def METHOD_NAME(objectives):
    from muse.decisions import weighted_sum
    weights = {"a": -1, "c": 2}
    def normalize(objective):
        min, max = objective.min("replacement"), objective.max("replacement")
        return (objective - min) / (max - min)
    expected = (
        normalize(objectives.a) * weights["a"]
        + normalize(objectives.b)
        + normalize(objectives.c).mean("other") * weights["c"]
    )
    actual = weighted_sum(objectives, weights)
    assert actual.values == approx(expected.values)