JSSP.solver module

class JSSP.solver.Solver(data)

Bases: object

The main solver class which calls tabu search and/or the genetic algorithm.

Parameters:data (Data) – JSSP instance data
genetic_algorithm_iter(iterations, population=None, population_size=200, selection_method_enum=<function _tournament_selection>, mutation_probability=0.8, selection_size=10, benchmark=False, verbose=False)

Performs the genetic algorithm for a certain number of generations.

First this function generates a random initial population if the population parameter is None, then it runs GA with the parameters specified and updates self.solution.

Parameters:
  • iterations (int) – number of generations to go through during the GA
  • population ([Solution]) – list of Solutions to start the GA from
  • population_size (int) – size of the initial population
  • selection_method_enum (GASelectionEnum) – selection method to use for selecting parents from the population. Options are GASelectionEnum.TOURNAMENT, GASelectionEnum.FITNESS_PROPORTIONATE, GASelectionEnum.RANDOM
  • mutation_probability (float) – probability of mutating a chromosome (i.e change an operation’s machine), must be in range [0, 1]
  • selection_size (int) – size of the selection group for tournament style selection
  • benchmark (bool) – if true benchmark data is gathered (i.e. # of iterations, makespans, min makespan iteration)
  • verbose (bool) – if true runs in verbose mode
Return type:

Solution
Returns:

best solution found
genetic_algorithm_time(runtime, population=None, population_size=200, selection_method_enum=<function _tournament_selection>, mutation_probability=0.8, selection_size=10, benchmark=False, verbose=False, progress_bar=False)

Performs the genetic algorithm for a certain number of seconds.

First this function generates a random initial population if the population parameter is None, then it runs GA with the parameters specified and updates self.solution.

Parameters:
  • runtime (float | datetime.timedelta) – either the number of seconds or timedelta to run the GA for
  • population ([Solution]) – list of Solutions to start the GA from
  • population_size (int) – size of the initial population
  • selection_method_enum (GASelectionEnum) – selection method to use for selecting parents from the population. Options are GASelectionEnum.TOURNAMENT, GASelectionEnum.FITNESS_PROPORTIONATE, GASelectionEnum.RANDOM
  • mutation_probability (float) – probability of mutating a chromosome (i.e change an operation’s machine), must be in range [0, 1]
  • selection_size (int) – size of the selection group for tournament style selection
  • benchmark (bool) – if true benchmark data is gathered (i.e. # of iterations, makespans, min makespan iteration)
  • verbose (bool) – if true runs in verbose mode
  • progress_bar (bool) – if true a progress bar is spawned
Return type:

Solution
Returns:

best solution found
iplot_benchmark_results()

Plots the benchmark results in an ipython notebook.

Returns:None

:raise UserWarning if one of the optimization functions was not ran in benchmark mode

output_benchmark_results(output_dir, title=None, auto_open=True)

Outputs html files containing benchmark results in the output directory specified.

Parameters:
  • output_dir (Path | str) – path to the output directory to place the results into
  • title (str) – title of the benchmark run
  • auto_open (bool) – if true index.html is automatically opened in a browser
Returns:

None

:raise UserWarning if one of the optimization functions was not ran in benchmark mode

tabu_search_iter(iterations, num_solutions_per_process=1, num_processes=4, tabu_list_size=50, neighborhood_size=300, neighborhood_wait=0.1, probability_change_machine=0.8, reset_threshold=100, initial_solutions=None, benchmark=False, verbose=False)

Performs parallel tabu search for a certain number of iterations.

First the function generates random initial solutions if the initial_solutions parameter is None, then it forks a number of child processes to run tabu search.

The parent process waits for the child processes to finish, then collects their results and updates self.solution.

Parameters:
  • iterations (int) – number of iterations for each tabu search to go through
  • num_solutions_per_process (int) – number of solutions that one tabu search process should gather
  • num_processes (int) – number of processes to run tabu search in parallel
  • tabu_list_size (int) – size of the tabu list
  • neighborhood_size (int) – size of neighborhoods to generate during tabu search
  • neighborhood_wait (float) – maximum time to wait while generating a neighborhood in seconds
  • probability_change_machine (float) – probability of changing a chosen operations machine, must be in range [0, 1]
  • reset_threshold (int) – number of iterations to potentially force a worse move after if the best solution is not improved
  • initial_solutions ([Solution]) – initial solutions to start the tabu searches from
  • benchmark (bool) – if true benchmark data is gathered (e.g. # of iterations, makespans, etc.)
  • verbose (bool) – if true runs in verbose mode
Return type:

Solution
Returns:

best solution found
tabu_search_time(runtime, num_solutions_per_process=1, num_processes=4, tabu_list_size=50, neighborhood_size=300, neighborhood_wait=0.1, probability_change_machine=0.8, reset_threshold=100, initial_solutions=None, benchmark=False, verbose=False, progress_bar=False)

Performs parallel tabu search for a certain number of seconds.

First the function generates random initial solutions if the initial_solutions parameter is None, then it forks/spawns num_processes of child processes to run tabu search in parallel.

The parent process waits for the child processes to finish, then collects their results and updates self.solution.

Parameters:
  • runtime (float | datetime.timedelta) – either the number of seconds or timedelta that tabu search should run for
  • num_solutions_per_process (int) – number of solutions that one tabu search process should gather
  • num_processes (int) – number of processes to run tabu search in parallel
  • tabu_list_size (int) – size of the tabu list
  • neighborhood_size (int) – size of neighborhoods to generate during tabu search
  • neighborhood_wait (float) – maximum time to wait while generating a neighborhood in seconds
  • probability_change_machine (float) – probability of changing a chosen operations machine, must be in range [0, 1]
  • reset_threshold (int) – number of iterations to potentially force a worse move after if the best solution is not improved
  • initial_solutions ([Solution]) – initial solutions to start the tabu searches from
  • benchmark (bool) – if true benchmark data is gathered (e.g. # of iterations, makespans, etc.)
  • verbose (bool) – if true runs in verbose mode
  • progress_bar (bool) – if true a progress bar is spawned
Return type:

Solution
Returns:

best solution found