Optimizing number of effective-efficient counters using poisson queuing model / Nurul Elfieqah Rostam

Rostam, Nurul Elfieqah (2021) Optimizing number of effective-efficient counters using poisson queuing model / Nurul Elfieqah Rostam. Degree thesis, Universiti Teknologi Mara Perlis.


Train is the most preferred public transport in urban area. However, passengers are often delayed by ineffective and inefficient counter services. They often have to queue to buy a ticket. The purpose of this study was to optimize the number of effective and efficient counters at a train station. Specifically, this study attempted to identify the optimal number of opened counters at one time and to determine the minimum time taken for a passenger to buy a ticket. To achieve both objectives, the researcher has chosen to apply Poisson Queuing Simulation (PQ) to the Morning Shift (MS) and Evening Shift (ES) data at a train station. When the interval was capped to 25 minutes (the upper time limit for passengers to catch the morning train), the total time taken by passengers in three MS queues were 16.80 minutes, 14.70 minutes, and 12.60 minutes, respectively. Therefore, the optimal number of counters to be opened was 1 counter. Total time taken for three ES queues were 28.51 minutes, 20.18 minutes and 17.58 minutes respectively. Hence, a maximum of 2 counters were needed for the ES if the number of passengers were to exceed the normal flow. Based on these findings it is recommended that KTMB analyses the number of passengers using their stations to determine the optimal number of opened counters for all stations.


Item Type: Thesis (Degree)
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Rostam, Nurul Elfieqah
Subjects: H Social Sciences > HE Transportation and Communications > Urban transportation
Q Science > QA Mathematics > Numerical simulation. Monte Carlo method
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Computer and Mathematical Sciences
Programme: Management Mathematics
Keywords: Optimization; Poisson Queuing Simulation; Public Transport; Queuing; Train
Date: 31 March 2021
URI: https://ir.uitm.edu.my/id/eprint/44613
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