Satisficing Optimization
Satisficing Optimization is an optimization framework that, unlike classical optimization which seeks globally optimal solutions, is distinguished by its pursuit of solutions that meet acceptable performance thresholds sufficient to satisfy decision criteria, thereby balancing computational efficiency and decision quality under bounded rationality.
62
Publications
2.2K
Citations
132
Authors
86
Institutions
Publications per year
2017–2026
10
62
Iterative and core-guided MaxSAT solving: A survey and assessment
António Morgado, Federico Heras, Mark H. Liffiton et al. · Constraints · 2013 · 167 citations
Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou · Operations Research · 2022 · 108 citations
1–5 of 62
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Chinese University of Hong Kong, Hong Kong SAR China
2 publications · 125 citations
2
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1–5 of 132
86

Hiroshima University
Hiroshima, Japan
10 h-index
24 publications
378 citations
National University of Singapore
Singapore, Singapore
3 h-index
5 publications
197 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
2 publications
227 citations
Panasonic (Japan)
Kadoma, Japan
2 h-index
2 publications
44 citations
The University of Osaka
Osaka, Japan
2 h-index
2 publications
37 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
2 h-index
2 publications
125 citations
Technion – Israel Institute of Technology
Haifa, Israel
2 h-index
5 publications
185 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
2 publications
77 citations
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4 publications · 74 citations
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2 publications · 134 citations
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2 publications · 23 citations
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2 publications · 54 citations
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2 publications · 118 citations
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