# Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations
## 2 Literature Review
### RFMS Design and Operation  
Following the earlier work about Kiva Systems ([PERSON] and [PERSON], 2011 [42]), most of the recent research about RMFS focuses on operational issues including: order and rack sequencing ([PERSON] et al., 2017 [11]; [PERSON] et al., 2017 [38]; [PERSON] et al., 2019 [27]; [PERSON] et al., 2020, [43]; [PERSON] et al., 2021, [44]), workstation assignment ([PERSON] et al., 2017 [31]; [PERSON] et al., 2021, [44]), storage policy ([PERSON] et al., 2019 [30]; [PERSON] et al., 2019 [27]; [PERSON] et al., 2020, [43]; [PERSON] et al., 2020, [45]), and traffic planning ([PERSON] et al., 2007 [46]; [PERSON] and [PERSON], 2011 [47]; [PERSON] et al., 2018 [48]). [PERSON] et al. (2017) [11] propose a mobile robot-based order-picking problem (MROP) to determine the order sequence that was handled in a workstation as well as the sequence of racks that feed the workstation, whereas no discussion on layout was conducted. [PERSON] et al. Figure 2: The flying-V (**left**) and fishbone layout (**right**). (2017) [38] studied the similar problem using a single workstation by formulating it as an integer programming model. [PERSON] et al. (2019) [30] developed a linear travel distance function to discuss several typical storage policies. Their study considers multiple workstations that are located around the storage area, but the positions of workstations are pre-defined. [PERSON] et al. (2019) [27] used discrete event simulations to evaluate the RMFS performance in both the pick and replenishment process by evaluating multiple decision rules related to order assignment, pod selection and storage assignment problems. The workstation layout problem is pointed out but not addressed. [PERSON] et al. (2021) [44] investigate the optimal operational policies with splitable orders in RMFS, in which the decisions that assignment of pods to workstations and orders to workstations are involved. They claim that the system performances are significantly improved (e.g., increasing throughput by 46%) if parts of an order are allowed to be picked at different stations. Although some decisions that are related to this work are considered (i.e., pod-to-workstation assignment), the workstation layout decisions are still not mentioned. There is also some research addressing design problems on a strategic level, typically by analytic methods. [PERSON] et al. (2017) [31] used queue theory to evaluate the retrieval throughput time under different policies by which to assign pods to workstations. The paper analyzed the optimal shelf block size combined with an optimal length-to-width ratio. [PERSON] et al. (2017) [10] applied queue theory again to estimate maximum order throughput, average order cycle time, and robot use. Through a set of numerical experiments, they found that the warehouse performance is affected by the location of the workstations around the storage area, as well as the length-to-width ratio. However, both papers treat the locations of workstations as a fixed position instead of considering them as decision variables. Storage policy is obviously important to the system performance, which determines which location is assigned for a returning pod and thus influences the total travel distance of robots. According to [PERSON] et al. (2019) [30], there are generally four storage policies, namely full-turnover-based storage, class-based storage, closest-open-location storage, and random storage. These policies have advantages and disadvantages in terms of travel distance and space use, and depending on application context. This paper adopts the random storage policy, in which items are stored randomly on pods, and pods are returned to randomly assigned storage locations. It can lead to a high space use, since all storage locations are available to all storage units. As we can see from Table 1, [PERSON] et al. (2017) [31] also used this policy, and [PERSON] et al. (2018) [27] and [PERSON] et al. (2019) [30] compare the performance under random storage with other storage policies. [PERSON] et al.