Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Job shop scheduling by simulated annealing
Operations Research
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Heuristic Combination Method for Solving Job-Shop Scheduling Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Computers and Operations Research
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
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Genetic Algorithms (GAs) are popular approaches in solving various complex real-world problems. However, it is required that a careful attention is to be paid to the contextual knowledge as well as the implementation of genetic material and operators. On the other hand, the job-shop scheduling (JSS) problem remains as challenging NP-hard combinatorial problem, which attracts researchers since it is invented. The dynamic version of job-shop is even more challenging due to its dynamically changing characteristics. Similar to other metaheuristic approaches, GA has not been so successful in solving this sort of problems due to instant decision making process needed in solving this type of problems. Heuristic procedures such as those so called Priority Rule or Dispatching Rules are more useful for this purpose, but, depending on the properties and purpose of use of each, the same performance is not expected from these instant decision making operators. In this paper, a policy refinement approach is proposed to optimise a sequence of Dispatching Rules (DRs) for a time-window of scheduling process in which a GA algorithm evolves the sequences towards an optimum configuration. The preliminary results provided in this paper seem very encouraging.