How our team approached to Job Shop Scheduling using Genetic Algorithm

As said in previous post below is our approach for the Job Shop Scheduling using Genetic Algorithm

Project Team: Kumaravel.S, Mohammed Ishaq.I, Sankaralingam.B, Venkatesh.G

As first to generate the chromosomes, we generate the Random number from
1 to Number of jobs and that number is used to form to each chromosomes. For an instance to generate the chromosome of length 5, the following is the steps,

As similar we generate the 30 number of chromosomes and taken it as initial population. Then the elapse time (actual fitness) for each chromosome is calculated and subsequently they are ranked in such a way that the worst string will take the first position where as the best holds the last position. Next the subjective fitness for each of them was calculated and the expected count of each chromosome was computed with the formula below.
Then expected count is rounded to get the actual count. These steps were illustrated below.
After the computation of the actual count the chromosomes were copied actual count times to next generation. Now the selected strings are crossover and mutation operation were carried out as below.
Then now the selected strings and crossover offspring are copied to the next generation (where the mutated string replaces the mutated offspring) as the population is constant value of 30. The remaining chromosomes are generated randomly as step 1. Now the Generation 1 process started and the subsequent generations are proceed until the termination value 100.
At the end the optimal solutions of the final generation will segregated and outputted as optimal solutions.
Related Posts:

• ABSTRACT
• INTRODUCTION
• Genetic Algorithm
• Biological Background
• Parameters
• Implementation

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© 2006 Kumaravel & Project Team