Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
An optimization approach for the job shop scheduling problem
MATH'09 Proceedings of the 14th WSEAS International Conference on Applied mathematics
Complex scheduling problems using an optimization methodology
WSEAS Transactions on Information Science and Applications
A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.