Dynamic Load Scheduling Using Clustering for Increasing Efficiency of Warehouse Order Fulfillment Done Through Pick and Place Bots
Clustering; Dynamic order allocation; Fuzzy C-means clustering; Machine Learning; Master Bot; Order picking; Slave Bot; Swarm robotics
The domain of warehouse automation has been picking up due to the vast developments in e-commerce owing to growing demand and the need to improve customer satisfaction. The one crucial component that needs to be integrated into large warehouses is automated pick and place of orders from the storage facility using automated vehicles integrated with a forklift (Pick and Place bots). Even with automation being employed, there is a lot of room for improvement with the current technology being used as the loading of the bots is inefficient and not dynamic. This paper discusses a method to dynamically allocate load between the Pick and Place BOTs in a warehouse during order fulfillment. This dynamic allocation is done using clustering,an unsupervised Machine Learning algorithm. This paper discusses using fuzzy C-means clustering to improve the efficiency of warehouse automation. The discussed algorithm improves the efficiency of order fulfillment significantly and is demonstrated in this paper using multiple simulations to see around 35% reduction in order fulfillment time and around 55% increase in efficiency. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-613 LNNS, pp. 315-328.
Springer Science and Business Media Deutschland GmbH
Baby C.T., CHRIST (Deemed to be University), Bangalore, 560029, India; Baby C.J., Fupro Innovation Private Limited, Mohali, 160055, India