Multi Objective Optimization for Intelligent Scheduling and Routing of Automated Guided Vehicle (AGV) Assisted Order Picking

Abstract

This study addresses the complexities of efficient warehouse management and order picking crucial for modern supply chain success. It focuses on the implementation of Automated Guided Vehicles (AGVs) to enhance order picking processes involving selecting and retrieving items to fulfill customer orders. Challenges such as suboptimal picking strategies poor layout and lack of realtime visibility leads to inefficiencies. The research introduces a multi-objective mathematical model and a novel clustering algorithm aimed at optimizing AGV-assisted order picking by balancing the minimization of travel distance and AGV energy consumption against the investment cost of purchasing new AGVs. The model seeks to efficiently assign orders to AGVs and determine optimal routing to reduce total travel distance addressing the trade-off between operational efficiency and investment costs. The results obtained from executing the experimental design demonstrate that the proposed algorithm can solve most instances more quickly while the mathematical model is particularly effective in reducing travel distances.