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• W3133490532 endingPage "8616" @default.
• W3133490532 startingPage "8603" @default.
• W3133490532 abstract "Resource constraint job scheduling is an important combinatorial optimization problem with many practical applications. This problem aims at determining a schedule for executing jobs on machines satisfying several constraints (e.g., precedence and resource constraints) given a shared central resource while minimizing the tardiness of the jobs. Due to the complexity of the problem, several exact, heuristic, and hybrid methods have been attempted. Despite their success, scalability is still a major issue of the existing methods. In this study, we develop a new genetic programming algorithm for resource constraint job scheduling to overcome or alleviate the scalability issue. The goal of the proposed algorithm is to evolve effective and efficient multipass heuristics by a surrogate-assisted learning mechanism and self-competitive genetic operations. The experiments show that the evolved multipass heuristics are very effective when tested with a large dataset. Moreover, the algorithm scales very well as excellent solutions are found for even the largest problem instances, outperforming existing metaheuristic and hybrid methods." @default.
• W3133490532 created "2021-03-15" @default.
• W3133490532 creator A5008490552 @default.
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• W3133490532 date "2022-09-01" @default.
• W3133490532 modified "2023-09-24" @default.
• W3133490532 title "Automated Design of Multipass Heuristics for Resource-Constrained Job Scheduling With Self-Competitive Genetic Programming" @default.
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• W3133490532 doi "https://doi.org/10.1109/tcyb.2021.3062799" @default.
• W3133490532 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33710971" @default.
• W3133490532 hasPublicationYear "2022" @default.
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