Unrelated parallel machine scheduling problem heuristic : Genetic algorithm

1. Introduction

In the Scheduling on Unrelated Parallel Machines problem, the goal is to find an jobs/machines assignment to minimize the overall makespan. In other words, the goal is to have the best balance between machines.

Unrelated-parallel-machine-scheduling-problem-1

A not well balanced schedule :
not-balanced-machines

A well balanced schedule :
balanced-machines

2. Problem data

In our problem, we’ll consider n jobs to be assigned on m machines.

2.1 Processing time

The jobs processing time will be manage as follow :
processing-times

2.2 Job assignment

The jobs/machines assignment will be manage as follow : If the job j is schedule on machine i then Xij = 1, else Xij = 0.
machines-jobs-assignement-table

3. Genetic algorithms

3.1 Introduction

In a genetic algorithm, a population of chromosomes is evolved toward better solutions. Each chromosome is defined by its genes. For each chromosome, you should be able to calculate it’s score, also called fitness.

genetic-algorithm-structure

genetic-algorithm-process

To find better solutions, the process is:
1- Evaluation: Sort the population based on chromosomes scores (fitness).
2- Selection: Choose the best chromosomes to generate the next population (natural selection).
3- Crossover: Mate the chromosomes between them by mixing their genome.
4- Mutation: As in a natural environment, some genes are changed arbitrarily.

3.2 Example

The goal is to give a practical idea of the genetic algorithm operations.
We’ll consider a problem with 2 machines (m=2) and 4 jobs (n=4).

Processing times :
example-processing-times-table

Population :
Let’s generate 4 chromosomes randomly :
example-population-chromosomes

Evaluation :
Evaluation of the generated chromosomes :
example-population-chromosomes-evaluation

Selection :
Select only the bests chromosomes, here we’ll choose to keep 75% of the sorted population :
example-population-chromosomes-selection

Crossover :
1 – Choose two random chromosomes in the selected ones (the best ones).
2 – Merge these two chromosomes by mixing their genome.
3 – Store the new generated chromosome in the new population.
4 – Repeat the crossover operation until the new population is fully generated.

example-population-chromosomes-crossover

Mutation:
The mutation operation is not systematic. Usually, around 1% of the crossover chromosomes will go through a mutation.
During this operation, a random gene is arbitrarily changed:
example-population-chromosomes-mutation

4 Code example

__author__ = 'rfontenay'
__description__ = 'Genetic algorithm to solve a scheduling problem of N jobs on M parallel machines'
import random
import time

# ******************* Parameters ******************* #
# Jobs processing times
jobsProcessingTime = [543, 545, 854, 766, 599, 657, 556, 568, 242, 371, 5, 569, 9, 614, 464, 557, 460, 970, 772, 886,
                      69, 423, 181, 98, 25, 642, 222, 842, 328, 799, 651, 197, 213, 666, 112, 136, 150, 810, 37, 620,
                      139, 721, 823, 351, 953, 765, 204, 800, 840, 132, 764, 336, 587, 514, 948, 134, 203, 766, 954,
                      537, 933, 458, 936, 835, 335, 690, 307, 102, 639, 635, 923, 699, 71, 913, 465, 664, 49, 198, 747,
                      931, 124, 41, 214, 246, 954, 676, 811, 295, 977, 100, 316, 453, 903, 50, 120, 320, 517, 441, 874,
                      842]
# Number of jobs
n = len(jobsProcessingTime)
# Number of machines
m = 2
# Genetic Algorithm : Population size
GA_POPSIZE = 256
# Genetic Algorithm : Elite rate
GA_ELITRATE = 0.1
# Genetic Algorithm : Mutation rate
GA_MUTATIONRATE = 0.25
# Genetic Algorithm : Iterations number
GA_ITERATIONS = 1000


