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ISSN:
2319-6009 (Online)
Abbreviated title:
Int. J Struct. Civ. Eng. Res.
Editor-in-Chief:
Prof. Eric Strauss
Associate Editor:
Assoc. Prof. Wenxing Zhou
Executive Editor:
Ms. Cherry L. Chen
DOI:
10.18178/ijscer
Abstracting/Indexing:
Google Scholar, Cross-ref, CNKI,
etc.
E-mail questions to:
IJSCER Editorial Office
.
Editor-in-Chief
Prof. Eric Strauss
Michigan State University, USA
I am very excited to serve as the Editor-in-Chief of the International Journal of Structural and Civil Engineering Research
(IJSCER)
and hope that the publication can enrich the readers’ experience...
What's New
2024-08-27
August 27th, 2024 News! Vol. 13, No. 3, 2024 issue has been published online
2024-01-04
IJSCER will adopt Article-by-Article Work Flow. For the quarterly journal, each issue will be released at the end of the issue month.
2023-11-06
November 6th, 2023 News! Vol. 12, No. 4, November 2023 issue has been published online
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Volume 6, No. 2, May 2017
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Heavy Equipment Demand Prediction with Support Vector Machine Regression Towards a Strategic Equipment Management
A. Kargul
1
, A. Glaese
2
, S. Kessler
2
, and W. A. Günthner
2
1. Technical University Munich, Institute for Materials Handling, Material Flow, Logistics, Munich, Germany
2. Technical University Munich, Institute for Materials Handling, Material Flow, Munich, Germany
Abstract
—Equipment owner have realized that professionalized equipment management offers cost advantages. The procurement strategy as one of the most important tasks for equipment managers changed from simply buying heavy equipment to make use of different options regarding leases and sales. Nevertheless, a strategic and cost efficient heavy equipment procurement is only one import step towards a strategic equipment management. As a next step there is need to improve the utilization of heavy equipment regarding equipment logistics, maintenance and repair to increase return on investment over the equipment’s lifecycle. Therefore, the paper presents an approach to predict a reliable heavy equipment demand by computing the monthly utilization rate with support vector machines regression. In total, sample data of over 111 construction projects between 2013 and 2015 is computed. A better knowledge of the upcoming equipment demand for future projects allows to progress from an ad-hoc equipment management to a data-driven strategic equipment management. Benefits of the presented approach are discussed in order to increase return of investment by renting out unused equipment or in order to balance out the heavy equipment fleet by reducing respectively buying new equipment.
Index Terms
—heavy equipment management, support vector machine regression, prediction models
Cite: A. Kargul, A. Glaese, S. Kessler, and W. A. Günthner, "Heavy Equipment Demand Prediction with Support Vector Machine Regression Towards a Strategic Equipment Management," International Journal of Structural and Civil Engineering Research, Vol. 6, No. 2, pp. 137-143, May 2017. doi: 10.18178/ijscer.6.2.137-143
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