High-performance order processing in picking workstations

Abstract

This paper investigates the order processing in picking workstations, which are among today’s most efficient order picking devices. A workstation is continuously supplied with storage bins containing stock keeping units (SKUs) from an interconnected storage system, so that a human worker only has to repack the items into customer bins. This way of unproductive work content, e.g., travel time, is eliminated and up to 1000 picks per hour is enabled. We aim at a concerted processing of picking orders and storage bins delivered from the storage system, such that an even more efficient picking process is enabled. We formalize the resulting order processing problem and present an efficient solution procedure. Our computational results show that the automated crane of the storage system feeding the picking workstation with bins can considerably be relieved by our procedure.

Appendix

This appendix provides an example for order processing when having an AS/RS focus. Given are our four orders that have initially been introduced in our example of Fig. 2. First, our AS/RS-focused planning approach determines a random order sequence, which represents the first-come-first-served sequence of orders. We assume that order sequence $\langle \{A,B\};\{A,B,D\};\{C\};\{A,C\} ]$ is the outcome of this random process. Analogously, the random sequence of bins queuing at the IO point is $\langle D;A;C;A;B]$. They need to be returned to storage positions $\langle r_{11};r_{13};r_{31};r_{25};r_{54}]$. The initial storage positions of bins and the empty storage spaces are given by the following rack layout, where $r_{xy}$ is applied to address the rack’s storage positions: The first batch of orders to be moved onto the workbench, for with we presuppose a capacity for $C=2$ orders, are the first two orders of the order sequence, i.e., orders $\{A,B\}$ and $\{A,B,D\}$. Together they require SKUs A, B, and D. Among the alternative positions of these SKUs in the rack, we always choose those closest to the IO point, so that the bins at storage positions $r_{12}$, $r_{22}$, and $r_{21}$ for SKUs A, B, and D, respectively, are selected. The resulting bipartite assignment graph between the storage positions to where the first three bins of the return queue need to be returned and the retrieval positions from where SKUs A, B, and D are to be retrieved is depicted in Fig. 7. For instance, pairing the storage of SKU D at position $r_{11}$ with the retrieval of SKU A from position $r_{12}$ to a dual-command is represented by the left most (light gray) arc and requires three distance units. Recall that the crane simultaneously moves in horizontal and vertical direction. The optimal assignment is represented by the bold arcs and indicates the selected dual-commands. Specifically, the first three SKUs D, A, and C according to the return queue are stored at the positions $r_{11}$, $r_{13}$, and $r_{31}$ and they are paired with SKUs B, A, and D to be retrieved from positions $r_{22}$, $r_{12}$, and $r_{21}$, respectively.

Fig. 7

Assignment graph and best solution (in bold) for the first subset of customer orders

Afterwards, we update the return sequence of bins to be stored in the AS/RS to $\langle A;B;B;A;D]$ and the rack: According to our order sequence, the next $C=2$ orders to be processed are orders $\{C\}$ and $\{A,C\}$. Note that the former order has already been placed on the workbench after completing order $\{A,B\}$ after the first two storage bins containing SKUs B and A of the previous batch. However, afterwards only the non required SKU D arrived at the workstation, so that for the new batch SKUs A and C from nearest positions $r_{13}$ and $r_{31}$, respectively, are required. They need to be paired with SKUS A and B to be returned into the AS/RS to positions $r_{25}$ and $r_{54}$, respectively. The resulting assignment graph and the bold arcs of the optimal solution are depicted in Fig. 8.

Fig. 8

Assignment graph and best solution (in bold) for the second subset of customer orders

In total, order processing with an AS/RS focus leads to five storage bins that need to processed and a total crane travel of 33 distance unit. With a workstation focus, solving our OPPW leads to just four bin deliveries [i.e., solution (a) depicted in Fig. 2] and total crane travel of 25 distance units. In our example, the workstation focus is, thus, better suited to relieve the crane of the bottleneck storage system.