A hybrid framework for over-constrained generalized resource-constrained project scheduling problems

Authors:
A. Lim;B. Rodrigues;R. Thangarajoo;F. Xiao
Affiliations:
Department of IEEM, Hong Kong, University of Science and Technology, Clearwater Bay, Hong Kong;School of Business, Singapore Management University, Singapore;Defence Science and Technology Agency, Singapore;Department of Computer Science, National University of Singapore, Singapore
Venue:
Artificial Intelligence Review
Year:
2004

Citing 18
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Abstract

Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification ...