Note
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Gantt Chart¶
In this example, we illustrate the use of the Gantt chart plot on the Sobieski’s SSBJ problem.
Import¶
The first step is to import some high-level functions and a method to get the design space.
from __future__ import annotations
from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.core.discipline import MDODiscipline
from gemseo.post.core.gantt_chart import create_gantt_chart
from gemseo.problems.sobieski.core.design_space import SobieskiDesignSpace
configure_logger()
<RootLogger root (INFO)>
Create disciplines¶
Then, we instantiate the disciplines of the Sobieski’s SSBJ problem: Propulsion, Aerodynamics, Structure and Mission
disciplines = create_discipline([
"SobieskiPropulsion",
"SobieskiAerodynamics",
"SobieskiStructure",
"SobieskiMission",
])
Create design space¶
We also create the SobieskiDesignSpace.
design_space = SobieskiDesignSpace()
Create and execute scenario¶
The next step is to build an MDO scenario in order to maximize the range, encoded ‘y_4’, with respect to the design parameters, while satisfying the inequality constraints ‘g_1’, ‘g_2’ and ‘g_3’. We can use the MDF formulation, the SLSQP optimization algorithm and a maximum number of iterations equal to 100.
scenario = create_scenario(
disciplines,
"MDF",
"y_4",
design_space,
maximize_objective=True,
)
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, constraint_type="ineq")
Activate time stamps¶
In order to record all time stamps recording, we have to call this method before the execution of the scenarios
MDODiscipline.activate_time_stamps()
scenario.execute({"algo": "SLSQP", "max_iter": 10})
INFO - 13:08:23:
INFO - 13:08:23: *** Start MDOScenario execution ***
INFO - 13:08:23: MDOScenario
INFO - 13:08:23: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:08:23: MDO formulation: MDF
INFO - 13:08:23: Optimization problem:
INFO - 13:08:23: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:08:23: with respect to x_1, x_2, x_3, x_shared
INFO - 13:08:23: subject to constraints:
INFO - 13:08:23: g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:08:23: g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:08:23: g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:08:23: over the design space:
INFO - 13:08:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:08:23: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:08:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:08:23: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:08:23: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:08:23: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:08:23: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:08:23: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:08:23: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:08:23: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:08:23: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:08:23: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:08:23: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:08:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:08:23: Solving optimization problem with algorithm SLSQP:
INFO - 13:08:23: 10%|█ | 1/10 [00:00<00:02, 4.06 it/sec, obj=-536]
INFO - 13:08:23: 20%|██ | 2/10 [00:00<00:02, 3.08 it/sec, obj=-2.12e+3]
WARNING - 13:08:24: MDAJacobi has reached its maximum number of iterations but the normed residual 2.338273970736908e-06 is still above the tolerance 1e-06.
INFO - 13:08:24: 30%|███ | 3/10 [00:01<00:02, 2.49 it/sec, obj=-3.56e+3]
INFO - 13:08:24: 40%|████ | 4/10 [00:01<00:02, 2.37 it/sec, obj=-3.96e+3]
INFO - 13:08:25: 50%|█████ | 5/10 [00:02<00:02, 2.31 it/sec, obj=-3.96e+3]
INFO - 13:08:25: Optimization result:
INFO - 13:08:25: Optimizer info:
INFO - 13:08:25: Status: 8
INFO - 13:08:25: Message: Positive directional derivative for linesearch
INFO - 13:08:25: Number of calls to the objective function by the optimizer: 6
INFO - 13:08:25: Solution:
INFO - 13:08:25: The solution is feasible.
INFO - 13:08:25: Objective: -3963.403105287515
INFO - 13:08:25: Standardized constraints:
INFO - 13:08:25: g_1 = [-0.01806054 -0.03334606 -0.04424918 -0.05183437 -0.05732588 -0.13720864
INFO - 13:08:25: -0.10279136]
INFO - 13:08:25: g_2 = 3.1658077606078194e-06
INFO - 13:08:25: g_3 = [-7.67177346e-01 -2.32822654e-01 -5.57051011e-06 -1.83255000e-01]
INFO - 13:08:25: Design space:
INFO - 13:08:25: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:08:25: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:08:25: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:08:25: | x_shared[0] | 0.01 | 0.06000079145194018 | 0.09 | float |
INFO - 13:08:25: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:08:25: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 13:08:25: | x_shared[3] | 2.5 | 2.5 | 8.5 | float |
INFO - 13:08:25: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:08:25: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 13:08:25: | x_1[0] | 0.1 | 0.3999999322608766 | 0.4 | float |
INFO - 13:08:25: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 13:08:25: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 13:08:25: | x_3 | 0.1 | 0.1562438752833519 | 1 | float |
INFO - 13:08:25: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:08:25: *** End MDOScenario execution (time: 0:00:02.344064) ***
{'max_iter': 10, 'algo': 'SLSQP'}
Post-process scenario¶
Lastly, we plot the Gantt chart.
create_gantt_chart(save=False, show=True)
# Finally, we deactivate the time stamps for other executions
MDODiscipline.deactivate_time_stamps()
Total running time of the script: (0 minutes 2.656 seconds)