def main():
import genetic
import sys
# To make command line print complete nd arrays.
np.set_printoptions(threshold=sys.maxsize)
# Run for entire 120 problem range
for problem in range(0,120):
jobs = jobs_list[problem]
machines = machines_list[problem]
timeseed = timeseed_list[problem]
# Generate processing time matrix
a = random_matrix(machines, jobs, timeseed)
# Transpose matrix to calulate makespan
at = a.transpose()
# Start with all possible permutations of first 4 jobs
import itertools
init_jobs = 4
init_job_list = list(range(init_jobs))
# Generate all 24 permutations
sequence_list = np.array(list(itertools.permutations(init_job_list)))
# This loop will run till init_jobs == jobs
while init_jobs <= jobs:
makespan_list = np.array([])
for seq in sequence_list:
seq = list(seq)
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], seq))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Roulette wheel .. 3 x 20 output
sequence_list, makespan_list = roulette_wheel(sequence_list, makespan_list)
# Again, sort and reduce to 20
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
#print("Best 20\n", sequence_list, makespan_list)
# Now ordered crossover each of the 20 sequences with each other and add to lists
for i in range(20):
for j in range(20):
if i != j:
child = genetic.ordered_crossover(sequence_list[i], sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Applying mutation. Inverse mutation.
for i in sequence_list:
mutated = genetic.inverse_mutation(i)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Applying another mutation. Pairwise Swap Mutation.
for i in sequence_list:
mutated = genetic.pairwise_swap_mutation(i)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Ordered Crossover again
for i in range(20):
for j in range(20):
if i != j:
child = genetic.ordered_crossover(sequence_list[i], sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Set first element of sorted list as best makespan.
best_sequence = sequence_list[0]
best_makespan = makespan_list[0]
# Bring in next job into every position in sequence_list
init_jobs += 1
init_job_list = list(range(init_jobs))
new_sequence_list = np.array([]).reshape(0, init_jobs)
for s in sequence_list:
for pos in range(s.size + 1):
new_sequence_list = np.vstack((new_sequence_list, np.insert(s, pos, init_jobs - 1)))
sequence_list = new_sequence_list
best_sequence = list(map(int, best_sequence))
best_sequence = list(map(str, best_sequence))
best_sequence = ', '.join(best_sequence)
if best_makespan < makespans[problem]:
taillard.cell(row = problem + 3, column = 6).value = best_makespan
taillard.cell(row = problem + 3, column = 7).value = best_sequence
taillard_file.save('Makespans.xlsx')
print("Jobs: ", jobs, "\nMachines: ", machines, "\nTimeseed: ", timeseed, "\nOptimal sequence: ", best_sequence, "\nOptimal makespan: ", best_makespan, "\n")
def solve_benchmark_problem(problem,
jobs,
machines,
population,
nbgeneration,
probabilite,
show_matrix=True):
# read matrix size
a = read_matrix(problem, machines, jobs)
at = a.transpose()
# Start with all possible permutations of first 4 jobs
init_jobs = 4
init_job_list = list(range(init_jobs))
# Generate all 24 permutations
sequence_list = np.array(list(permutations(init_job_list)))
geneation = 0
while init_jobs <= jobs and geneation < nbgeneration:
makespan_list = np.array([])
for seq in sequence_list:
seq = list(seq)
makespan_list = np.append(
makespan_list, calculate_makespan(at[init_job_list], seq))
# Sort both arrays in ascending order of makespan
# and reduce to population size best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
# Roulette wheel .. 3 x population size output
sequence_list, makespan_list = genetic.roulette_wheel(
sequence_list, makespan_list)
# Again, sort and reduce to population size
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
# Now ordered crossover each of the population size sequences with each other
# and add to lists
for i in range(nbgeneration):
for j in range(nbgeneration):
if i != j:
