## Proceedings: 1996 IEEE Conference on Emerging Technologies and Factory Automation, ETFA '96, Kauai, Hawaii, November 18-21, 1996 |

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Page 24

Just as for the

the form 1 = ( rumpos Apo ) , where R ' E N ' is an optimal set for Problem A , i . e .

, a set of jobs such that i ) all jobs of the set N ' R ' , which are sequenced in ...

Just as for the

**initial**problem , for this problem there exists an optimal schedule ofthe form 1 = ( rumpos Apo ) , where R ' E N ' is an optimal set for Problem A , i . e .

, a set of jobs such that i ) all jobs of the set N ' R ' , which are sequenced in ...

Page 25

Let us now consider the reduced problem ( Problem B ) , which is obtained from

the

and changing the release and due dates of the set N " = N1Q of remaining jobs ...

Let us now consider the reduced problem ( Problem B ) , which is obtained from

the

**initial**problem 11 pmin ; rit , d ; T , C ; sdi , i eQIw ; U ; by removing the set Qand changing the release and due dates of the set N " = N1Q of remaining jobs ...

Page 285

The convergence time is depend on the

connection weights , we try to use the genetic algorithm ( GA ) to find rough

values for them . The

using the ...

The convergence time is depend on the

**initial**values of the PID gains andconnection weights , we try to use the genetic algorithm ( GA ) to find rough

values for them . The

**initial**values of connection weights are also de termined byusing the ...

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### Contents

PLENARY SESSION I | 1 |

PLENARY SESSION II | 7 |

SESSION TU4AScheduling | 16 |

Copyright | |

44 other sections not shown

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### Common terms and phrases

action activities agent algorithm allows analysis application approach assignment associated Automation behavior called communication complete components Computer connection consider constraints cost deadlines defined depends described determined developed distributed dynamic environment error estimation evaluation event example execution field Figure force frequency function given holons human IEEE implementation initial input intelligent learning machine manipulator manufacturing means measure messages method Mimetism motion nodes object obtained operation optimal output parameters performance period Petri phase planning plant position possible presented priority problem proposed real-time reference represented request resource robot rules scheduling scheme selection sensor sequence server shown shows signal simple simulation solution solve space specific stage station step structure Table task technique tion transition vector