Toward Evolving Dispatching Rules for Dynamic Job Shop Scheduling Under Uncertainty

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Karunakaran:2017:GECCO,

• author = "Deepak Karunakaran and Yi Mei and Gang Chen2 and Mengjie Zhang",
• title = "Toward Evolving Dispatching Rules for Dynamic Job Shop Scheduling Under Uncertainty",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4920-8",
• address = "Berlin, Germany",
• pages = "282--289",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3071178.3071202",
• DOI = "doi:10.1145/3071178.3071202",
• acmid = "3071202",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, job shop scheduling, uncertainty",
• month = "15-19 " # jul,
• abstract = "Dynamic job shop scheduling (DJSS) is a complex problem which is an important aspect of manufacturing systems. Even though the manufacturing environment is uncertain, most of the existing research works consider deterministic scheduling problems where the time required for processing any job is known in advance and never changes. In this work, we consider DJSS problems with varied uncertainty configurations of machines in terms of processing times and the total flow time as scheduling objective. With the varying levels of uncertainty many machines become bottlenecks of the job shop. It is essential to identify these bottleneck machines and schedule the jobs to be performed by them carefully. Driven by this idea, we develop a new effective method to evolve pairs of dispatching rules each for a different bottleneck level of the machines. A clustering approach to classifying the bottleneck level of the machines arising in the system due to uncertain processing times is proposed. Then, a cooperative co-evolution technique to evolve pairs of dispatching rules which generalize well across different uncertainty configurations is presented. We perform empirical analysis to show its generalization characteristic over the different uncertainty configurations and show that the proposed method outperforms the current approaches.",
• notes = "Also known as \cite{Karunakaran:2017:TED:3071178.3071202} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Deepak Karunakaran Yi Mei Aaron Chen Mengjie Zhang

Citations