An Improved Immune Genetic Algorithm for Solving the Flexible Job Shop Scheduling Problem with Batch Processing

Algorithm 1

IIGA framework.

1: Randomly generate initial populations and ; the population size is ; set .

2: Combine the parent and offspring populations, namely, .

3: Obtain through the greedy thought and then calculate the fitness value of the 2N individuals in and the cross-entropy value and similarity with respectively.

4: Arrange and according to their values and take the largest of the first phases as and .

5: Let

6: Define ; update population

7: Perform a crossover operation on the population.

8: Perform a mutation operation on the population to generate a new population .

9: If , then ; return to Step 2; otherwise, the algorithm terminates.