Learning the Distribution Characteristics of Critical Machines in Production Scheduling Problems

Abstract:

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A critical machine identification algorithm is proposed for the job shop scheduling problem in which the total tardiness must be minimized. An optimization-based procedure is devised to learn the distribution characteristics of critical machines in a specific scheduling instance. The proposed simulated annealing algorithm optimizes the scheduling problem after the capacity constraints for each machine are modified. A genetic algorithm based on combined dispatching rules is designed to verify the effectiveness of the proposed methodology.

Info:

Periodical:

Edited by:

Prasad Yarlagadda, Yun-Hae Kim, Zhijiu Ai and Xiaodong Zhang

Pages:

142-145

DOI:

10.4028/www.scientific.net/AMR.337.142

Citation:

R. Zhang "Learning the Distribution Characteristics of Critical Machines in Production Scheduling Problems", Advanced Materials Research, Vol. 337, pp. 142-145, 2011

Online since:

September 2011

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$35.00

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