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ETD Abstract Container

Abstract Header

Combining genetic algorithms and artificial neural networks to select heterogeneous dispatching rules for a job shop system

Wilson, Daniel B.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177701025

Abstract Details

1996, Master of Science (MS), Ohio University, Industrial and Manufacturing Systems Engineering (Engineering).
Combining genetic algorithms and artificial neural networks to select heterogeneous dispatching rules for a job shop system
Luis Rabelo (Advisor)
82 p.
Engineering, Industrial
Artificial Neural Networks; Heterogeneous Dispatching Rules; Genetic Algorithms

Recommended Citations

Citations

  • Wilson, D. B. (1996). Combining genetic algorithms and artificial neural networks to select heterogeneous dispatching rules for a job shop system [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177701025

    APA Style (7th edition)

  • Wilson, Daniel. Combining genetic algorithms and artificial neural networks to select heterogeneous dispatching rules for a job shop system. 1996. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177701025.

    MLA Style (8th edition)

  • Wilson, Daniel. "Combining genetic algorithms and artificial neural networks to select heterogeneous dispatching rules for a job shop system." Master's thesis, Ohio University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177701025

    Chicago Manual of Style (17th edition)

ohiou1177701025
789
© 1996, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.