Abstract
Resource-constrained project scheduling allocates scarce resources over time to perform a set of activities. Priority rule-based heuristics are the most widely used scheduling methods though their performance depends on the characteristics of the projects. To overcome this deficiency, a feed-forward neural network is designed and integrated into the scheduling scheme so as to automatically select the suitable priority rules for each stage of project scheduling. Testing on Patterson’s classic test problems and comparison with other heuristics show that the proposed neural network based heuristic is able to improve the performance of project scheduling.
Project 70401017 is supported by National Natural Science Foundation of China.
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Shou, Y. (2005). A Neural Network Based Heuristic for Resource-Constrained Project Scheduling. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_127
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DOI: https://doi.org/10.1007/11427391_127
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