# ******************* Functions ******************* #

def random_chromosome():
    """
    Description :Generate a chromosome with a random genome (for each job, assign a random machine).
    Input : -Line 2 of comment...
    Output : Random chromosome.
    """
    # Jobs assignment  : Boolean matrix with 1 line by job, 1 column by machine
    new_chromosome = [[0 for i in range(m)] for j in range(n)]
    # For each job, assign a random machine
    for i in range(n):
        new_chromosome[i][random.randint(0, m - 1)] = 1
    return new_chromosome


def fitness(chromosome):
    """
    Description : Calculate the score of the specified chromosome.
    The score is the longest processing time among all the machines processing times.
    Input : A chromosome.
    Output : The score/fitness.
    """
    max_processing_time = -1
    for i in range(m):
        machine_processing_time = 0
        for j in range(n):
            machine_processing_time += chromosome[j][i] * jobsProcessingTime[j]
        # Save the maximum processing time found
        if machine_processing_time > max_processing_time:
            max_processing_time = machine_processing_time
    return max_processing_time


def crossover(chromosome1, chromosome2):
    """
    Description : Crossover two chromosomes by alternative genes picking.
    Input : Two chromosome.
    Output : One chromosome.
    """
    new_chromosome = [[0 for i in range(m)] for j in range(n)]
    for i in range(n):
        # Alternate the pickup between the two selected solutions
        if not i % 2:
            new_chromosome[i] = chromosome1[i]
        else:
            new_chromosome[i] = chromosome2[i]
    return new_chromosome

def evolve(population):
    """
    Description : Create a new population based on the previous population.
    The new population is generated by mixing the best chromosomes of the previous population.
    Input : Old population.
    Output : New population.
    """
    new_population = [[] for i in range(GA_POPSIZE)]
    # First : Keep elites untouched
    elites_size = int(GA_POPSIZE * GA_ELITRATE)
    for i in xrange(elites_size):  # Elitism
        new_population[i] = population[i]
    # Then generate the new population
    for i in range(elites_size, GA_POPSIZE):
        # Generate new chromosome by crossing over two from the previous population
        new_population[i] = crossover(population[random.randint(0, GA_POPSIZE / 2)],
                                     population[random.randint(0, GA_POPSIZE / 2)])
        # Mutate
        if random.random() < GA_MUTATIONRATE:
            random_job = random.randint(0, n - 1)
            # Reset assignment
            new_population[i][random_job] = [0 for j in range(m)]
            # Random re-assignment
            new_population[i][random_job][random.randint(0, m - 1)] = 1
    return new_population


# ******************* Program ******************* #
# Measure execution time
start = time.time()

# Generate an initial random population
population = [[] for i in range(GA_POPSIZE)]
for i in range(GA_POPSIZE):
    population[i] = random_chromosome()

# Sort the population based on the fitness of chromosomes
population = sorted(population, key=lambda c: fitness(c))
# Print initial best makespan
print "Starting makespan = %03d" % (fitness(population[0]))
#Iterate
for i in range(GA_ITERATIONS):
    # Sort the population : order by chromosone's scores.
    population = sorted(population, key=lambda c: fitness(c))
    #Generate the following generation (new population)
    population = evolve(population)

# Print the best fitness and the execution time after iterations
print "Ending makespan   = %03d" % (fitness(population[0]))
print "Execution time = %02d s" % (time.time() - start)

7 thoughts on “Unrelated parallel machine scheduling problem heuristic : Genetic algorithm”

  1. Hey Ronan,
    Great article. I found your your blog, and this is a great example of your content, practical and accessible.
    Good to see you are still fooling around with programming apart from within your job!
    I’ll probably be in Singapore next year, but I saw you are back to Nantes… We are bound to bump into each other at some point in the future though!
    See you soon

  2. Hey Ronan,

    Great job on this article. Finding a clear code about something in this field was pretty difficult. You really helped me out, I was struggling on defining the chromosomes. With the help of this code, I am able to solve a multivariate bin packing problem in a couple of minutes.

    Thx!

  3. Nice Article.
    But there is one mistake. Unrelated PMS means each machine has own processing time for each job. You describe Identical PMS when each machine has same processing time for each job.

    1. I agree, this example was as simple as possible to be easy to understand. For the final use, each job/machine has a specific time, and each job change / machine has a specific setup time as well.

  4. code in python 3

    __author__ = ‘rfontenay’
    __description__ = ‘Genetic algorithm to solve a scheduling problem of N jobs on M parallel machines’
    import random
    import time

