OutboundStudentProblem/MainExample.py at main · alexisuaguilaru/OutboundStudentProblem · GitHub

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import numpy as np
import PyCode as src

if __name__ == '__main__':
    # Example Instance
    num_boxes = 8
    max_weight = 24
    egg_box = src.EggBox(2,2,2,max_weight)
    support_threshold = 0.75

    boxes = np.array([
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
        src.SmallBox(1,1,1),
    ])
    weight_boxes = np.array([3,3,3,3,3,3,3,3])


    # Definition of Objective Function with DE Optimizer
    penalties_weights = np.array([1,1,1])
    obj_boxes = src.InitObjectiveBoxes(
        egg_box,
        boxes,
        SupportThreshold = support_threshold,
        PenaltiesWeights = penalties_weights,
        InitOptimizer = src.DifferentialEvolutionOptimizer,
        OptimizerParameters = dict(
            Iterations = 10,
            PopulationSize = 20,
            ScalingFactor = 0.3,
            CrossoverRate = 0.6,
        ),
    )


    # Definition of Functions for Tabu Search
    rand_solution = src.InitRandomBoxesSolution(weight_boxes,max_weight)
    generate_neighborhood = src.InitGenerateNeighborhoodBoxes(weight_boxes,max_weight)


    # Definitions of TB Optimizer
    tabu_search = src.TabuSearchOptimizer(
        obj_boxes,
        rand_solution,
        generate_neighborhood,
        src.TabuRepresentationBoxes,
    )


    # Calling of TB Optimizer to Find Best Positions
    max_iterations = 6
    tabu_time = 3
    best_boxes , snapshots_boxes = tabu_search(max_iterations,tabu_time)


    # Results Presentation
    best_positions = snapshots_boxes[-1].Positions
    print(f'Selected Boxes: {best_boxes[:num_boxes]}')
    print(f'Rotations Boxes: {best_boxes[num_boxes:]}')
    print(f'Positions of Each Box:\n{best_positions}')