reformat python files as tab length = 4 now
This commit is contained in:
Laurent Perron
@@ -22,142 +22,145 @@ from ortools.sat.python import cp_model
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def SolvingLinearProblem():
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"""CP-SAT linear solver problem."""
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# Create a model.
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model = cp_model.CpModel()
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"""CP-SAT linear solver problem."""
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# Create a model.
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model = cp_model.CpModel()
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# x and y are integer non-negative variables.
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x = model.NewIntVar(0, 17, 'x')
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y = model.NewIntVar(0, 17, 'y')
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model.Add(2 * x + 14 * y <= 35)
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model.Add(2 * x <= 7)
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obj_var = model.NewIntVar(0, 1000, 'obj_var')
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model.Add(obj_var == x + 10 * y)
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model.Maximize(obj_var)
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# x and y are integer non-negative variables.
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x = model.NewIntVar(0, 17, 'x')
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y = model.NewIntVar(0, 17, 'y')
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model.Add(2 * x + 14 * y <= 35)
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model.Add(2 * x <= 7)
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obj_var = model.NewIntVar(0, 1000, 'obj_var')
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model.Add(obj_var == x + 10 * y)
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model.Maximize(obj_var)
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# Create a solver and solve.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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if status == cp_model.OPTIMAL:
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print('Objective value: %i' % solver.ObjectiveValue())
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print()
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print('x= %i' % solver.Value(x))
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print('y= %i' % solver.Value(y))
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# Create a solver and solve.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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if status == cp_model.OPTIMAL:
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print('Objective value: %i' % solver.ObjectiveValue())
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print()
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print('x= %i' % solver.Value(x))
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print('y= %i' % solver.Value(y))
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def MinimalJobShop():
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"""Minimal jobshop problem."""
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# Create the model.
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model = cp_model.CpModel()
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"""Minimal jobshop problem."""
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# Create the model.
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model = cp_model.CpModel()
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machines_count = 3
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jobs_count = 3
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all_machines = range(0, machines_count)
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all_jobs = range(0, jobs_count)
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# Define data.
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machines = [[0, 1, 2], [0, 2, 1], [1, 2]]
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machines_count = 3
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jobs_count = 3
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all_machines = range(0, machines_count)
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all_jobs = range(0, jobs_count)
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# Define data.
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machines = [[0, 1, 2], [0, 2, 1], [1, 2]]
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processing_times = [[3, 2, 2], [2, 1, 4], [4, 3]]
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# Computes horizon.
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horizon = 0
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for job in all_jobs:
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horizon += sum(processing_times[job])
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task_type = collections.namedtuple('task_type', 'start end interval')
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assigned_task_type = collections.namedtuple('assigned_task_type',
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'start job index')
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# Creates jobs.
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all_tasks = {}
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for job in all_jobs:
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for index in range(0, len(machines[job])):
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start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (job, index))
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duration = processing_times[job][index]
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end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (job, index))
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interval_var = model.NewIntervalVar(start_var, duration, end_var,
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'interval_%i_%i' % (job, index))
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all_tasks[(job, index)] = task_type(
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start=start_var, end=end_var, interval=interval_var)
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# Creates sequence variables and add disjunctive constraints.
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for machine in all_machines:
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intervals = []
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processing_times = [[3, 2, 2], [2, 1, 4], [4, 3]]
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# Computes horizon.
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horizon = 0
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for job in all_jobs:
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for index in range(0, len(machines[job])):
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if machines[job][index] == machine:
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intervals.append(all_tasks[(job, index)].interval)
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model.AddNoOverlap(intervals)
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horizon += sum(processing_times[job])
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# Add precedence contraints.
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for job in all_jobs:
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for index in range(0, len(machines[job]) - 1):
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model.Add(
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all_tasks[(job, index + 1)].start >= all_tasks[(job, index)].end)
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task_type = collections.namedtuple('task_type', 'start end interval')
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assigned_task_type = collections.namedtuple('assigned_task_type',
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'start job index')
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# Makespan objective.
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obj_var = model.NewIntVar(0, horizon, 'makespan')
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model.AddMaxEquality(
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obj_var,
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[all_tasks[(job, len(machines[job]) - 1)].end for job in all_jobs])
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model.Minimize(obj_var)
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# Solve model.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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if status == cp_model.OPTIMAL:
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# Print out makespan.
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print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
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print()
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# Create one list of assigned tasks per machine.
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assigned_jobs = [[] for _ in range(machines_count)]
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# Creates jobs.
