An online tuning method for multiobjective control of elevator group

Fujino, Atsuya; Tobita, T.; Yoneda, K.

doi:10.1109/iecon.1992.254529

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…In the elevator request interfaces, there are two types of call buttons: hall call and car call [9,47]. A hall call is triggered when a passenger presses upward or downward button at hall in order to ride an elevator car.…”

Section: The Proposed Systemmentioning

confidence: 99%

“…Inter-floor happens when passengers randomly move between floors during the daytime and there is no peak traffic at this time. Our nonhomogeneous traffic includes up-peak (8:00-9:00), downpeak (17:00-18:00), up/down-peak (12:00-13:00), and interfloor patterns as is done in previous studies [9,47,54,55]. The arrival rate for the up-peak and down-peak is set to 30 passengers/minute and 15 passengers/minute, respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

2019

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In modern smart buildings, the electricity consumption of a building is monitored every time and costs differently at each time slot of a day. Smart buildings are also equipped with indoor sensors that can track the movement of human beings. In this paper, we propose a new elevator control system (ECS) that utilizes two kinds of context information in smart buildings: (1) human movements estimated by indoor sensors and (2) dynamic changes of electricity price. In particular, indoor sensors recognize elevator passengers before they press the elevator call buttons, and smart meters inform the dynamically changing price of the electricity to ECS. By using this information, our ECS aims at minimizing both the electricity cost and the waiting time of passengers. As this is a complex optimization problem, we use an evolutionary computation technique based on genetic algorithms (GA). We inject a learning module into the control unit of ECS, which monitors the change of the electricity price and the passengers’ traffic detected by sensors. Experimental results with the simulator we developed show that our ECS outperforms the scheduling configuration that does not consider sensor information or electricity price changes.

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Section: The Proposed Systemmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

2019

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“…A system described by Tobita et al (1991) and Fujino et al (1992) uses a greedy control algorithm that combines multiple objectives such as wait time, travel time, crowding, and power consumption. The weighting of these objectives is accomplished using parameters that are tuned on-line.…”

Section: Adaptive and Learning Approachesmentioning

confidence: 99%

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1998

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Abstract. Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithms have appeared that approximate dynamic programming on an incremental basis. They can be trained on the basis of real or simulated experiences, focusing their computation on areas of state space that are actually visited during control, making them computationally tractable on very large problems. If each member of a team of agents employs one of these algorithms, a new collective learning algorithm emerges for the team as a whole. In this paper we demonstrate that such collective RL algorithms can be powerful heuristic methods for addressing large-scale control problems.Elevator group control serves as our testbed. It is a difficult domain posing a combination of challenges not seen in most multi-agent learning research to date. We use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reward signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of multi-agent RL on a very large scale stochastic dynamic optimization problem of practical utility.

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A Context-Aware Elevator Scheduling System for Smart Apartment Buildings

Kwon

Bahn

Koh

Lecture Notes in Computer Science

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An online tuning method for multiobjective control of elevator group

Cited by 5 publications

References 2 publications

Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

Untitled

A Context-Aware Elevator Scheduling System for Smart Apartment Buildings

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