| Summary: | In fulfillment centers, efficient inbound transportation and goods storage are crucial factors that impact overall performance and supply chain costs. Traditional processes often involve human workers performing repetitive tasks, leading to increased expenses. This study presents a Manned-Unmanned Teaming (MUM-T) approach that combines unmanned Automated Guided Vehicles (AGVs) with manned forklift vehicles to automate these processes and minimize costs. The primary objectives are to model AGV-based unmanned inbound transportation, design a manned traveling forklift problem (TFP) with a shortest path algorithm, and compare the MUM-T approach to traditional methods in terms of distance, time, and cost. Results from theoretical analysis and simulations show that the MUM-T approach can reduce traveling distance, working hours, and operational costs by up to 32%, 38%, and 51%, respectively. Moreover, the proposed algorithm enables Beginner and Intermediate-level forklift operators to achieve efficiency comparable to that of Professionals. These findings indicate that implementing the MUM-T approach can significantly enhance the efficiency and cost-effectiveness of inbound transportation and forklift processes in fulfillment centers.
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