  In this work, we consider online vector bin packing. It is known that no
algorithm can have a competitive ratio of $o(d/\log^2 d)$ in the absolute
sense, though upper bounds for this problem were always shown in the asymptotic
sense. Since variants of bin packing are traditionally studied with respect to
the asymptotic measure and since the two measures are different, we focus on
the asymptotic measure and prove new lower bounds on the asymptotic competitive
ratio. The existing lower bounds prior to this work were much smaller than $3$
even for very large dimensions.
  We significantly improve the best known lower bounds on the asymptotic
competitive ratio (and as a byproduct, on the absolute competitive ratio) for
online vector packing of vectors with $d \geq 3$ dimensions, for every such
dimension $d$. To obtain these results, we use several different constructions,
one of which is an adaptive construction showing a lower bound of
$\Omega(\sqrt{d})$. Our main result is that the lower bound of $\Omega(d/\log^2
d)$ on the competitive ratio holds also in the asymptotic sense. The last
result requires a careful adaptation of constructions for online coloring
rather than simple black-box reductions.
