Arc and path consistence revisited
Artificial Intelligence
Backtrack-free and backtrack-bounded search
Search in Artificial Intelligence
Network-based heuristics for constraint satisfaction problems
Search in Artificial Intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
A methodology for object-oriented constraint programming
APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
A nurse rostering system using constraint programming and redundant modeling
IEEE Transactions on Information Technology in Biomedicine
A hybrid approach for solving real-world nurse rostering problems
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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Constraint programming techniques have been widely used in many different types of applications. However for NP-hard problems, such as scheduling, resources allocation, etc, basic constraint programming techniques may not be enough solve efficiently. This paper describes a design and implementation of a simplified nurse rostering system using constraint programming and automatic implied constraint generation by meta-level reasoning. The nurse rostering system requires generating a weekly timetable by assigning work shifts to nurse. Although the problem set is simplified, the search is difficult because it involves more than hundred constraints with a search space of about $3.74 \times 10^{50}$. Using only traditional constraint programming techniques, even in addition with popular heuristics, no timetable can be generated in reasonable time. To improve the search, we propose to use automatic implied constraint generation by meta-level reasoning. Several solvable and non-solvable problem instances were tested. With our approach, these instances can be solved or identified as nonsolvable within one second.