In general, there is no guarantee that any of these 4 algorithms will yield an optimal solution. If you experiment with problems similar to this one and with different sized bins and longer lists of items to pack, you will notice a problem that often occurs. Large or medium size items occurring late in a list are hard to pack since there is little room left over in the partially filled bins to accommodate them. This suggests a different approach to solving bin packing problems. Sorting numbers by size is something 2nd graders should practice.
Bin packing provides a meaningful context.
One needs to sort what are sometimes very long lists first, which can be time consuming. Although first fit or best fit decreasing do not always give optimal answers, there work remarkably well on most problems. I began this discussion with commenting on the role mathematics is playing in scheduling problems. The problem of scheduling processors to accomplish a collection of tasks is extremely complicated, even in the simple case we look at here, where the processors are all identical. Some of the complications that are involved are that the tasks may have a complex collection of requirements for the order in which they can be done.