Matheuristic Algorithm for Automated Guided Vehicle (AGV) Assisted Intelligent Order Picking

Abstract

Warehouse management plays an important role in the supply chain. It has received attention from academics and the industry for many years. During this time warehouse management processes have evolved like all other systems in the industry. Preparing orders faster has a key role for companies with an increasing demand from online shopping. The requirements for rapid response to customers have increased. Meanwhile the use of automated guided vehicles (AGVs) has increased because they are used to collect items by pickers in warehouses to prepare customer orders. Assignments of orders to AGVs and routing of AGVs should be done quickly and efficiently to prepare orders. Due to the NP-hardness of the problem it is difficult to solve realistic-size problems in an acceptable amount of time especially when orders from online shopping flow at a fast pace. In this study a new matheuristic algorithm was proposed to minimize the total traveled distance by all AGVs to collect orders with a minimum number of AGVs. This helps to reach a more sustainable system. Minimizing the distance of all AGVs helps for rapid responses to customers’ orders with minimum total energy/cost. Experimental results were provided to compare the computational complexities and performances of the models in this paper. The proposed novel matheuristic algorithm was shown to create intelligent order picking operations in warehouses within a reasonable computational time. © 2023 Elsevier B.V. All rights reserved.