Container transportation takes a large portion in international maritime transportation. Recently, automated container storage yards have attracted much attention, due to its high standardization and low transportation costs. As a key to achieve high operational efficiency, scheduling of critical operational equipments, i.e. the scheduling cranes in the automated container storage yards, is an important issue. In the past decades, many methods have been proposed, ranging from traditional mathematical modeling methods to the emerging deep reinforcement learning based methods. In this paper, we briefly survey existing scheduling methods for automated container storage yards, and discuss some emerging directions in this important topic.