Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems

Pengpeng Lin; Jun Zhang; Marco A. Contreras

doi:10.1109/IRI.2014.7051970

Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems

Pengpeng Lin, Jun Zhang, Marco A. Contreras

Computer Science

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

7 Scopus citations

Abstract

Problems related to the transportation of timber products have traditionally involved finding routes that minimize timber hauling and road construction costs. However, increasing environmental concerns have introduced negative impacts (i.e., soil erosion and water quality) into forest transportation planning problems (FTPPs). In this paper, we designed and implemented a multi-objective ant colony optimization algorithm (MOACO) to solve a bi-objective FTPP that considers both transportation cost and environmental impacts. The goal is to provide decision makers with different timber transportation planning alternatives to help them make informed decisions. The MOACO incorporates various design choices that have been identified to have better performances in recent research literature. To test for performance, we applied the algorithm to ten FTPPs. Experimental results demonstrate the MOACO was able to solve all test problems under different stop conditions.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014
Editors	Elisa Bertino, Bhavani Thuraisingham, Ling Liu, James Joshi
Pages	795-802
Number of pages	8
ISBN (Electronic)	9781479958801
DOIs	https://doi.org/10.1109/IRI.2014.7051970
State	Published - Feb 27 2014
Event	15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014 - San Francisco, United States Duration: Aug 13 2014 → Aug 15 2014

Publication series

Name	Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014

Conference

Conference	15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014
Country/Territory	United States
City	San Francisco
Period	8/13/14 → 8/15/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

Information Systems

Access to Document

10.1109/IRI.2014.7051970

Cite this

Lin, P., Zhang, J., & Contreras, M. A. (2014). Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems. In E. Bertino, B. Thuraisingham, L. Liu, & J. Joshi (Eds.), Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014 (pp. 795-802). Article 7051970 (Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014). https://doi.org/10.1109/IRI.2014.7051970

Lin, Pengpeng ; Zhang, Jun ; Contreras, Marco A. / Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems. Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014. editor / Elisa Bertino ; Bhavani Thuraisingham ; Ling Liu ; James Joshi. 2014. pp. 795-802 (Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014).

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abstract = "Problems related to the transportation of timber products have traditionally involved finding routes that minimize timber hauling and road construction costs. However, increasing environmental concerns have introduced negative impacts (i.e., soil erosion and water quality) into forest transportation planning problems (FTPPs). In this paper, we designed and implemented a multi-objective ant colony optimization algorithm (MOACO) to solve a bi-objective FTPP that considers both transportation cost and environmental impacts. The goal is to provide decision makers with different timber transportation planning alternatives to help them make informed decisions. The MOACO incorporates various design choices that have been identified to have better performances in recent research literature. To test for performance, we applied the algorithm to ten FTPPs. Experimental results demonstrate the MOACO was able to solve all test problems under different stop conditions.",

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Lin, P, Zhang, J & Contreras, MA 2014, Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems. in E Bertino, B Thuraisingham, L Liu & J Joshi (eds), Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014., 7051970, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014, pp. 795-802, 15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014, San Francisco, United States, 8/13/14. https://doi.org/10.1109/IRI.2014.7051970

Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems. / Lin, Pengpeng; Zhang, Jun; Contreras, Marco A.
Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014. ed. / Elisa Bertino; Bhavani Thuraisingham; Ling Liu; James Joshi. 2014. p. 795-802 7051970 (Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014).

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

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Lin P, Zhang J, Contreras MA. Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems. In Bertino E, Thuraisingham B, Liu L, Joshi J, editors, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014. 2014. p. 795-802. 7051970. (Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014). doi: 10.1109/IRI.2014.7051970

Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems

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