A Novel Intelligent Method for Task Scheduling in Industrial Cluster
In this paper the author proposes a new genetic algorithm (NGA) for a scheduling problem in a local supply network (in other words industrial cluster - IC). The new genetic algorithm enables not only a manufacturing scheduling in IC. Additionally, NGA aids planners in transport orders planning. New genetic algorithm employs two steps to encode the scheduling problem in IC. In the first step, each chromosome type A represents a potential optimal solution of a problem being optimized. Chromosome type A is a set of 4-positions genes. The value of the first position represents the job, the value of the second position the operation number, the next value the resource number or the order transport, and the last value the factory number or the source of the transport order. The second step is to copy the first and the second position from the gene of the chromosome A into the gene of the chromosome B, and to translate the last two positions from the gene of the chromosome A into one position of the gene of the chromosome B. The cases study shows that proposed by the author new genetic algorithm is effective in solving the scheduling problems in local supply networks. (original abstract)
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