Off-day scheduling with hierarchical worker categories
Operations Research
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Improving repair-based constraint satisfaction methods by value propagation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A tabu search heuristic for the vehicle routing problem
Management Science
Constraint tightness and looseness versus local and global consistency
Journal of the ACM (JACM)
Solving a timetabling problem using hybrid genetic algorithms
Software—Practice & Experience
Software—Practice & Experience
LOCAL ++: A C++ framework for local search algorithms
Software—Practice & Experience
Tabu Search
Automated Assignment and Scheduling of Service Personnel
IEEE Expert: Intelligent Systems and Their Applications
A Constraint-Based High School Scheduling System
IEEE Expert: Intelligent Systems and Their Applications
Employee Timetabling, Constraint Networks and Knowledge-Based Rules: A Mixed Approach
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Experiments on Networks of Employee Timetabling Problems
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Combining local search and look-ahead for scheduling and constraint satisfaction problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Look-ahead value ordering for constraint satisfaction problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Combining local search and backtracking techniques for constraint satisfaction
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
The State of the Art of Nurse Rostering
Journal of Scheduling
Generalizing Global Constraints Based on Network Flows
Recent Advances in Constraints
Staff Scheduling with Particle Swarm Optimisation and Evolution Strategies
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
An overview of AI research in Italy
Artificial intelligence
Days-off scheduling for a bus transportation company
International Journal of Innovative Computing and Applications
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
A CSP search algorithm with reduced branching factor
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Optimizing the unlimited shift generation problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
A Time Predefined Variable Depth Search for Nurse Rostering
INFORMS Journal on Computing
Alternative MIP formulations for an integrated shift scheduling and task assignment problem
Discrete Applied Mathematics
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Employee timetabling is the operation of assigning employees to tasks in a set of shifts during a fixed period of time, typically a week. We present a general definition of employee timetabling problems (ETPs) that captures many real-world problem formulations and includes complex constraints. The proposed model of ETPs can be represented in a tabular form that is both intuitive and efficient for constraint representation and processing. The constraint networks of ETPs include non-binary constraints and are difficult to formulate in terms of simple constraint solvers. We investigate the use of local search techniques for solving ETPs. In particular, we propose several versions of hill-climbing that make use of a novel search space that includes also partial assignments. We show that, on large and difficult instances of real world ETPs, where systematic search fails, local search methods perform well and solve the hardest instances. According to our experimental results on various techniques, a simple version of hill climbing based on random moves is the best method for solving large ETP instances.