Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106256
DC Field | Value | Language |
---|---|---|
dc.contributor | College of Professional and Continuing Education | en_US |
dc.creator | Li, BT | en_US |
dc.creator | Chen, Q | en_US |
dc.creator | Lau, YY | en_US |
dc.creator | Dulebenets, MA | en_US |
dc.date.accessioned | 2024-05-03T00:46:04Z | - |
dc.date.available | 2024-05-03T00:46:04Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106256 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Li B, Chen Q, Lau Y-y, Dulebenets MA. Tugboat Scheduling with Multiple Berthing Bases under Uncertainty. Journal of Marine Science and Engineering. 2023; 11(11):2180 is available at https://dx.doi.org/10.3390/jmse11112180. | en_US |
dc.subject | Tugboat scheduling | en_US |
dc.subject | Multiple berthing bases | en_US |
dc.subject | Fuzzy programming | en_US |
dc.subject | Genetic operators | en_US |
dc.subject | Grey Wolf Algorithm | en_US |
dc.title | Tugboat scheduling with multiple berthing bases under uncertainty | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | en_US |
dc.identifier.issue | 11 | en_US |
dc.identifier.doi | 10.3390/jmse11112180 | en_US |
dcterms.abstract | This study proposes a novel fuzzy programming optimization model for tugboat scheduling, directly considering multiple berthing bases, time windows, and operational uncertainties. The uncertainties in the required number of tugboats, the earliest start time, the latest start time, the processing time, and the start and end locations of each task are directly captured in the proposed fuzzy optimization model. The objective of the presented formulation is to minimize the total cost of fuel and delays. According to the characteristics of the problem, a Grey Wolf Optimization algorithm based on random probability encoding and custom genetic operators is proposed. The proposed algorithm, LINGO, the canonical Grey Wolf Optimization algorithm, and particle swarm optimization were used to compare and analyze the results of several examples. The results validate the efficiency of the proposed algorithm against the alternative exact and metaheuristics methods. Moreover, the findings from the conducted sensitivity analysis show the applicability of the developed fuzzy programming model for different confidence interval levels. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of marine science and engineering, Nov. 2023, v. 11, no. 11, 2180 | en_US |
dcterms.isPartOf | Journal of marine science and engineering | en_US |
dcterms.issued | 2023-11 | - |
dc.identifier.isi | WOS:001120810000001 | - |
dc.identifier.eissn | 2077-1312 | en_US |
dc.identifier.artn | 2180 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Fujian Provincial Department of Education | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
jmse-11-02180.pdf | 1.57 MB | Adobe PDF | View/Open |
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