A New Genetic Algorithm for Resource Constrained Project Scheduling

Y. Mohsenin and H.H. Ali (USA)


Scheduling, Genetic Algorithms, Resource Allocation


The Project Scheduling Problem has attracted the attention of researchers for years. Much of this research has been conducted in the Resource Constrained Project Scheduling Problem (RCPSP). The goal of this paper is to define new Genetic Algorithms Operators for a new a model of Resource Constrained Project Scheduling Problem with heterogeneous resources (operators). The model better resembles real-world projects and has more flexibility than previous models for manpower scheduling. Most RCPSP research uses Finish Time as a measure of the fitness of a solution. In this model the combination of Quality and Finish Time will be used. The goal will be to maximize the Quality and minimize the Finish Time. To solve the problem, the model will use Genetic Algorithms. New Crossover and Mutation operators will be implemented. These operators will attempt to create children with the same or higher Quality than their parents and will add the children the solution pool.

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