During the COVID-19 pandemic period, global supply chains are disrupted and most of the workers cannot go to the factories to work. Robotic mobile fulfillment system (RMFS) is one of the new types of autonomous, parts-to-picker system, designed especially for e-commerce warehouses. The system can increase the productivity of pickers. However, no research discussed the application of RMFS in manufacturing sector. This research simulates the picking process of a factory warehouse and studies the operational sub-problems which include order line assignment, station selection, pod selection, and pod storage assignment. Different decision rules of each operational problem are proposed and evaluated. We analyze several key performance indicators (KPIs), makespan of picking for a production order, station utilization, robot utilization, and inventory reduction (in number of reels). The discrete event simulation model is constructed by an open source software, RAWSim-O, from the GitHub. Real world data was collected and evaluated for different combination of decision rules. The makespan and inventory reduction are very dependent to the number of reels handled. In the simulation result, the KPIs are sensitive to the decision rules in the order line selection and pod selection.
Progress in Material Handling Research
Ting, Ching-Jung; Permana, Hendra; and Meng, Hsien-Mi, "Decision Rules for the Robotic Mobile Fulfillment System of A PCB Assembly Factory Warehouse" (2023). 16th Proceedings (Dresden, Germany- 2023). 19.