# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Infrastructure and Operation  
The shipping industry faces many interrelated challenges and opportunities as its role in the global trading system has become increasingly important over the past few decades. On the one hand, a collaboration between port terminals and shipping lines can reduce costs and achieve sustainable supply chains, and on the other hand, optimizing operations and voyage times can reduce bunker consumption and lower fuel costs and air emissions. Therefore, there is a growing need to address the consolidation opportunities and environmental challenges associated with container shipping through optimization. Container port operations can be classified into two categories: landslide operations and seaside operations. The landslide usually consists of a gate, yard, and quay, while the seaside consists of berth and channel anchorage areas. As shown in Figure 22, container ships require the assistance of tugboats. However, the availability of tugboats and the ability of each tug to serve a ship is limited. Therefore, for tugs to provide effective service to large ships, they must be effectively utilized. Figure 21: Policy network and value network configuration for reinforcement learning-based controller proposed by [PERSON] and [PERSON] [29]. Figure 22: Seaside and landslide operations in container ports [44]. A mixed-integer linear programming (MILP) method is developed to solve the tugboat scheduling problem by considering real-world problems such as tugboat flow balance constraints, ship arrival time constraints, sail time limits, tugboat arrival time constraints, service start time constraints, and ship-tugboat compatibility constraints and is applied to the tugboat scheduling problem (Tug-SP) to show efficient scheduling performance [44]. Figure 23 shows a more practical tugboat scheduling strategy, considering a proactive scheduling strategy that takes into account the expected variability and uncertainty during the execution of the tugboat operation schedule and a reactive scheduling strategy that appropriately adjusts the initial schedule to deal with unexpected scenarios with minimum recovery cost [45]. In addition to the tugboat scheduling problem, we can focus on the Berth Allocation Problem (BAP), which is a well-known optimization problem for assigning berth times and locations for ships. By considering the speed of ships and their arrival and departure times as decision variables in the problem, we extend the traditional BAP to obtain a better solution by using a higher level of discretization with more accurate speed variables. In addition, fuel and ship GreenHouse Gas (GHG) emissions can be reduced by approximately 40% when comparing the model with a situation where each ship is sailing at its design speed [46]. It also focuses on the Quay Crane Assignment Problem (QCAP). The Berth Allocation and Quay Crane Assignment Problem (BACAP), where each vessel is assigned a berth time and location for a given planning horizon, is solved by assigning cranes considering the marginal productivity loss due to crane interference and the processing time depending on the vessel's berth location [47]. In addition, research in the field of energy efficiency and greening for green ports is increasing, and operational strategies, technologies, and energy management systems for energy efficiency are being reviewed, and all methods, measures, and technologies are being reviewed, quantified, and compared. Through this comparative analysis, energy savings and emission reductions can be expected [49]. Finally, mooring is one of the most dangerous operations on ships and in ports. Currently, as shown in Figure 24, there are various innovations in mooring systems, such as automatic vacuum mooring systems, magnetic mooring systems, and berthing assistance systems.