Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems
A new MIP-model is presented to integrate both decision problems about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer’s order and a heuristic is proposed to reduce the computational time for a real-world application.
Abstract
Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing\nsystem, which has received more attention recently, due to increasing growth in\nthe e-commerce sector. Instead of sending pickers to the inventory area to\nsearch for and pick the ordered items, robots carry shelves (called "pods")\nincluding ordered items from the inventory area to picking stations. In the\npicking stations, human pickers put ordered items into totes; then these items\nare transported by a conveyor to the packing stations. This type of warehousing\nsystem relieves the human pickers and improves the picking process. In this\npaper, we concentrate on decisions about the assignment of pods to stations and\norders to stations to fulfill picking for each incoming customer's order. In\nprevious research for an RMFS with multiple picking stations, these decisions\nare made sequentially. Instead, we present a new integrated model. To improve\nthe system performance even more, we extend our model by splitting orders. This\nmeans parts of an order are allowed to be picked at different stations. To the\nbest of the authors' knowledge, this is the first publication on split orders\nin an RMFS. We analyze different performance metrics, such as pile-on,\npod-station visits, robot moving distance and order turn-over time. We compare\nthe results of our models in different instances with the sequential method in\nour open-source simulation framework RAWSim-O.\n