Wednesday, July 8, 2009

IMPLEMENTATION OF GA TO JSSP

From the earlier said operational parameters, we took the real-value encoding in which the job numbers are encoded to form the string which will give the sequence of machining order. i.e., each chromosome represents a legal solution to the problem and is composed of a string of genes. In our application, each solution is encoded as a string of job sequence. For example, the string “235641” represents for a job sequence of job 2 is processed before job “3”, job “3” is processed before job “5” and so on. The requirement is that repeating a job in a sequence is not allowed and all jobs should appear in the sequence.

For the crossover we preferred the one point method since it is simple and easy to implement and it is robust. In case of mutation the swap mutation is used, which play the major role to avoid the convergence. Termination condition used is Fixed Generation Termination. In general the roulette wheel selection is used for the maximize problem so here we used the rank selection with the fitness function as said above.

Configuration of Operational Parameters with which application runs 

Population Size

Number of chromosomes in the population is 30

Crossover

Probability of crossover’s tossing head is 0.6

Mutation

Probability of Mutation’s tossing head 0.2

Fitness

Subjective Fitness of Makes-span

Selection

Linear Ranking selection

Termination Condition

After 100 Generations

References:

Book Reference: David Edward Goldberg (1989), “Genetic Algorithms in Search, Optimization and Machine Learning” Addison-Wesley.

Citation Needed: Scheduling Problem For A Single Flexible Manufacturing machine, Nguyen Van Hop, Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University

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