Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
“Teachers and classes” with neural networks
International Journal of Neural Systems
Neural network methods in combinatorial optimization
Computers and Operations Research - Special issue on neural networks and operations research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Applied Numerical Methods for Digital Computation with FORTRAN and CSMP
Applied Numerical Methods for Digital Computation with FORTRAN and CSMP
A Survey of Automated Timetabling
Artificial Intelligence Review
Automated Solution of a Highly Constrained School Timetabling Problem - Preliminary Results
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Genetic Algorithms and Highly Constrained Problems: The Time-Table Case
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Recent Developments in Practical Course Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Specialised Recombinative Operators for Timetabling Problems
Selected Papers from AISB Workshop on Evolutionary Computing
A genetic algorithm approach to multiobjective land use planning
Computers and Operations Research
A Computational Study of a Cutting Plane Algorithm for University Course Timetabling
Journal of Scheduling
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A case study of mutual routing-scheduling reformulation
Journal of Scheduling
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Applying evolutionary computation to the school timetabling problem: The Greek case
Computers and Operations Research
New integer linear programming approaches for course timetabling
Computers and Operations Research
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multi-objective evolutionary algorithms for resource allocation problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A perspective on bridging the gap between theory and practice in university timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Tabu search techniques for large high-school timetabling problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
A real-integer-discrete-coded differential evolution
Applied Soft Computing
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The complexity of a resource allocation problem (RAP) is usually NP-complete, which makes an exact method inadequate to handle RAPs, and encourages heuristic techniques to this class of problems for obtaining approximate solutions in polynomial time. Different heuristic techniques have already been investigated for handling various RAPs. However, since the properties of an RAP can help in characterizing other RAPs, instead of individual solution techniques, the similarities of different RAPs might be exploited for developing a common solution technique for them. Two RAPs of quite different nature, namely university class timetabling and land-use management, are considered here for such a study. The similarities between the problems are first explored, and then a common multi-objective evolutionary algorithm (a kind of heuristic techniques) for them is developed by exploiting those similarities. The algorithm is problem-dependent to some extent and can easily be extended to other similar RAPs. In the present work, the algorithm is applied to two real instances of the considered problems, and its properties are derived from the obtained results.