A Dynamic Genetic Algorithm for Solving a Single Machine Scheduling Problem with Periodic Maintenance

Algorithm 1

Pseudocodes for proposed genetic algorithm.

Inputs: 𝑛_Var (Number of variables), 𝑛_pop (Number of initial individuals), 𝑃_𝑐 (Crossover percentage), 𝑃_𝑚 (Mutation percentage),

    Max𝐼𝑡 (Number of generations)

(1) Initialization:

(1-1) Create 𝑛_pop chromosomes calculate the fitness of each one and sort them due to their fitness

(2) Iterations: for 𝑖=1 to Max𝐼𝑡:

(2-1) Selection

(2-1-1) Select 𝑛_pop×𝑝_𝑐 individuals using roulette wheel for crossover.

(2-1-2) Select 𝑛_pop×𝑝_𝑚 individuals using roulette wheel for mutation.

(2-2) Reproductions

(2-2-1) Conduct the crossover operator on each pair of the selected parents to generate offspring

(2-2-2) conduct the mutation operator on each of the selected parents to generate an offspring.

(2-2-2-1) If the current solution is repeated less than (Max𝐼𝑡/5) conduct 𝑛_Var/3 swapping and else go to (2-2-2-2)

(2-2-2-2) If the current solution is repeated less than (Max𝐼𝑡/3) conduct 𝑛_Var/2 swapping and else go to (2-2-2-3)

(2-2-2-3) Conduct 𝑛_Var swapping.

(2-2-3) Merge mutants, Crossover offsprings and initial population; sort them according to fitness and choose the top 𝑛_pop

    members.

(2-3) If the current solution is repeated for (Max𝐼𝑡/2) go to (3) and else continue.

(3) End of genetic algorithm.