Determination of Customers’ Arrival and Service Patterns for a Restaurant Food Serving Process

Main Article Content

Latifat A. Odeniyi
Rafiu A. Ganiyu
Elijah O. Omidiora
Stephen O. Olabiyisi

Abstract

Restaurant industry has become one of the most profitable industries in the world where incessant long waiting time may not only lead to customers’ dissatisfactions but also facilitate loosing of customers to other competitors. In this paper, in order to determine customers’ arrival patterns and service patterns which are critical factors in determining customers’ queue length and waiting time for a given restaurant, the food serving process employed at a named International Institute Restaurant (IIR), Ibadan, Nigeria, was used as a case study. Data were collected on customers’ number, customers’ inter arrival time and service time from Monday to Friday for a week. The data were analyzed statistically using the ARENA Input Analyzer to determine the arrival patterns and service patterns of customers within five working days of the week (Monday, Tuesday, Wednesday, Thursday and Friday). The results of the data analysis revealed that the arrival times of customers who patronized the IIR on Monday and Tuesday followed a Beta distribution. Furthermore, the arrival times of customers patronizing the IIR on Wednesday and Thursday followed a Weibull distribution while that of Friday assumed an Erlang distribution. Besides, the results of the data analysis revealed that the service times at IIR on Monday and Tuesday followed a Lognormal distribution. Beta, Lognormal and Weibull distributions were recorded in respect of service times characterizing the IIR on Wednesday, Thursday and Friday, respectively.

Keywords:
Restaurant, customer, arrival pattern, service pattern, exponential distribution.

Article Details

How to Cite
Odeniyi, L. A., Ganiyu, R. A., Omidiora, E. O., & Olabiyisi, S. O. (2020). Determination of Customers’ Arrival and Service Patterns for a Restaurant Food Serving Process. Asian Journal of Research in Computer Science, 5(4), 13-24. https://doi.org/10.9734/ajrcos/2020/v5i430140
Section
Original Research Article

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