具有多个箱子和约束的无界背包

我是 Python 编码新手，需要帮助解决具有多个垃圾箱（4 个垃圾箱）和约束的无界背包问题。

1. 这些箱子的重量限制分别为 10.5、10.5、7 和 7。
2. 每个箱子只能装满某些物品。例如，仓 0 只能填充项目 0-9，仓 1 只能填充项目 10-19，等等。
3. 该算法递归运行，直到达到总权重限制 3000。

我已经修改了 or-tools 中可用的代码，如下所示。输出非常接近我的要求，但我在第 2 项和第 3 项上遇到了问题。

from ortools.linear_solver import pywraplp


def main():
    data = {}
    data['weights'] = [
        0.95,0.31,0.95,0.95,0.95,0.95,0.95,0.95,0.95,0.75,0.95,0.78,0.45,1.00,1.00,1.00,0.19,1.00,1.00,0.50,3.00,3.00,3.00,0.75,
     1.25,5.00,3.00,1.00,3.00,1.00,1.00,0.63,3.00,1.00,0.55,1.00,1.00,0.75,0.67,0.38
    ]
    data['values'] = [
        0.8554,0.7855,0.7406,0.7282,0.7855,0.8653,0.8529,0.9177,0.8329,0.9077,0.7706,0.8928,0.8354,0.9077,0.8379,0.8055,0.8778,
     0.8778,0.9252,0.9501,0.9451,0.9377,0.9252,0.9102,0.9252,0.9476,0.9426,0.9476,0.9377,0.9302,0.9401,0.8579,0.8678,0.8903,
     0.8853,0.8105,0.9077,0.9152,0.9152,0.8579
    ]
    assert len(data['weights']) == len(data['values'])
    data['num_items'] = len(data['weights'])
    data['all_items'] = range(data['num_items'])

    data['bin_capacities'] = [10.5, 10.5, 7, 7]
    data['num_bins'] = len(data['bin_capacities'])
    data['all_bins'] = range(data['num_bins'])

    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver('SCIP')
    if solver is None:
        print('SCIP solver unavailable.')
        return

    # Variables.
    # x[i, b] = 1 if item i is packed in bin b.
    x = {}
    for i in data['all_items']:
        for b in data['all_bins']:
            x[i, b] = solver.BoolVar(f'x_{i}_{b}')

    # Constraints.
    # Each item is assigned to at most one bin.
    for i in data['all_items']:
        solver.Add(sum(x[i, b] for b in data['all_bins']) <= 1)

    # The amount packed in each bin cannot exceed its capacity.
    for b in data['all_bins']:
        solver.Add(
            sum(x[i, b] * data['weights'][i]
                for i in data['all_items']) <= data['bin_capacities'][b])

    # Objective.
    # Maximize total value of packed items.
    objective = solver.Objective()
    for i in data['all_items']:
        for b in data['all_bins']:
            objective.SetCoefficient(x[i, b], data['values'][i])
    objective.SetMaximization()

    status = solver.Solve()

    if status == pywraplp.Solver.OPTIMAL:
        print(f'Total packed value: {objective.Value()}')
        total_weight = 0
        for b in data['all_bins']:
            print(f'Bin {b}')
            bin_weight = 0
            bin_value = 0
            for i in data['all_items']:
                if x[i, b].solution_value() > 0:
                    print(
                        f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
                    )
                    bin_weight += data['weights'][i]
                    bin_value += data['values'][i]
            print(f'Packed bin weight: {bin_weight}')
            print(f'Packed bin value: {bin_value}\n')
            total_weight += bin_weight
        print(f'Total packed weight: {total_weight}')
    else:
        print('The problem does not have an optimal solution.')


if __name__ == '__main__':
    main()