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Publication
Automated design of priority rules for resource-constrained project scheduling problem using surrogate-assisted genetic programming
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| 1.4 MB | Adobe PDF |
Advisor(s)
Abstract(s)
In the past few years, the genetic programming approach (GP) has been successfully used by researchers to design priority rules for the resource-constrained project scheduling problem (RCPSP) thanks to its high
generalization ability and superior performance. However, one of the main drawbacks of the GP is that the
fitness evaluation in the training process often requires a very high computational effort. In order to reduce the runtime of the training process, this research proposed four different surrogate models for the RCPSP. The
experiment results have verified the effectiveness and the performance of the proposed surrogate models. It is
shown that they achieve similar performance as the original model with the same number of evaluations and
better performance with the same runtime. We have also tested the performance of one of our surrogate
models with seven different population sizes to show that the selected surrogate model achieves similar
performance for each population size as the original model, even when the searching space is sufficiently
explored. Furthermore, we have investigated the accuracy of our proposed surrogate models and the size of
the rules they designed. The result reveals that all the proposed surrogate models have high accuracy, and
sometimes the rules found by them have a smaller size compared with the original model.
Description
Keywords
Resource-constrained project scheduling Priority rules Genetic programming Surrogate models
