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

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    Publication type
    Vlerick strategic journal article
    Author
    Luo, Jingyu
    Vanhoucke, Mario
    Coelho, José
    Guo, Weikang
    Publication Year
    2022
    Journal
    Expert Systems with Applications
    Publication Volume
    198
    Publication Issue
    July
    
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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.
    Keyword
    Resource-constrained Project Scheduling, Priority Rules, Genetic Programming
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.eswa.2022.116753
    URI
    http://hdl.handle.net/20.500.12127/7049
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.eswa.2022.116753
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