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A handshake operation can mitigate workload imbalance and interference between twin transporters in a material handling system. Terminal operators in a rail-based automated container terminal can employ the handshake operation to twin overhead shuttle cranes (OSs) under maritime demand uncertainty. Since a handshake location is critical to collaboration performance, terminal operators often rely on simulation experiments with a manual iterative design to determine optimal handshake locations. However, the simulation optimization is still challenging when a simulation execution is computationally expensive. This study proposes a Bayesian optimization-based approach to expedite the decision-making process. The approach infers the conditional outcomes of a simulation and actively searches optimal handshake locations. Our optimization results show that the proposed approach maximizes the collaborations between the twin OSs within fewer simulation runs. This study also provides extensive simulation analysis of the handshake locations. The experiments indicate that a handshake location has a significant influence on the required space for handshake operations and the workloads of the twin OSs.

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Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-based Automated Container Terminal under Demand Uncertainty