Bridging the Gap in Warehouse Automation: A Comparative Analysis of Autonomous Case-handling Mobile Robots (ACMRs), AMRs, and AGVs

With automation in the warehouse getting increasingly common, much of the literature focuses either on Automated Guided Vehicles (AGV) or Autonomous Mobile Robot (AMR), leaving quite a gaping hole in the body of literature related to Autonomous case-handling mobile robot (ACMR). The ACMR offers the flexibility of AMRs but adds the precision of case handling and, therefore, is especially fit for the demands of addressing the challenges of high-throughput environments: dynamic layouts, congestion, and the demands put on rapid, accurate order fulfillment. However, most of their potentials are yet unexplored, especially in cost efficiency, scalability, and operation performance. This thesis tends to fill in that gap by presenting, in the IBM CPLEX optimization tool, a mathematical model for the optimum deployment of ACMR at minimum operational cost with maximum throughput. Further scaling of the model is covered by using realistic scaled simulation friendly empirical data obtained by leading robot manufacturers such as Hai Robotics, Invia, and Quicktron. The model will be further validated through simulations, which can allow ACMR to benchmark with AGVs and AMRs based on key indicators such as cost efficiency, distance travelled, and speed of completion of order. In the light of the above, the present study evidences the benefits of ACMR in trading up the deficiencies of the traditional automation technologies. The results will imply practices to be followed by the warehouse operators and will form a basis for further academic investigation with regard to ACMR technologies, their integration, and their transformational potential within automated warehousing.