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all_tasks = {}
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for job in all_jobs:
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for index in range(len(machines[job])):
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machine = machines[job][index]
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assigned_jobs[machine].append(
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assigned_task_type(
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start=solver.Value(all_tasks[(job, index)].start),
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job=job,
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index=index))
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disp_col_width = 10
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sol_line = ''
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sol_line_tasks = ''
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print('Optimal Schedule', '\n')
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for index in range(0, len(machines[job])):
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start_var = model.NewIntVar(0, horizon,
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'start_%i_%i' % (job, index))
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duration = processing_times[job][index]
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end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (job, index))
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interval_var = model.NewIntervalVar(
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start_var, duration, end_var, 'interval_%i_%i' % (job, index))
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all_tasks[(job, index)] = task_type(
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start=start_var, end=end_var, interval=interval_var)
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# Creates sequence variables and add disjunctive constraints.
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for machine in all_machines:
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# Sort by starting time.
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assigned_jobs[machine].sort()
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sol_line += 'Machine ' + str(machine) + ': '
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sol_line_tasks += 'Machine ' + str(machine) + ': '
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intervals = []
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for job in all_jobs:
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for index in range(0, len(machines[job])):
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if machines[job][index] == machine:
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intervals.append(all_tasks[(job, index)].interval)
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model.AddNoOverlap(intervals)
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for assigned_task in assigned_jobs[machine]:
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name = 'job_%i_%i' % (assigned_task.job, assigned_task.index)
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# Add spaces to output to align columns.
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sol_line_tasks += name + ' ' * (disp_col_width - len(name))
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start = assigned_task.start
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duration = processing_times[assigned_task.job][assigned_task.index]
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# Add precedence contraints.
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for job in all_jobs:
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for index in range(0, len(machines[job]) - 1):
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model.Add(
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all_tasks[(job, index + 1)].start >= all_tasks[(job,
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index)].end)
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sol_tmp = '[%i,%i]' % (start, start + duration)
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# Add spaces to output to align columns.
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sol_line += sol_tmp + ' ' * (disp_col_width - len(sol_tmp))
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# Makespan objective.
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obj_var = model.NewIntVar(0, horizon, 'makespan')
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model.AddMaxEquality(
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obj_var,
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[all_tasks[(job, len(machines[job]) - 1)].end for job in all_jobs])
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model.Minimize(obj_var)
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sol_line += '\n'
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sol_line_tasks += '\n'
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# Solve model.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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print(sol_line_tasks)
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print('Time Intervals for task_types\n')
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print(sol_line)
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if status == cp_model.OPTIMAL:
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# Print out makespan.
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print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
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print()
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# Create one list of assigned tasks per machine.
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assigned_jobs = [[] for _ in range(machines_count)]
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for job in all_jobs:
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for index in range(len(machines[job])):
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machine = machines[job][index]
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assigned_jobs[machine].append(
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assigned_task_type(
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start=solver.Value(all_tasks[(job, index)].start),
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job=job,
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index=index))
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disp_col_width = 10
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sol_line = ''
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sol_line_tasks = ''
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print('Optimal Schedule', '\n')
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for machine in all_machines:
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# Sort by starting time.
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assigned_jobs[machine].sort()
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sol_line += 'Machine ' + str(machine) + ': '
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sol_line_tasks += 'Machine ' + str(machine) + ': '
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for assigned_task in assigned_jobs[machine]:
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name = 'job_%i_%i' % (assigned_task.job, assigned_task.index)
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# Add spaces to output to align columns.
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sol_line_tasks += name + ' ' * (disp_col_width - len(name))
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start = assigned_task.start
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duration = processing_times[assigned_task.job][assigned_task.
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index]
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sol_tmp = '[%i,%i]' % (start, start + duration)
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# Add spaces to output to align columns.
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sol_line += sol_tmp + ' ' * (disp_col_width - len(sol_tmp))
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sol_line += '\n'
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sol_line_tasks += '\n'
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print(sol_line_tasks)
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print('Time Intervals for task_types\n')
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print(sol_line)
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def main(_):
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print('--- SolvingLinearProblem ---')
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SolvingLinearProblem()
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print('--- MinimalJobShop ---')
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MinimalJobShop()
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print('--- SolvingLinearProblem ---')
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SolvingLinearProblem()
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print('--- MinimalJobShop ---')
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MinimalJobShop()
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if __name__ == '__main__':
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main(None)
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main(None)
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