An exact reoptimization algorithm for the scheduling of elevator groups

Hiller, Benjamin; Klug, Torsten; Tuchscherer, Andreas

doi:10.1007/s10696-013-9175-6

Cited by 16 publications

(5 citation statements)

References 6 publications

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Section: Discussionmentioning

confidence: 99%

“…13,35 Alternatively, call-giving devices and lobby guidance different from the de facto standard destination control system allow the system to reoptimize and change the lifts assigned to particular calls, which makes the lift group more adaptive to unexpected events. [36][37][38][39] Hence, the modelling of social groups in people flow simulations would add another level to the realisticness of lift traffic design. The current understanding is that simulations with only individual passengers describes the worst-case situation for lift group performance and, therefore, can be used as the main benchmark in lift traffic design.…”

Section: Discussionmentioning

confidence: 99%

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A field study and analysis of passengers arriving at lift lobbies in social groups in multi-storey office, hotel and residential buildings

Sorsa¹,

Siikonen²,

Kuusinen³

et al. 2020

Building Services Engineering Research and Technology

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This field study was made to study the size of social groups among passengers travelling with lifts. The group size was observed in three types of buildings located in four countries, totally in nine multi-storey buildings. The observations were carried out manually for 12 hours on a normal weekday. Analysis results show that the daily mean group size was generally low: 1.2–1.3 persons in the offices, 1.3–2.0 persons in the hotels and 1.1–1.4 persons in the residential buildings. Hourly means differed significantly from the daily means for each building. This suggests that group size should be considered on hourly or shorter basis. In addition, goodness-of-fit tests were conducted to determine a statistical model for the group size. A geometric distribution was found to fit the data the best across all building types which simplifies modeling of passenger group arrivals in buildings. Practical application: This article contributes to the understanding of lift passenger tendency to travel in socially connected groups in multi-storey buildings. A unique data set of group size observations around the world is reported on an hourly basis for office, hotel and residential buildings. A geometric distribution is proposed to model the group size statistically, which simplifies the use of socially connected groups when generating passengers in lift traffic simulations. The real-world data and the statistical model of the group size enable more realistic lift traffic simulations that are routinely conducted during the design phase of multi-storey buildings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A field study and analysis of passengers arriving at lift lobbies in social groups in multi-storey office, hotel and residential buildings

Sorsa¹,

Siikonen²,

Kuusinen³

et al. 2020

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

show abstract

“…The authors consider the number of passengers waiting for an elevator on each floor as the main source of uncertainty. In [18] the authors attempted to develop a model that unifies immediate and delayed call allocation systems to improve elevator dispatching. The former allocates the call immediately after the call was made by a passenger, the later allocates the call just before an elevator is ready to serve the passengers.…”

Section: Ai Techniques For Elevatorsmentioning

confidence: 99%

“…The method utilizes an optimization-based collision and reversal avoidance technique for simultaneous operation of elevator cars in a single shaft. Similarly, as in [18], the passenger call assignment is done under immediate or delayed call allocation control policy. In [20] the authors attempt to improve the energy-efficiency of an elevator group without compromising the passenger satisfaction.…”

Section: Ai Techniques For Elevatorsmentioning

confidence: 99%

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Bapin

Zarikas²

2019

ijacsa

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Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation-Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested.

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“…Decision tree is also adopted to limit the complex search space, thereby reducing the computational complexity significantly [24]. Hiller and Klug generate a new candidate schedule set whenever an elevator request arrives and prune the schedules in the set by making use of branch-and-bound techniques [25].…”

Section: Literature Reviewmentioning

confidence: 99%

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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An exact reoptimization algorithm for the scheduling of elevator groups

Cited by 16 publications

References 6 publications

A field study and analysis of passengers arriving at lift lobbies in social groups in multi-storey office, hotel and residential buildings

A field study and analysis of passengers arriving at lift lobbies in social groups in multi-storey office, hotel and residential buildings

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

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