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Intelligent Engineering Systems through Artificial Neural Networks

Editor
Cihan H. Dagli
Cihan H. Dagli
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K. Mark Bryden
K. Mark Bryden
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Steven M. Corns
Steven M. Corns
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Mitsuo Gen
Mitsuo Gen
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Kagan Tumer
Kagan Tumer
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Gürsel Süer
Gürsel Süer
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ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009

In recent years, rescheduling attracted considerable attention, motivated by both important practical issues and interesting research problems. In this paper we discuss a multi-objective rescheduling problem. A set of original jobs has been scheduled to minimize a given cost objective, and then several sets of new jobs arrive unexpectedly. The objective in the original schedule still needs to be minimized over all of the jobs. However, this will change the original schedule, reducing customer satisfaction and creating havoc with the original resource allocations. Thus, the tradeoff between the scheduling cost and the disruption cost must be considered in detail. We also propose a multi-objective evolutionary approach for solving this rescheduling problem. We use new representation method in the proposed algorithm. Advanced genetic operators adapted to the specific chromosome structure and the characteristics of the rescheduling problem are developed. Some practical test instances will demonstrate the effectiveness and efficiency of the proposed algorithm.

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