Surrogate-assisted cooperative learning genetic programming for the resource-constrained project scheduling problem with stochastic activity durations and transfer times

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Zhang:2025:cor,

• author = "Haohua Zhang and Erik Demeulemeester and Lubo Li and Sijun Bai",
• title = "Surrogate-assisted cooperative learning genetic programming for the resource-constrained project scheduling problem with stochastic activity durations and transfer times",
• journal = "Computer and Operations Research",
• year = "2025",
• volume = "173",
• pages = "106816",
• keywords = "genetic algorithms, genetic programming, Stochastic project scheduling, Transfer times, Surrogate model, Cooperative learning",
• ISSN = "0305-0548",
• URL = "https://www.sciencedirect.com/science/article/pii/S0305054824002880",
• DOI = "doi:10.1016/j.cor.2024.106816",
• abstract = "For the project scheduling problem with transfer times under an uncertain environment, not only the activity durations are stochastic, but transfer times are often also stochastic. Therefore, we propose a resource-constrained project scheduling problem with stochastic activity durations and transfer times (RCPSP-SDT), which requires complex activity sequencing and resource transfer decisions with an activity priority rule (APR) and a resource transfer priority rule (RTPR) under unpredicted dynamic factors. However, manually designed combination rules of APRs and RTPRs are time-consuming and only for specific scenarios. Therefore, we develop a hyper-heuristic approach based on genetic programming (GP), which has been successfully applied to evolve activity priority rules for project scheduling problems. A new representation of GP individuals was designed to evolve the APR and the RTPR simultaneously. In order to improve the efficiency and solution quality of the approach, we propose surrogate-assisted cooperative learning genetic programming (SCLGP) based on GP. Based on the benchmark data set, computer experiments were conducted under nine variance levels of stochastic distributions. The results show that the proposed algorithm SCLGP performs significantly better than the classical priority rule (PR)-based heuristics. Furthermore, the effectiveness and efficiency of SCLGP were verified by comparing it to four other GP-based algorithms. Finally, the impact of the parameters on the algorithm was investigated, proving that these parameters affect the algorithm's performance",

}

Genetic Programming entries for Haohua Zhang Erik Demeulemeester Lubo Li Sijun Bai

Citations