    # ******************* Parameters ******************* #
    # Jobs processing times
    jobsProcessingTime = [543, 545, 854, 766, 599, 657, 556, 568, 242, 371, 5, 569, 9, 614, 464, 557, 460, 970, 772, 886,
    69, 423, 181, 98, 25, 642, 222, 842, 328, 799, 651, 197, 213, 666, 112, 136, 150, 810, 37, 620,
    139, 721, 823, 351, 953, 765, 204, 800, 840, 132, 764, 336, 587, 514, 948, 134, 203, 766, 954,
    537, 933, 458, 936, 835, 335, 690, 307, 102, 639, 635, 923, 699, 71, 913, 465, 664, 49, 198, 747,
    931, 124, 41, 214, 246, 954, 676, 811, 295, 977, 100, 316, 453, 903, 50, 120, 320, 517, 441, 874,
    842]
    # Number of jobs
    n = len(jobsProcessingTime)
    # Number of machines
    m = 2
    # Genetic Algorithm : Population size
    GA_POPSIZE = 256
    # Genetic Algorithm : Elite rate
    GA_ELITRATE = 0.1
    # Genetic Algorithm : Mutation rate
    GA_MUTATIONRATE = 0.25
    # Genetic Algorithm : Iterations number
    GA_ITERATIONS = 1000

    # ******************* Functions ******************* #

    def random_chromosome():
    “””
    Description :Generate a chromosome with a random genome (for each job, assign a random machine).
    Input : -Line 2 of comment…
    Output : Random chromosome.
    “””
    # Jobs assignment : Boolean matrix with 1 line by job, 1 column by machine
    new_chromosome = [[0 for i in range(m)] for j in range(n)]
    # For each job, assign a random machine
    for i in range(n):
    new_chromosome[i][random.randint(0, m – 1)] = 1
    return new_chromosome

    def fitness(chromosome):
    “””
    Description : Calculate the score of the specified chromosome.
    The score is the longest processing time among all the machines processing times.
    Input : A chromosome.
    Output : The score/fitness.
    “””
    max_processing_time = -1
    for i in range(m):
    machine_processing_time = 0
    for j in range(n):
    machine_processing_time += chromosome[j][i] * jobsProcessingTime[j]
    # Save the maximum processing time found
    if machine_processing_time > max_processing_time:
    max_processing_time = machine_processing_time
    return max_processing_time

    def crossover(chromosome1, chromosome2):
    “””
    Description : Crossover two chromosomes by alternative genes picking.
    Input : Two chromosome.
    Output : One chromosome.
    “””
    new_chromosome = [[0 for i in range(m)] for j in range(n)]
    for i in range(n):
    # Alternate the pickup between the two selected solutions
    if not i % 2:
    new_chromosome[i] = chromosome1[i]
    else:
    new_chromosome[i] = chromosome2[i]
    return new_chromosome

    def evolve(population):
    “””
    Description : Create a new population based on the previous population.
    The new population is generated by mixing the best chromosomes of the previous population.
    Input : Old population.
    Output : New population.
    “””
    new_population = [[] for i in range(GA_POPSIZE)]
    # First : Keep elites untouched
    elites_size = int(GA_POPSIZE * GA_ELITRATE)
    for i in range((elites_size) ): # Elitism
    new_population[i] = population[i]
    # Then generate the new population
    for i in range(elites_size, GA_POPSIZE):
    # Generate new chromosome by crossing over two from the previous population
    new_population[i] = crossover(population[random.randint(0, GA_POPSIZE / 2)],
    population[random.randint(0, GA_POPSIZE / 2)])
    # Mutate
    if random.random() < GA_MUTATIONRATE:
    random_job = random.randint(0, n – 1)
    # Reset assignment
    new_population[i][random_job] = [0 for j in range(m)]
    # Random re-assignment
    new_population[i][random_job][random.randint(0, m – 1)] = 1
    return new_population

    # ******************* Program ******************* #
    # Measure execution time
    start = time.time()

    # Generate an initial random population
    population = [[] for i in range(GA_POPSIZE)]
    for i in range(GA_POPSIZE):
    population[i] = random_chromosome()

    # Sort the population based on the fitness of chromosomes
    population = sorted(population, key=lambda c: fitness(c))
    # Print initial best makespan
    print ( "Starting makespan = %03d" % (fitness(population[0])) )
    #Iterate
    for i in range(GA_ITERATIONS):
    # Sort the population : order by chromosone's scores.
    population = sorted(population, key=lambda c: fitness(c))
    #Generate the following generation (new population)
    population = evolve(population)

    # Print the best fitness and the execution time after iterations
    print ("Ending makespan = %03d" % (fitness(population[0])) )
    print ("Execution time = %02d s" % (time.time() – start))

  5. when I tried this code in Python it gives me a syntax error in this line,
    print “Starting makespan = %03d” % (fitness(population[0]))
    I don’t know where is the problem?!

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