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An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem

Jingyu Luo (UGent) , Mario Vanhoucke (UGent) , José Fernandes da Silva Coelho (UGent) and Weikang Guo (UGent)
(2022) EXPERT SYSTEMS WITH APPLICATIONS. 198.
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Abstract
In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.
Keywords
Resource-constrained project scheduling, Priority rules, Genetic programming, HEURISTIC PERFORMANCE, CLASSIFICATION

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MLA
Luo, Jingyu, et al. “An Efficient Genetic Programming Approach to Design Priority Rules for Resource-Constrained Project Scheduling Problem.” EXPERT SYSTEMS WITH APPLICATIONS, vol. 198, 2022, doi:10.1016/j.eswa.2022.116753.
APA
Luo, J., Vanhoucke, M., Fernandes da Silva Coelho, J., & Guo, W. (2022). An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem. EXPERT SYSTEMS WITH APPLICATIONS, 198. https://doi.org/10.1016/j.eswa.2022.116753
Chicago author-date
Luo, Jingyu, Mario Vanhoucke, José Fernandes da Silva Coelho, and Weikang Guo. 2022. “An Efficient Genetic Programming Approach to Design Priority Rules for Resource-Constrained Project Scheduling Problem.” EXPERT SYSTEMS WITH APPLICATIONS 198. https://doi.org/10.1016/j.eswa.2022.116753.
Chicago author-date (all authors)
Luo, Jingyu, Mario Vanhoucke, José Fernandes da Silva Coelho, and Weikang Guo. 2022. “An Efficient Genetic Programming Approach to Design Priority Rules for Resource-Constrained Project Scheduling Problem.” EXPERT SYSTEMS WITH APPLICATIONS 198. doi:10.1016/j.eswa.2022.116753.
Vancouver
1.
Luo J, Vanhoucke M, Fernandes da Silva Coelho J, Guo W. An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem. EXPERT SYSTEMS WITH APPLICATIONS. 2022;198.
IEEE
[1]
J. Luo, M. Vanhoucke, J. Fernandes da Silva Coelho, and W. Guo, “An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem,” EXPERT SYSTEMS WITH APPLICATIONS, vol. 198, 2022.
@article{8744796,
  abstract     = {{In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.}},
  articleno    = {{116753}},
  author       = {{Luo, Jingyu and Vanhoucke, Mario and Fernandes da Silva Coelho, José and Guo, Weikang}},
  issn         = {{0957-4174}},
  journal      = {{EXPERT SYSTEMS WITH APPLICATIONS}},
  keywords     = {{Resource-constrained project scheduling,Priority rules,Genetic programming,HEURISTIC PERFORMANCE,CLASSIFICATION}},
  language     = {{eng}},
  pages        = {{20}},
  title        = {{An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem}},
  url          = {{http://doi.org/10.1016/j.eswa.2022.116753}},
  volume       = {{198}},
  year         = {{2022}},
}
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