Paper Title:
Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems
  Abstract

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

  Info
Periodical
Key Engineering Materials (Volumes 419-420)
Edited by
Daizhong Su, Qingbin Zhang and Shifan Zhu
Pages
633-636
DOI
10.4028/www.scientific.net/KEM.419-420.633
Citation
J. C. Chen, W. H. Jaong, C. J. Sun, H. Y. Lee, J. S. Wu, C. C. Ku, "Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems", Key Engineering Materials, Vols. 419-420, pp. 633-636, 2010
Online since
October 2009
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Price
$32.00
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