A New Priority Rule for Solving Project Scheduling Problems

Author:

Adamu Patience I.ORCID,Okagbue Hilary I.,Oguntunde Pelumi E.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Reference17 articles.

1. Adamu, P. I. & Aromolaran O. T. (2018). Machine learning priority rule (MLPR) for solving resource-constrained project scheduling problems. In Lecture Notes in engineering and computer science: Proceedings of the international multi-conference of engineers and computer scientists 2018, Hong Kong (pp. 974-979) 14–16 March 2018.

2. Adamu, P. I., Agarana, M. C., & Okagbue, H. I. (2018). Machine learning heuristic for solving multi-mode resource-constrained project scheduling problems. In Lecture notes in engineering and computer science: Proceedings of the international multiconference of engineers and computer scientists 2018, Hong Kong (pp. 23–28) 14–16 March 2018.

3. Adamu, P. I. (2009). Hard resource-constrained project scheduling problems. International Journal of Numerical Mathematics, 4, 154–169.

4. Blazewicz, J., Lenstra, J. K., & Rinnooy Kan, A. H. G. (1983). Scheduling subject to resource constraints: Classification and complexity. Discrete Applied Mathematics, 5, 11–24.

5. Chand, S., Huynh, Q., Singh, H., Ray, T., & Wagner, M. (2018). On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems. Information Sciences, 432, 146–163.

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