Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A multiheuristic approach to resource constrained project scheduling: an adaptive hybrid genetic algorithm
An evolutionary algorithm for resource-constrained projectscheduling
IEEE Transactions on Evolutionary Computation
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This research paper presents a new permutation-based Elitist genetic algorithm using serial schedule generation scheme for solving a large-sized multiple resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. A key aspect of the algorithm was the application of the elitist roulette selection operator to preserve the best individual solution for the next generation so as to obtain the improved solution. Serial schedule generation scheme was applied to generate a feasible solution to the problem. Results for large-sized project network problems were presented to demonstrate the performance and accuracy of the algorithm. The computational results indicate that the proposed algorithm not only produces reasonably good solutions for the resource scheduling problem over the heuristic method and other GA, but also able to solve large-sized multiple resource-constrained project scheduling problems applicable to the construction industry.