Local search-based meta-heuristics combined with an improved K-Means++ clustering algorithm for unmanned surface vessel scheduling

Unmanned surface vessels (USVs) play an important role in marine field, which can improve the efficiency and safety of task execution in hazardous environments. The applications of artificial intelligence technologies on USV collaboration scheduling can guide the USV cluster intelligence. In this st...

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Bibliographic Details
Published in:International journal of production research Vol. 63; no. 17; pp. 6339 - 6363
Main Authors: Tang, Weiyu, Gao, Kaizhou, Ma, Zhenfang, Lin, Zhongjie, Yu, Hui, Huang, Wuze, Wu, Naiqi
Format: Journal Article
Language:English
Published: London Taylor & Francis 02.09.2025
Taylor & Francis LLC
Subjects:
Algorithms
Artificial intelligence
Clustering
Completion time
Hazardous areas
Heuristic
innovation andinfrastructure
K-Means
local search
meta-heuristic
Scheduling
SDG 9: Industry
Searching
Solution space
Statistical analysis
Statistical tests
Technology
Unmanned surface vessel
Vessels
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:Unmanned surface vessels (USVs) play an important role in marine field, which can improve the efficiency and safety of task execution in hazardous environments. The applications of artificial intelligence technologies on USV collaboration scheduling can guide the USV cluster intelligence. In this study, the scheduling problems of USVs are solved by five local search-based meta-heuristics combining with an improved K-Means++ algorithm. The objective is to minimise the maximum completion time of USVs. For task assignment of USVs, an improved K-Means++ clustering (IKC) algorithm is proposed. The assignment results are used to initialise the population of meta-heuristics. According to the characteristics of the concerned problems and the structure of the solution space, six local search operators are designed to improve the convergence of meta-heuristics. Finally, the proposed strategies are integrated to five meta-heuristics and their performance are verified by solving 40 instances with different scales. Experimental results and statistical tests prove the strong competitiveness of the proposed algorithms. From the statistical analysis, the local search-based harmony search with the IKC algorithm performs better than the compared ones for solving the concerned problems.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2025.2470991