P
Pascal Van Hentenryck
Researcher at Georgia Institute of Technology
Publications - 540
Citations - 18829
Pascal Van Hentenryck is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Constraint programming & Constraint satisfaction. The author has an hindex of 62, co-authored 506 publications receiving 17042 citations. Previous affiliations of Pascal Van Hentenryck include University at Albany, SUNY & Australian National University.
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Book
Constraint satisfaction in logic programming
TL;DR: Van Hentenryck as mentioned in this paper proposes a new approach to solving discrete combinatorial problems using consistency techniques. But this approach is not suitable for many real-world problems, such as disjunctive scheduling, warehouse location, cutting stock car sequencing, and microcode labeling.
Journal Article
The Constraint Logic Programming Language CHIP.
Mehmet Dincbas,Pascal Van Hentenryck,Helmut Simonis,Abderrahmane Aggoun,Thomas Graf,Françoise Berthier +5 more
Book
The OPL optimization programming language
TL;DR: The language: a short tour of OPL models data modelling expressions and constraints formal parameters search display and the application areas: linear and integer programming constraint programming scheduling.
Integration of AI and OR techniques in constraint programming for combinatorial optimization problems : 4th International Conference, CPAIOR 2007, Brussels, Belgium, May 23-26, 2007 : proceedings
TL;DR: Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches and Connections in Networks: Hardness of Feasibility Versus Optimality.
Journal ArticleDOI
Rapid assessment of disaster damage using social media activity.
Yury Kryvasheyeu,Yury Kryvasheyeu,Haohui Chen,Haohui Chen,Nick Obradovich,Nick Obradovich,Esteban Moro,Pascal Van Hentenryck,Pascal Van Hentenryck,Pascal Van Hentenryck,James H. Fowler,Manuel Cebrian,Manuel Cebrian +12 more
TL;DR: It is shown that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter’s message stream, and suggested that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.