Efficient Task Allocation in Smart Warehouses with Multi-Delivery Stations and Heterogeneous Robots

Abstract

Robotic Mobile Fulfillment Systems (RMFS) are crucial for increasing the efficiency of the logistics sector. One of the essential processes in such systems is efficiently allocating tasks to robots to reduce costs and speed up order fulfillment. In this context, we found a gap in exploring the possibility of using heterogeneous robot fleets in RMFS plus other real constraints, such as dynamic orders and multi-delivery stations. This work presents a space decomposition-driven heuristic based on Voronoi Tessellation concepts to efficiently allocate tasks to robots in an environment with several constraints. Experiments deal with a large simulated smart warehouse, with dynamic orders and thousands of tasks and robots. Results showed that our solution reduced route costs and time to fulfill orders up to 45% and 24%, respectively, compared to the state-of-the-art algorithm.