A Multi-Objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Prince:2021:evoapplications,

• author = "Michael H. Prince and Kristian K. DeHaan and Daniel R. Tauritz",
• title = "A Multi-Objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems",
• booktitle = "24th International Conference, EvoApplications 2021",
• year = "2021",
• month = "7-9 " # apr,
• editor = "Pedro Castillo and Juanlu Jimenez-Laredo",
• series = "LNCS",
• volume = "12694",
• publisher = "Springer Verlag",
• address = "virtual event",
• pages = "97--112",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Assembly Job Shop Scheduling, Evolutionary algorithm, Manufacturing, Multi-objective evolutionary algorithm",
• isbn13 = "978-3-030-72698-0",
• URL = "https://www.osti.gov/servlets/purl/1763897",
• DOI = "doi:10.1007/978-3-030-72699-7_7",
• size = "16 pages",
• abstract = "Motivated by a real-world application, we consider an Assembly Job Shop Scheduling Problem (AJSSP), with three objectives: product quality, product quantity, and first product lead time. Using real-world inspection data, we demonstrate the ability to model product quality transformations during assembly jobs via genetic programming by considering the quality attributes of subparts. We investigate integrating quality transformation models into an AJSSP. Through the use of the de facto standard multi-objective evolutionary algorithm, NSGA-II, and a novel genotype to handle the constraints, we describe an evolutionary approach to optimizing all stated objectives. This approach is empirically shown to outperform random search and hill climbing in both performance and usability metrics expected to be valuable to administrators involved in plant scheduling and operations.",
• notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021",

}

Genetic Programming entries for Michael H Prince Kristian K DeHaan Daniel R Tauritz

Citations