child = genetic.ordered_crossover(sequence_list[i],
sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(
makespan_list,
calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan
# and reduce to population size best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
# Applying mutation. Inverse Mutation.
for i in sequence_list:
mutated = genetic.inverse_mutation(i, probabilite)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(
makespan_list,
calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan
# and reduce to population size best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
# Applying another mutation. Pairwise Swap Mutation.
for i in sequence_list:
mutated = genetic.pairwise_swap_mutation(i, probabilite)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(
makespan_list,
calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan
# and reduce to population best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
#Order cross over again
for i in range(population):
for j in range(population):
if i != j:
child = genetic.ordered_crossover(sequence_list[i],
sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(
makespan_list,
calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan
# and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list,
makespan_list,
population)
# Set first element of sorted list as best makespan.
best_sequence = sequence_list[0]
best_makespan = makespan_list[0]
# Bring in next job into every position in sequence_list
init_jobs += 1
init_job_list = list(range(init_jobs))
new_sequence_list = np.array([], dtype=int).reshape(0, init_jobs)
for s in sequence_list:
for pos in range(s.size + 1):
new_sequence_list = np.vstack(
(new_sequence_list, np.insert(s, pos, init_jobs - 1)))
sequence_list = new_sequence_list
return best_sequence, best_makespan
def calculate_optimal_makespan(*args):
import genetic
import sys
# To make command line print complete nd arrays
np.set_printoptions(threshold=sys.maxsize)
problem = problem_num.get()
jobs = jobs_list[problem]
machines = machines_list[problem]
timeseed = timeseed_list[problem]
# Write these to GUI
jobs_val.set(jobs)
macs_val.set(machines)
time_val.set(timeseed)
# Generate matrix of times
a = random_matrix(machines, jobs, timeseed)
print("Problem No.:", problem, "\nMatrix:\n", a)
# Transpose matrix to calulate makespan
at = a.transpose()
# Start with all possible permutations of first 4 jobs
import itertools
init_jobs = 4
init_job_list = list(range(init_jobs))
# Generate all 24 permutations
sequence_list = np.array(list(itertools.permutations(init_job_list)))
# This loop will eventually run till init_jobs <= jobs
while init_jobs <= jobs:
makespan_list = np.array([])
for seq in sequence_list:
seq = list(seq)
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], seq))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Roulette wheel .. 3 x 20 output
sequence_list, makespan_list = roulette_wheel(sequence_list, makespan_list)
# Again, sort and reduce to 20
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Now ordered crossover each of the 20 sequences with each other and add to lists
for i in range(20):
for j in range(20):
if i != j:
child = genetic.ordered_crossover(sequence_list[i], sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Applying mutation. Inverse Mutation.
for i in sequence_list:
mutated = genetic.inverse_mutation(i)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Applying another mutation. Pairwise Swap Mutation.
for i in sequence_list:
mutated = genetic.pairwise_swap_mutation(i)
mutated = np.array(mutated)
sequence_list = np.vstack((sequence_list, mutated))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(mutated)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Ordered Crossover again
for i in range(20):
for j in range(20):
if i != j:
child = genetic.ordered_crossover(sequence_list[i], sequence_list[j])
child = np.array(child)
sequence_list = np.vstack((sequence_list, child))
makespan_list = np.append(makespan_list, calculate_makespan(at[init_job_list], list(child)))
# Sort both arrays in ascending order of makespan and reduce to 20 best sequences
sequence_list, makespan_list = sort_and_reduce(sequence_list, makespan_list)
# Set first element of sorted list as best makespan.
best_sequence = sequence_list[0]
best_makespan = makespan_list[0]
# Bring in next job into every position in sequence_list
init_jobs += 1
init_job_list = list(range(init_jobs))
new_sequence_list = np.array([]).reshape(0, init_jobs)
for s in sequence_list:
for pos in range(s.size + 1):
new_sequence_list = np.vstack((new_sequence_list, np.insert(s, pos, init_jobs - 1)))
sequence_list = new_sequence_list
print("\nBest Sequence:\n", best_sequence, "\n\n")
makespan_val.set(best_makespan)