Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7686

@InProceedings{kovacs:2023:GECCO,

• author = "Benjamin Kovacs and Pierre Tassel and Martin Gebser",
• title = "Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "1374--1382",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, semiconductor fab, dispatching heuristics",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590402",
• size = "9 pages",
• abstract = "Optimizing operations in semiconductor manufacturing facilities is challenging. The production line can be modeled as an NP-hard constrained flexible job-shop scheduling problem, intractable with mathematical optimization due to its scale. Therefore, decision-making in factories is dominated by handcrafted heuristics. Though machine learning-based approaches proved efficient in solving such problems, their applications are limited due to the lack of trust in the underlying black-box models, and issues with scalability for larger instances. This work presents a genetic programming-based method to generate explainable, improved dispatching heuristics. Our method outputs a set of human-readable dispatching strategies, verifiable by scheduling experts before deployment. In case of minor changes in the environment or the optimization objectives, the continued evolution of the candidate solutions is possible without starting the training process from scratch. The introduced method is evaluated on a simulator executing real-world scale instances. The resulting heuristics improve the key performance indicators of the generated schedules. Furthermore, the generated dispatchers are easy to integrate into existing industrial systems. These favorable properties make the method applicable to various large-scale, dynamic, practical scheduling scenarios, where adaptions to different environments go along with modest human effort limited to the design of a fitness function.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Benjamin Kovacs Pierre Tassel Martin Gebser

Citations