A framework for integration model of resource-constrained scheduling using genetic algorithms

The objective of this paper is to present an optimal algorithm for a resource allocation model, which would be implemented into a framework for the development of an integration model. Unlike present heuristic-based resource allocation models, the model does not depend solely on a set of heuristic rules, but adopts the concept of future float to set the order of priority when activities compete for resources. The model determines the shortest duration by allocating available resources to a set of activities simultaneously. Genetic algorithms (GAs) are adopted to search optimal solutions. The results obtained from a case example indicate that the model is capable of producing optimal scheduling alternatives, compared to a single solution that is produced by either the total float model or the least impact model

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