based on decomposable state transition probabilities

Inamoto

Tamaki

Murao

2007

SICE Annual Conference 2007

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In this paper, a discrete model of an elevator system and a design of dynamic programming (DP) method based on that model are shown. The method is followed by a modified DP method on approximated state transition models. The discrete model theoretically considers the causes of a system's probabilistic behavior, and leads such approximation by the reduction of the insignificant constituent of them. In computational illustrations, the two DP methods are applied to two problems of small scale. The results show that the policies obtained by DP methods are both statistically better than an existing elevator allocation rule, and the modified method requires less computational time than the another.

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An elevator group control system with floor attribute control method and system optimization using genetic algorithms

Cited by 13 publications

References 5 publications

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Design of fuzzy based controller for modern elevator group with floor priority constraints

Model-approximated dynamic programming based on decomposable state transition probabilities

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