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Pattern-recognition scheduling

Yao, Xiaoqiang
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177698616

Abstract Details

1996, Master of Science (MS), Ohio University, Industrial and Manufacturing Systems Engineering (Engineering).
The interest in the use of artificial neural networks (ANNs) to solve engineering optimization problems have been growing at a substantial pace in recent years. This mainly owes to ANNs' ability to mimic human intelligence and hence making ANNs a more robust technique in the decision making in a dynamic environment. The emphasis in this study is try to find an approach that is intelligent and flexible enough to handle the real- time scheduling requirements in a dynamic manufacturing environment, with shorter response time. An artificial neural network based pattern-recognition approach for real- time scheduling of production system is studied and a scheduling system which integrates artificial neural networks, dispatching rules, real-time simulation and genetic algorithms has been developed. In this system, artificial neural networks, with their ability of learning and generalization, are used to make a predictive selection of a small set of candidate scheduling policies from a larger set of heuristics dynamically at a decision point without searching through the solution space exhaustively. Genetic algorithms are then applied to take this selected set of rules as part of the "seed" rules to generate a single final "best" schedule, this schedule may be totally different from any of the root schedules. The approach has been applied, with some variance, in two cases: (1) single-machine scheduling problem with sequence dependent setup times; (2) a multiple-machine scheduling problem. The simulation results in both cases for different performance measures demonstrated that the neural network based integrated system performed better than any dispatching rule alone. Artificial neural networks, when appropriately built, do possess promising potentials in solving real-time production scheduling problems at an intelligent level, which traditional scheduling theories and techniques have not been able to provide.
Luis Rabelo (Advisor)
157 p.
Engineering, Industrial
Artificial Neural Networks; Genetic Algorithms; pattern-Recognition

Recommended Citations

Citations

  • Yao, X. (1996). Pattern-recognition scheduling [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177698616

    APA Style (7th edition)

  • Yao, Xiaoqiang. Pattern-recognition scheduling. 1996. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177698616.

    MLA Style (8th edition)

  • Yao, Xiaoqiang. "Pattern-recognition scheduling." Master's thesis, Ohio University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177698616

    Chicago Manual of Style (17th edition)

ohiou1177698616
1,003
© 1996, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.