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()
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