Optimal Selection of Movable Shelves under Cargo-to-Person Picking Mode

In recent years, some e-commerce companies such as Amazon have adopted the cargo-to-person picking mode to improve their pickup efficiency. Under this mode, a shelf can store several types of goods and a type of goods can be placed on some shelves. When orders arrive, the warehouse robots move one shelf or more containing the ordered items to a fixed platform, and the pickers select the items from the shelves. It is very important to decide which shelves should be moved to increase picking efficiency. This paper addresses the problem of optimal movable-shelf selection for the cargoto-person picking mode. The goal of this study is to minimize the total time (costs) of moving the selected shelves to finish a batch of orders. We model this problem using 0-1 linear programming and show that the problem is NP-hard. Furthermore, we propose a three-stage hybrid heuristic algorithm with polynomial complexity to solve it. We conduct numerical experiments to show the efficiency of this algorithm.