- author = "Jingyu Luo and Mario Vanhoucke and Jose Coelho and Weikang Guo",
- title = "An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem",
- booktitle = "31st European Conference On Operational Research",
- year = "2021",
- address = "Athens",
- month = "11-14 " # jul,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://kar.kent.ac.uk/90397/1/EURO21-Conference_e-Handbook_Full_version.pdf",
- 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 hyperheuristic 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 conventional GP approach.Furthermore, the impact of the training data selection and fitness evaluation has 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 1000 activities. 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",
-
notes = "See \cite{Luo:2022:ESA}
https://euro2021athens.com/",
