A fuzzy approach to elevator group control system

Kim, Chang Bum; Seong, K.A.; Lee-Kwang, Hyung; Kim, J.O.; Lim, Yong Bae

doi:10.1109/21.384263

Cited by 44 publications

(2 citation statements)

References 12 publications

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“…Most of them deployed such technologies as the fuzzy rule, the genetic algorithm, and the agent [17]. Especially the fuzzy rule seems dominant [18,19,20], although the number of published papers has decreased. Most of those studies were conducted by researchers in industries, and destined applicable to real problems.…”

Section: Short Overview On the Literaturementioning

confidence: 99%

Proposal and Progress of a Road-map to Bridge Theoretical and Practical Approaches for Elevator Operation Problems

Inamoto

Higami

Kobayashi

2017

International Journal of Smart Computing and Artificial Intelligence

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In this paper, the authors propose a road-map to bridge theoretical and practical approaches in the discipline of the elevator operation problem. The theoretical approach aims to solve static elevator operation problems, here static denotes all information on users of the elevator system is known before scheduling. The practical approach aims to construct rule-bases for realistic situations, where the user's behavior is not known in detail, but known to obey a certain traffic pattern. The authors expect efforts for bridging those approaches to yield a supervised learning of rule-bases by using optimal solutions as teaching data.The proposed road-map is comprised of 5 stages: (1) to obtain an optimal solution for a problem instance of a static elevator operation problem, (2) to construct an identical optimal rule-base from the optimal solution, (3) to construct a similar optimal rule-base which is based on some characteristic functions and effective only for that problem instance, (4) to construct a rule-base which is effective for a set of problem instances, and finally (5) to construct a rule-base which is effective for various problem instances. Computational result display current progress in earlier stages of the road-map.

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Section: Short Overview On the Literaturementioning

confidence: 99%

Proposal and Progress of a Road-map to Bridge Theoretical and Practical Approaches for Elevator Operation Problems

Inamoto

Higami

Kobayashi

2017

International Journal of Smart Computing and Artificial Intelligence

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“…In the contemporary world today, the emergence of high-rise building makes the application of elevators more and more widely, and the demand for elevator group control system has been increased rapidly. To satisfy the traffic demands of high rise building and skyscrapers, the elevator group control system is used to manage elevator group via adopting optimized control tactic [1]. Because the conventional elevator group control system is based on the button as a response and where to dispatch an elevator car, there is one big problem when system dispatches the elevator to that button responding floor, but no passenger there and causing a waste of time and energy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Elevator Group Control Algorithm Based on Corridor Passenger Detection and Tracking

Zheng

Zhang

2013

AMM

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The conventional elevator group control system is based on the button as a response and where to dispatch an elevator car. There is one big problem that when system dispatches the elevator to that button responding floor, but no passenger there and causing a waste of time and energy. In this paper, we present a novel intelligent elevator group control algorithm based on corridor passenger detection and tracking. In the above proposed system, after the corridor passengers are detected using binocular-cameras, Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. At same time, a novel intelligent elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation condition.

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

Mabu

Hirasawa

et al. 2011

IEEJ Transactions Elec Engng

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Elevator 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.

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A fuzzy approach to elevator group control system

Cited by 44 publications

References 12 publications

Proposal and Progress of a Road-map to Bridge Theoretical and Practical Approaches for Elevator Operation Problems

Proposal and Progress of a Road-map to Bridge Theoretical and Practical Approaches for Elevator Operation Problems

A Novel Intelligent Elevator Group Control Algorithm Based on Corridor Passenger Detection and Tracking

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

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