Name
Abstract | ||
|---|---|---|
This paper presents a hybrid AI approach for a class of overconstrained Nurse Rostering Problems. Our approach comes in two phases. The first phase solves a relaxed version of problem which only includes hard rules and part of nurses' requests for shifts. This involves using a forward checking algorithm with non-binary constraint propagation, variable ordering, random value ordering and compulsory backjumping. In the second phase, adjustments with descend local search and tabu search are applied to improved the solution. This is to satisfy the preference rules as far as possible. Experiments show that our approach is able to solve this class of problems well. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1145/952532.952675 | SAC |
Keywords | Field | DocType |
compulsory backjumping,tabu search,descend local search,forward checking algorithm,preference rule,overconstrained nurse rostering problems,non-binary constraint propagation,random value,hard rule,hybrid ai approach,satisfiability,forward checking,local search,constraint propagation,spanning trees,genetic algorithms | Local consistency,Mathematical optimization,Nursing,Computer science,Nurse scheduling problem,Look-ahead,Spanning tree,Local search (optimization),Backjumping,Genetic algorithm,Tabu search | Conference |
ISBN | Citations | PageRank |
1-58113-624-2 | 19 | 1.11 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibing Li | 1 | 19 | 1.11 |
| Andrew Lim | 2 | 373 | 21.86 |
| Brian Rodrigues | 3 | 78 | 8.07 |