Title of article :
A prediction based iterative decomposition algorithm for scheduling large-scale job shops
Author/Authors :
Liu، نويسنده , , Min and Hao، نويسنده , , Jing-Hua and Wu، نويسنده , , Cheng، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Pages :
11
From page :
411
To page :
421
Abstract :
In this paper, we present a prediction based iterative decomposition algorithm for solving large-scale job shop scheduling problems using the rolling horizon scheme and the prediction mechanism, in which the original large-scale scheduling problem is iteratively decomposed into several sub-problems. In the proposed algorithm, based on the job-clustering method, we construct the Global Scheduling Characteristics Prediction Model (GSCPM) to obtain the scheduling characteristics values, including the information of the bottleneck jobs and the predicted value of the global scheduling objective. Then, we adopt the above scheduling characteristics values to guide and coordinate the process of the problem decomposition and the sub-problem solving. Furthermore, we propose an adaptive genetic algorithm to solve each sub-problem. Numerical computational results show that the proposed algorithm is effective for large-scale scheduling problems.
Keywords :
Large-scale , job shop , Scheduling , Prediction , decomposition
Journal title :
Mathematical and Computer Modelling
Serial Year :
2008
Journal title :
Mathematical and Computer Modelling
Record number :
1595415
Link To Document :
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