@inproceedings{eb3543a1040d449494f838f825a19e03,
title = "Order-based genetic algorithm for flow shop scheduling",
abstract = "Flow shop scheduling is one of the most well-known production scheduling problems and a typical NP-hard combinatorial optimization problem with strong engineering background. This paper presents an order-based genetic algorithm for flow shop scheduling, which borrows the idea of ordinal optimization to reduce computation and ensure the quality of the solution found and enforces the evolutionary searching mechanism and learning capability of genetic algorithm. With the guidance of ordinal comparison and by emphasizing the order-based search and elitist-based evolution in the proposed approach, good enough solution can be guaranteed with high confidence level and reduced computation quantity, which is demonstrated by numerical simulation based on some benchmarks. Moreover, some parameter sensitivities are presented and discussed.",
keywords = "Flow shop scheduling, Genetic algorithm, Ordinal optimization, Parameter sensitivity",
author = "Liang Zhang and Ling Wang and Fang Tang",
year = "2002",
language = "英语",
isbn = "0780375084",
series = "Proceedings of 2002 International Conference on Machine Learning and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "139--144",
booktitle = "2002 International Conference on Machine Learning and Cybernetics, ICMLC 2002",
address = "美国",
note = "1st International Conference on Machine Learning and Cybernetics, ICMLC 2002 ; Conference date: 04-11-2002 Through 05-11-2002",
}