Efficient Task Planning for Heterogeneous AGVs in Warehouses

Abstract

Recently, heterogeneous automated guided vehicles (AGVs) are widely deployed to improve the work efficiency of the logistics warehouse with limited space. While completing various tasks to improve efficiency and reduce transport costs, heterogeneous AGVs also bring new challenges to task planning in warehouses because of their different shapes, speeds, and capabilities. Briefly, it is difficult to efficiently calculate the costs for heterogeneous AGVs to complete tasks and assign heterogeneous AGVs suitable tasks in real time. Inspired by this, in this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task Planning for Heterogeneous AGVs (TPHA)</i> problem, in which the warehouse assigns tasks to suitable heterogeneous AGVs to minimize the total cost containing the travel cost and the tardiness cost. To solve the TPHA problem, we propose a novel framework that considers the route planning and task assignment simultaneously. Within this framework, we first present an efficient cost computation algorithm to calculate costs between tasks and heterogeneous AGVs. Then, we present two task assignment algorithms to assign tasks to suitable heterogeneous AGVs. Finally, we propose an improved route planning algorithm for further improving the processing time and reducing the total cost. Compared with state-of-the-art, extensive experiments on both real and synthesized datasets examine that our proposed algorithms can save 91.83% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 97.19% of processing time and reduce 29.08% of the total cost at most.