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
Solving a timetabling problem using hybrid genetic 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
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
Solving effectively the school timetabling problem using particle swarm optimization
Expert Systems with Applications: An International Journal
A hybrid particle swarm optimization based algorithm for high school timetabling problems
Applied Soft Computing
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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.We investigate the use of several local search techniques for solving ETPs. In particular, we propose a generalization of local search that makes use of a novel search space that includes also partial assignments. We describe the distinguishing features of this generalized local search that allows it to navigate the search space effectively. 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 local search techniques, generalized local search is the best method for solving large ETP instances.