Airbus Quantum Computing Challenge 2020

Implementation of the solution to the Airbus Quantum Challenge 2020 aircraft loading problem proposed in Aircraft Loading Optimization -- QUBO models under multiple constraints.
The optimization process can be run on both a simulated annealer and real D-Wave solver.
The current implementation lacks an effective strategy to tune the coefficients so a lot of runs are required.

How to run

• Install requirements pip install -r requirements.txt
• [Optional] Create an account at D-Wave Leap
• [Optional] Take your API key then run dwave config create
• Run python main.py

Customize problem parameters

To change the aircraft data create an AircraftData object and a shear curve. Two functions are provided to create symmetric and asymmetric linear curves.

    shear_curve = get_linear_shear_curve(4, 26000)
    acft = AircraftData(num_positions=4, payloa_area_length=40, max_payload=8000, shear_curve=shear_curve, min_cg=-0.1, max_cg=0.2)

To change the problem requirements first define the container types and masses as lists.
Each point of the shear curve must correspond to a container position with the center elements representing the central position left and right shears.

    cont_types = np.array(['t1', 't1', 't1', 't1', 't1', 't1'])
    cont_masses = np.array([2134, 3455, 1866, 1699, 3500, 3332])

Define the penalty functions coefficients as a dict

    coefs = {'pl_o': 1.0,
             'pl_w': 1.0,
             'pl_d': 1.0,
             'pl_c': 1.0,
             'cl_u': 1.0,
             'cl_l': 1.0,
             'cl_t': 1.0,
             'sl_l': 1.0,
             'sl_r': 1.0}

Finally create a new LoadingProblem and set its coefficients

    problem = LoadingProblem(acft, cont_types, cont_masses, 0.1, 120000, -0.05)
    problem.coefficients = coefs

The LoadingProblem class allows to get the BQM representation of each constraint as well as the total problem to pass to the solver. For example to create a BQM to pass to the solvers use

    bqm = problem.get_bqm()

The plot functions assume feasible solutions so first parse the solver results then remove the unfeasible solutoins. The plot the top k solutions.

    cont_occ_solutions = problem.parse_solution(result)
    cont_occ_solutions = problem.filter_solutions(cont_occ_solutions)

    plot_top_solutions(problem, cont_occ_solutions, 1)

Plots will be saved in the out directory.

Todo

• Payload constraints
• CG constraints
• Shear constraints
• Improve coefficient tuning. This should increase the ratio of feasible solutions found by the solvers
• Try hybrid solvers