An Online Genetic Programming Approach to Dynamic Production Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8464

@InProceedings{tran:2025:GECCO,

• author = "Binh Tran and Su Nguyen",
• title = "An Online Genetic Programming Approach to Dynamic Production Scheduling",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "1053--1061",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, hyper-heuristic, online machine learning",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726342",
• DOI = "doi:10.1145/3712256.3726342",
• size = "9 pages",
• abstract = "Scheduling is an important function in dynamic and complex production systems. Effective scheduling strategies help production systems use resources efficiently and improve delivery performance. Due to the production system's complexity and dynamic changes, designing such scheduling strategies is challenging. Recently, advanced machine learning and optimisation methods such as genetic programming (GP) have shown promise in designing sophisticated scheduling strategies. These methods' success relies on accurate data-driven simulation models for evaluating automatically-generated scheduling strategies. However, building a simulation model that accurately predicts complex production system behaviours requires a lot of historical operational data, which may not always be available, especially for new production systems or those adaptive to the market. To overcome this limitation, this study develops the first online GP method called OGP for dynamic production scheduling problems that allows GP to learn and optimise scheduling decisions on the fly without an exact model for fitness evaluations. The experiments with dynamic flexible job shops show that OGP outperforms existing scheduling strategies in the literature when both scheduling and routing decisions are considered. When used as an automated heuristic design method, OGP can generate competitive rules compared to the state-of-the-art GP methods in terms of test performance and rule sizes.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Binh Ngan Tran Su Nguyen

Citations