Joint Optimization of Order Sequencing and Rack Scheduling in the Robotic Mobile Fulfilment System

Abstract

Putting the parts-to-picker idea into practice, the robotic mobile fulfillment system (RMFS) deploys robots to bring racks to stationary pickers so that the pickers focus only on order picking and the racks can be concurrently moved. An order sequencing and rack scheduling policy determines the processing sequence of given orders, as well as the allocation and arriving sequence of the racks. While order sequencing has been studied in the literature, there is little work on rack scheduling, which also affects the picking efficiency. We formulate a mixed-integer linear programming model and propose a two-phase hybrid heuristic approach to address the problem of joint optimization of order sequencing and rack scheduling in the RMFS. Our numerical studies show that jointly optimizing order sequencing and rack scheduling reduces the robotic tasks by up to 59.8%, 50.8%, 32.0% compared with benchmark solutions, rack scheduling only, and order sequencing only, respectively. Our findings provide managerial insights that expanding the workbench capacity and storage density enhances processing performance.