@article {10.3844/jcssp.2023.1098.1106,
article_type = {journal},
title = {Fuzzy Logic-Based Truck Demand Computational Model},
author = {Mauludin, Anugrah and Utama, Ditdit Nugeraha},
volume = {19},
number = {9},
year = {2023},
month = {Aug},
pages = {1098-1106},
doi = {10.3844/jcssp.2023.1098.1106},
url = {https://thescipub.com/abstract/jcssp.2023.1098.1106},
abstract = {Trucking is one of the essential aspects linking suppliers and customers in many countries. Services at a trucking service provider will be based on a first-come-first-served basis. When a customer contacts a trucking service provider, there is a possibility that all the truck units owned are in fact being used to serve other customers causing potential delays in their delivery arrangement. For the trucking service provider, the truck demand prediction is not well observed resulting in a loss of business opportunities. The purpose of this research is to develop a truck demand prediction model using the Chen Fuzzy Time Series (FTS) method. The dataset used in this research was 11 years of monthly trucking demand information from a trucking service provider in Batam City, Indonesia. The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 4.4%, this result indicates that the prediction was very accurate. After model deployment, expected can improve the readiness of trucking units.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}