A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective

Chia Rong Su; Chang Shu Tu; Ching Ter Chang

doi:10.1109/ICMLC.2015.7340652

A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective

Chia Rong Su, Chang Shu Tu, Ching Ter Chang

Graduate Institute of Mangement

Chang Gung University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The aircraft tractor supplier selection (ATSS) is a critical issue for the sustainable management of the airport ground handling service (AGHS) company. It also guarantees the airport work safety. Although the AGHS aircraft tractor supplier problems have an important role in the safety of the ground handling ramp operations, few studies have analyzed the ATSS problem of the AGHS company. The objective of this paper is to present a new model from a financial perspective. In order to assist managers of the AGHS company to select the optimal aircraft tractor suppliers, we provide a real case in Taiwan for the proposed models.

Original language	English
Title of host publication	Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015
Publisher	IEEE Computer Society
Pages	771-776
Number of pages	6
ISBN (Electronic)	9781467372213
DOIs	https://doi.org/10.1109/ICMLC.2015.7340652
State	Published - 30 11 2015
Event	14th International Conference on Machine Learning and Cybernetics, ICMLC 2015 - Guangzhou, China Duration: 12 07 2015 → 15 07 2015

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	14th International Conference on Machine Learning and Cybernetics, ICMLC 2015
Country/Territory	China
City	Guangzhou
Period	12/07/15 → 15/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Analytic hierarchy process
Delphi method
Involvement cost-factor measures
Multi-choice goal programming
Supplier selection

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICMLC.2015.7340652

Cite this

Su, C. R., Tu, C. S., & Chang, C. T. (2015). A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective. In Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015 (pp. 771-776). Article 7340652 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2015.7340652

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title = "A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective",

abstract = "The aircraft tractor supplier selection (ATSS) is a critical issue for the sustainable management of the airport ground handling service (AGHS) company. It also guarantees the airport work safety. Although the AGHS aircraft tractor supplier problems have an important role in the safety of the ground handling ramp operations, few studies have analyzed the ATSS problem of the AGHS company. The objective of this paper is to present a new model from a financial perspective. In order to assist managers of the AGHS company to select the optimal aircraft tractor suppliers, we provide a real case in Taiwan for the proposed models.",

keywords = "Analytic hierarchy process, Delphi method, Involvement cost-factor measures, Multi-choice goal programming, Supplier selection",

author = "Su, \{Chia Rong\} and Tu, \{Chang Shu\} and Chang, \{Ching Ter\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 14th International Conference on Machine Learning and Cybernetics, ICMLC 2015 ; Conference date: 12-07-2015 Through 15-07-2015",

year = "2015",

month = nov,

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doi = "10.1109/ICMLC.2015.7340652",

language = "英语",

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Su, CR, Tu, CS & Chang, CT 2015, A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective. in Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015., 7340652, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2, IEEE Computer Society, pp. 771-776, 14th International Conference on Machine Learning and Cybernetics, ICMLC 2015, Guangzhou, China, 12/07/15. https://doi.org/10.1109/ICMLC.2015.7340652

A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective. / Su, Chia Rong; Tu, Chang Shu; Chang, Ching Ter.
Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015. IEEE Computer Society, 2015. p. 771-776 7340652 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The aircraft tractor supplier selection (ATSS) is a critical issue for the sustainable management of the airport ground handling service (AGHS) company. It also guarantees the airport work safety. Although the AGHS aircraft tractor supplier problems have an important role in the safety of the ground handling ramp operations, few studies have analyzed the ATSS problem of the AGHS company. The objective of this paper is to present a new model from a financial perspective. In order to assist managers of the AGHS company to select the optimal aircraft tractor suppliers, we provide a real case in Taiwan for the proposed models.

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Su CR, Tu CS, Chang CT. A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective. In Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015. IEEE Computer Society. 2015. p. 771-776. 7340652. (Proceedings - International Conference on Machine Learning and Cybernetics). doi: 10.1109/ICMLC.2015.7340652

A hybrid model to resolve aircraft tractor supplier's selection problem from a financial perspective

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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