Google Optimization Tools实现加工车间任务规划【Python版】

上一篇介绍了《使用.NET Core与Google Optimization Tools实现加工车间任务规划》，这次将Google官方文档python实现的版本的完整源码献出来，以满足喜爱python的朋友。

from __future__ import print_function

# Import Python wrapper for or-tools constraint solver.

from ortools.constraint_solver import pywrapcp

def main():

  # Create the solver.

  solver = pywrapcp.Solver('jobshop')

  machines_count = 3

  jobs_count = 3

  all_machines = range(0, machines_count)

  all_jobs = range(0, jobs_count)

  # Define data.

  machines = [[0, 1, 2],

              [0, 2, 1],

              [1, 2]]

  processing_times = [[3, 2, 2],

                      [2, 1, 4],

                      [4, 3]]

  # Computes horizon.

  horizon = 0

  for i in all_jobs:

    horizon += sum(processing_times[i])

  # Creates jobs.

  all_tasks = {}

  for i in all_jobs:

    for j in range(0, len(machines[i])):

      all_tasks[(i, j)] = solver.FixedDurationIntervalVar(0,

                                                          horizon,

                                                          processing_times[i][j],

                                                          False,

                                                          'Job_%i_%i' % (i, j))

  # Creates sequence variables and add disjunctive constraints.

  all_sequences = []

  all_machines_jobs = []

  for i in all_machines:

    machines_jobs = []

    for j in all_jobs:

      for k in range(0, len(machines[j])):

        if machines[j][k] == i:

          machines_jobs.append(all_tasks[(j, k)])

    disj = solver.DisjunctiveConstraint(machines_jobs, 'machine %i' % i)

    all_sequences.append(disj.SequenceVar())

    solver.Add(disj)

  # Add conjunctive contraints.

  for i in all_jobs:

    for j in range(0, len(machines[i]) - 1):

      solver.Add(all_tasks[(i, j + 1)].StartsAfterEnd(all_tasks[(i, j)]))

  # Set the objective.

  obj_var = solver.Max([all_tasks[(i, len(machines[i])-1)].EndExpr()

                        for i in all_jobs])

  objective_monitor = solver.Minimize(obj_var, 1)

  # Create search phases.

  sequence_phase = solver.Phase([all_sequences[i] for i in all_machines],

                                solver.SEQUENCE_DEFAULT)

  vars_phase = solver.Phase([obj_var],

                            solver.CHOOSE_FIRST_UNBOUND,

                            solver.ASSIGN_MIN_VALUE)

  main_phase = solver.Compose([sequence_phase, vars_phase])

  # Create the solution collector.

  collector = solver.LastSolutionCollector()

  # Add the interesting variables to the SolutionCollector.

  collector.Add(all_sequences)

  collector.AddObjective(obj_var)

  for i in all_machines:

    sequence = all_sequences[i];

    sequence_count = sequence.Size();

    for j in range(0, sequence_count):

      t = sequence.Interval(j)

      collector.Add(t.StartExpr().Var())

      collector.Add(t.EndExpr().Var())

  # Solve the problem.

  disp_col_width = 10

  if solver.Solve(main_phase, [objective_monitor, collector]):

    print("\nOptimal Schedule Length:", collector.ObjectiveValue(0), "\n")

    sol_line = ""

    sol_line_tasks = ""

    print("Optimal Schedule", "\n")

    for i in all_machines:

      seq = all_sequences[i]

      sol_line += "Machine " + str(i) + ": "

      sol_line_tasks += "Machine " + str(i) + ": "

      sequence = collector.ForwardSequence(0, seq)

      seq_size = len(sequence)

      for j in range(0, seq_size):

        t = seq.Interval(sequence[j]);

         # Add spaces to output to align columns.

        sol_line_tasks +=  t.Name() + " " * (disp_col_width - len(t.Name()))

      for j in range(0, seq_size):

        t = seq.Interval(sequence[j]);

        sol_tmp = "[" + str(collector.Value(0, t.StartExpr().Var())) + ","

        sol_tmp += str(collector.Value(0, t.EndExpr().Var())) + "] "

        # Add spaces to output to align columns.

        sol_line += sol_tmp + " " * (disp_col_width - len(sol_tmp))

      sol_line += "\n"

      sol_line_tasks += "\n"

    print(sol_line_tasks)

    print("Time Intervals for Tasks\n")

    print(sol_line)

if __name__ == '__main__':

  main()

Google Optimization Tools实现加工车间任务规划【Python版】的相关教程结束。