The use of Monte Carlo simulation in evaluating the elevator round trip time under up-peak traffic conditions and conventional group control

Al-Sharif, Lutfi; Aldahiyat, Husam M; Alkurdi, Laith M

doi:10.1177/0143624411414837

Cited by 25 publications

(27 citation statements)

References 6 publications

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“…For more details about the use of the Monte Carlo simulation in elevator traffic analysis, the reader is referred to earlier studies. 7,44,45 The time spent by the elevator to move a distance d is calculated based on Peters 46 as shown in equations (7) to (9).…”

Section: Numerical Examplementioning

confidence: 99%

“…The traffic of one-dimensional elevators is well studied both mathematically and using computers. [1][2][3][4][5][6][7] Two-dimensional elevators have been of great interest recently. 8 The idea of having elevators that use the fac¸ade of the building to move vertically and horizontally is considered both in the research and in the industry.…”

Section: Introductionmentioning

confidence: 99%

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Route selection for a three-dimensional elevator using deep reinforcement learning

Hammoudeh

2019

Building Services Engineering Research and Technology

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While the car of the conventional elevator system moves only vertically in one dimension (up and down), the car of the three-dimensional elevator system travels in three perpendicular dimensions. The elevator moves through a vertical shaft to a certain floor and then the elevator serves multiple passengers distributed among different rooms at that floor. The controller decides which route should be taken to serve the passengers. This article proposes the use of deep reinforcement learning to select a route for the three-dimensional elevator. Deep reinforcement learning method learns from experiencing a large number of scenarios generated using Monte Carlo simulation offline. Once trained, deep reinforcement learning can select the route online. Numerical experimentations are used to show the superiority of deep reinforcement learning in finding an optimum or near optimum-route instantaneously. Although deep reinforcement learning is closer to finding the optimum route than other methods, finding an optimum route is not always guaranteed. Deep reinforcement learning has some limitations that include the long training time and the difficulties in training the neural networks. Practical application:Multidimensional elevators have been of expanding interest to the elevator industry as well as to traffic analysis engineers. This article demonstrates that deep reinforcement learning surpasses other methods in finding an optimum or near-optimum route for the three-dimensional elevator, and it also overcomes the challenges of the non-intelligent methods. This article can help enterprises that develop multidimensional elevators in overcoming the challenges of the controller in addition to boosting the feasibility of multidimensional elevators.

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Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route selection for a three-dimensional elevator using deep reinforcement learning

Hammoudeh

2019

Building Services Engineering Research and Technology

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“…It is worth noting that this combined approach of using the kinematics matrix along with a journey probability matrix has been previously used for finding the value of the round trip time 30 and for estimating the energy consumption. 31…”

Section: The Kinematics Matrixmentioning

confidence: 99%

Traffic analysis of a three-dimensional elevator system

Al-Sharif

Hammoudeh

2017

Building Services Engineering Research and Technology

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“…The main disadvantage with this method is that it assumes that the upper-most entrance is dominant and it does not deal with the cases of unequal floor heights or top speed not attained in one floor journey. e. A formula has been developed to deal with the case of unequal floor heights in Al-Sharif et al 6 The formula develops an expected value for the floor heights where this value is then used in the classical round trip time formula. The formula can cope with both equal and unequal floor populations.…”

Section: Previous Workmentioning

confidence: 99%

“…The formula can cope with both equal and unequal floor populations. f. An equation for the round trip time under incoming traffic conditions has been derived in Khaleel 7 based on the concepts introduced in Al-Sharif et al 6 However, the equations are based on the assumption that the uppermost entrance is the most dominant entrance. g. A set of equations have been developed in Hakonen and Lahdelma 8 to calculate the round trip time assuming a Poisson passenger arrival model, but no verification of the equations was carried out.…”

Section: Previous Workmentioning

confidence: 99%

Derivation of a universal elevator round trip time formula under incoming traffic

Al-Sharif

Alqumsan

Khaleel

2013

Building Services Engineering Research and Technology

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The design of vertical transportation systems still heavily relies on the calculation of the round trip time (). The round trip time () is defined as the average time taken by an elevator to complete a full trip around a building. There are currently two methods for calculating the round trip time: the conventional analytical calculation method and the Monte Carlo simulation method. The conventional analytical method is based on calculating the expected number of stops and the expected highest reversal floor and then substituting the values in the main formula for the round trip time. This method makes some assumptions as to the existence of some special conditions (such as equal floor heights and a single entrance). Where these assumptions are not true in a building, this invalidates the use of the analytical formula the use of which will lead to errors in the result. The conventional analytical equation can be further developed to cover some of the special conditions in the building, but they do not cover all these special conditions and also do not cover combinations of these special conditions. The simplest round trip time equation makes the following assumptions: equal floor heights, a single entrance, equal floor populations and that the rated speed is attained in one floor jump. The case of unequal floor populations can be accounted for by amending the values of the probable number of stops and the highest reversal by using the formulae for the unequal floor population case. The work presented in this piece of work identifies the most important four special conditions (out a total of nine conditions) that are assumed in the classical round trip time analytical equation. It then develops analytical formulae for calculating the round trip time equation for any of the four special conditions or any combination of these conditions under incoming traffic conditions. A numerical example is given and verified using Monte Carlo simulation. Practical application: This piece of work presents new equations that allow the designer to evaluate the value of the round trip time. The equations can deal with special cases such as top speed not attained in one floor journey, multiple entrances, unequal floor heights and unequal floor populations. Once the value of the round trip time is obtained, the elevator system can be designed, providing the required number of elevators, their speed and capacity.

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The use of Monte Carlo simulation in evaluating the elevator round trip time under up-peak traffic conditions and conventional group control

Cited by 25 publications

References 6 publications

Route selection for a three-dimensional elevator using deep reinforcement learning

Route selection for a three-dimensional elevator using deep reinforcement learning

Traffic analysis of a three-dimensional elevator system

Derivation of a universal elevator round trip time formula under incoming traffic

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