A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Scheduling projects with labor constraints
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
Cooperative Parallel Tabu Search for Capacitated Network Design
Journal of Heuristics
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
The State of the Art of Nurse Rostering
Journal of Scheduling
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
Artificial agents learning human fairness
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
Memes, self-generation and nurse rostering
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Solving the really hard problems with cooperative search
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A cooperative hyper-heuristic search framework
Journal of Heuristics
Memetic algorithms for nurse rostering
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
| Hi-index | 12.05 |
The development of decision support systems acceptable for nurse rostering practitioners still presents a daunting challenge. Building on an existing nurse rostering problem, a set of fairness-based objective functions recently introduced in the literature has been extended. To this end, a generic agent-based cooperative search framework utilising new mechanisms is described, aiming to combine the strengths of multiple metaheuristics. These different metaheuristics represent individual planners' implicit procedures for improving rosters. The framework enables to explore different ways of assessing nurse rosters in terms of fairness objectives. Computational experiments have been conducted across a set of benchmark instances. The overall results indicate that the proposed cooperative search for fair nurse rosters outperforms each metaheuristic run individually.