Comparison on queuing performance measures on customer’s flow using Queuing Theory Model and DSW Algorithm / Muhammad Izuanudin Azmi ... [et al.]

• Main Authors: Azmi, Muhammad Izuanudin, Azizul, Muhamad Aiman Zulhaikal, Abd Razak, Nurul Aisyah, Mohd Zaki, Noor Hidayah
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA, Negeri Sembilan 2023
• Subjects: Mathematical statistics. Probabilities
• Online Access: https://ir.uitm.edu.my/id/eprint/85290/1/85290.pdf; https://ir.uitm.edu.my/id/eprint/85290/
• Institution: Universiti Teknologi Mara

A queue is a line of people or things that are waiting to be handled. At any location, queues can form, including supermarkets, hospitals, manufacturing firms, and more (Priyangika & Cooray, 2016). The results of analyzing the queuing system are often used to make business decisions about waiting lines. Customer flow refers to the orderliness of a customer's trip which is important for any kind of business. The more efficient the customer flow, the higher the customer's satisfaction. Business owners can gain an understanding of customer flow by analysing flow patterns. Queuing theory is an area of mathematics concerned with the study and modelling the behaviour of waiting in lines (Whiting,2012). Queuing theory has been applied in many fields such as telecommunications, transportation, logistics, and more. The theory is used to calculate the length of time a customer must wait in a queuing situation (Yakubu & Najim, 2014). Fuzzy Queuing Model for the a-cut technique is based on the Dong, Shah, and Wong (DSW) algorithm. In a multi-server fuzzy queuing model, the approximate technique DSW algorithm is employed to describe a membership function regarding the performance measures (Thamotharan,2016). DSW approach simplifies the manipulation of the extension principle for continuous-valued fuzzy variables such as fuzzy integers defined on the real line. This research focuses on the queuing line system used by customers. The purpose of this research is to compare the queuing performance measures of a queuing system at service counters and to determine customer flow using the Queuing Theory Model and DSW Algorithm. There were five variables involved which were the number of customers, the arrival time of customers, the start and end of services, and average service time.