in Journal   Posted on September 23, 2020

Journal Ranking & Metrics

G2R Score : 1.98
G2R H-Index : 7
JCR Impact Factor : 1.167
Scopus Citescore : 4.4
SCIMAGO H-index : 43
Guide2Research Overall Ranking : 508

Journal Information

ISSN : 1383-7133
Publisher :
Periodicity : Quarterly
Editors-in-Chief : Michela Milano
Journal & Submission Website :

Top Scientists who published in this Journal

Number of top scientists* : 12
Documents published by top scientists* : 16
* Based on data published during the last three years.

Aims & Scope of the Journal

Constraints provides a common forum for the many disciplines interested in constraint programming and constraint satisfaction and optimization, and the many application domains in which constraint technology is employed. It covers all aspects of computing with constraints: theory and practice, algorithms and systems, reasoning and programming, logics and languages. Relevant disciplines and application domains include, but are not limited to: Disciplines:  Artificial Intelligence, Automated Reasoning, Combinatorial Algorithms, Databases, Discrete Mathematics, Operations Research, Programming Languages, Satisfiability and Computational LogicDomains: Agents, Bioinformatics, Design and Configuration, Graphics, Visualization, User Interfaces, Human-Computer Interaction and Decision Support, Robotics, Machine Vision and Computational Linguistics, Scheduling, Planning, Resource Allocation, Temporal and Spatial Reasoning Papers that cut across disciplinary lines, or that combine theory and practice, are especially welcome. The journal will also consider: Survey papers that provide a full-length, state-of-the-art review on a well-defined topic;Application papers presenting applications of Constraint Programming in areas such as industry, education, health and government. Papers describing real-life oriented benchmark problems, especially comparing constraint formulations with other solution techniques, will also be considered;Letters presenting important technical results, experimental results providing a relevant evaluation of a previously proposed algorithm, or improvements and corrections of results already in the literature.  Officially cited as: Constraints