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| description |
Today, sensors and cameras are often used to monitor the movement
and behavior of pedestrians, especially where there are a huge
number of visitors. The classical usage of such devices, for example
in a theme park, is to identify the queue size in front of each
attraction and thereby to predict the time it takes until the visit
can be completed. Work has been done in the past to use statistical
data that resembles the data collected by such devices to simulate
the pedestrian behavior. As a result, the congestions as well as the
queue sizes at different times can be predicted. This work aims in
using the data obtained from the simulation to optimally schedule a
list of tasks to be executed as well as to find an optimal path
between each destination. As an example, one might think of a
scenario where a customer enters a theme park would wish to visit as
many attractions as possible in the alloted time or a large clinic
where a patient has to be routed through various departments such as
registration, OP, X-Ray, ward, etc. The problem involves finding the
optimal sequence of the tasks and determining the fastest path
between the destinations, both combined. Since the data varies over
time, the problem is time dependent or dynamic. In the past, several
methods have been proposed to solve dynamic shortest path algorithms
and scheduling problems. However, due to the stochastic nature of
the available data, it is not necessary to find the best schedule
and route that takes the minimum amount of time but, it is rather
important to find an optimal solution in a short time. In this
paper, we study and compare different combinatorial optimization
methods and heuristics that can used to determine an optimal
schedule.
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| publisher |
Nottingham, UK: The University of Nottingham
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| type |
Text
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| Article in Proceedings
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| source |
In: Qu, Rong (ed.): Proceedings of the 25th workshop of the UK
Planning and Scheduling Special Interest Group (PlanSIG'06),
pp. 97-104
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| contributor |
IPVS, Simulation großer Systeme
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| subject |
Nonnumerical Algorithms and Problems (CR F.2.2)
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| Discrete Mathematics Combinatorics (CR G.2.1)
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| Probability and Statistics (CR G.3)
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| Simulation and Modeling (CR I.6)
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| relation |
UK Planning and Scheduling Special Interest Group
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