Metaheuristic Batch Scheduling
Metaheuristic Batch Scheduling is a batch scheduling approach that belongs to the class of optimization methods relying on high-level, problem-independent search strategies, distinguished by its use of metaheuristic algorithms—such as genetic algorithms, simulated annealing, or particle swarm optimization—to efficiently explore large solution spaces and find near-optimal batch sequences in complex, constrained environments.
32
Publications
2K
Citations
73
Authors
38
Institutions
Publications per year
2017–2026
16
Parents
32
1–5 of 32
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38

Anhui University
Hefei, China
3 h-index
7 publications
170 citations

University of the Witwatersrand
Johannesburg, South Africa
3 h-index
7 publications
86 citations

Hefei University of Technology
Hefei, China
2 h-index
3 publications
73 citations
New Jersey Institute of Technology
Newark, United States
2 h-index
2 publications
45 citations

University of L'Aquila
L’Aquila, Italy
1 h-index
1 publications
24 citations
National Pingtung University of Science and Technology
Pingtung City, Taiwan
1 h-index
1 publications
45 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
2 publications
1.6K citations
Tsinghua University
Beijing, China
1 h-index
3 publications
261 citations
1–8 of 38
6 publications · 219 citations
7
h-index
3 publications · 150 citations
3
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2 publications · 816 citations
2
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2 publications · 118 citations
2
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2 publications · 198 citations
2
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1–5