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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2018

08.08.2016 | Original Article

An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station

verfasst von: Masoud Rahiminezhad Galankashi, Ehsan Fallahiarezoudar, Anoosh Moazzami, Syed Ahmad Helmi, Jafri Mohd Rohani, Noordin Mohd Yusof

Erschienen in: Neural Computing and Applications | Ausgabe 4/2018

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Abstract

This study proposed an incorporated simulation–Taguchi model to optimize a petrol station sales rate. In addition, it provided a regression model to forecast the sales rate. Initially, Witness 2014 simulation software© was used to simulate the operating system of a petrol station. Next, the obtained simulation results were used as the input for Taguchi method to optimize the process. Taguchi L 4 standard orthogonal array was taken to optimize the petrol station parameters including the number of pumps, number of cashiers and customers’ interarrival times (IATs) to obtain a better sales rate. Three noise factors such as petrol station location, different cashiers and different dispensers considered as potential factors affecting the response. Based on Taguchi methodology, number of pumps and IAT were identified as highly contributing factors on the sales rate. The remaining factor (number of cashier) similarly influences the response, but the effect is not very significant. Therefore, the importance sequence of the sales rate parameter is IATs > number of pumps > number of cashiers. The regression equation was formulated to maximize the sales rate (Liter) and then verified by the confirmation runs.
Vorheriger Artikel Topological Gaussian ARAM for biologically inspired topological map building
Nächster Artikel Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system

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Metadaten
Titel
An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station
verfasst von
Masoud Rahiminezhad Galankashi
Ehsan Fallahiarezoudar
Anoosh Moazzami
Syed Ahmad Helmi
Jafri Mohd Rohani
Noordin Mohd Yusof
Publikationsdatum
08.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2491-5

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