Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach

This study deals with the assessment of decision making factors affecting dump truck allotment in the construction of gasoline service stations in the Philippines. Such factors considered in this study were the following: (a) location of project site, (b) project duration, (c) distance of the source...

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Main Authors: Alimon, Catherine P., Lumanlan, Raymund Francis D., Macabagdal, Jommel R.
Format: text
Language:English
Published: Animo Repository 2007
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/10788
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-114332022-02-02T08:33:29Z Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach Alimon, Catherine P. Lumanlan, Raymund Francis D. Macabagdal, Jommel R. This study deals with the assessment of decision making factors affecting dump truck allotment in the construction of gasoline service stations in the Philippines. Such factors considered in this study were the following: (a) location of project site, (b) project duration, (c) distance of the source materials (such as soil) from the the project site (d) volume of backfill, (e) depth of backfill, and (f) total project lot area. Upon conducting data gathering, the group came to a conclusion that the most significant decision making factors among those mentioned above were the volume of backfill and distance of source materials from project site. Moreover, throughout the course of the study, the group also found out that there are actually two methods of site development processes affecting dump truck allotment the Gradual and Rapid methods from which the factor of project duration is taken into account. The group utilized the MATLAB software, particularly of the supervised network approach in ANN analysis using the Learning Vector Quantization (LVQ) technique. This was performed in order to recognize patterns among the volume of backfill and distance of source materials so as to arrive with the best pattern-based mathematical models for each method. 2007-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/10788 Bachelor's Theses English Animo Repository Dump truck Dumping appliances Service stations Petroleum industry and trade Civil Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Dump truck
Dumping appliances
Service stations
Petroleum industry and trade
Civil Engineering
spellingShingle Dump truck
Dumping appliances
Service stations
Petroleum industry and trade
Civil Engineering
Alimon, Catherine P.
Lumanlan, Raymund Francis D.
Macabagdal, Jommel R.
Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
description This study deals with the assessment of decision making factors affecting dump truck allotment in the construction of gasoline service stations in the Philippines. Such factors considered in this study were the following: (a) location of project site, (b) project duration, (c) distance of the source materials (such as soil) from the the project site (d) volume of backfill, (e) depth of backfill, and (f) total project lot area. Upon conducting data gathering, the group came to a conclusion that the most significant decision making factors among those mentioned above were the volume of backfill and distance of source materials from project site. Moreover, throughout the course of the study, the group also found out that there are actually two methods of site development processes affecting dump truck allotment the Gradual and Rapid methods from which the factor of project duration is taken into account. The group utilized the MATLAB software, particularly of the supervised network approach in ANN analysis using the Learning Vector Quantization (LVQ) technique. This was performed in order to recognize patterns among the volume of backfill and distance of source materials so as to arrive with the best pattern-based mathematical models for each method.
format text
author Alimon, Catherine P.
Lumanlan, Raymund Francis D.
Macabagdal, Jommel R.
author_facet Alimon, Catherine P.
Lumanlan, Raymund Francis D.
Macabagdal, Jommel R.
author_sort Alimon, Catherine P.
title Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
title_short Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
title_full Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
title_fullStr Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
title_full_unstemmed Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach
title_sort assessment of decision-making factors affecting dump truck allotment: an artificial neural network approach
publisher Animo Repository
publishDate 2007
url https://animorepository.dlsu.edu.ph/etd_bachelors/10788
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