Table 12 AI solutions preferences list details

Smart waterway: this solution concerned autonomous barges in urban waterways with low-cost sensor systems, enabling a shift from road to water for sustainable last-mile logistics. The ship detection challenge might be the best match for this solution because one of the technologies used for this solution is computer vision and sensor fusion algorithms to detect and localize obstacles in the waterway. This technology can throw this challenge

This solution has been designed for affordable sensors. Although this sensor is employed for navigation and localization, this solution might be useful for predicting a ship's berthing risk range velocity. The goal of low-carbon shipping is reducing air pollution. On the other hand, this solution by researching autonomous barges causes to reduce pollution. Hence, there is a weak connection between them. Hereby the preferences list for this solution is as follows:

Smart waterway LP: Detecting ship and ships traffic, Predicting the risk range of ship's berthing velocity, Lowering emissions in shipping

Demand prediction: this solution indicates the demand for fresh food by prediction methods. The challenge of container demand prediction is the best match with this solution because an approach employed for predicting demand for fresh food must be appropriate for this challenge. Also, this solution can predict load and unload containers' demand. However, this challenge is after container demand prediction because it needs to concentrate on the waterside operation area and is not general like container demand. The prediction approach designed in this solution is related to demand. The amount of consumption is related to the demand, and then this approach can help to predict fuel and energy consumption as well. According to the fact relevant to this solution, the list of references is provided below

Demand prediction LP: Predicting container demand, Predicting loading and unloading container demand, Predicting fuel and energy consumption

Resource allocation: this solution assigns trucks to the optimal gate in a distribution center. Therefore, berth allocation to vessels has the most connected with this solution. Assigning quay cranes to load or unload vessels is based on the task assignment problem, which is unrelated to this solution as much as the berth allocation problem. One measure that can reduce congestion at the gate is optimizing the assignment of trucks to the gate. Hence, this solution can help reduce congestion at the gate. Also, assigning trucks to the gate is one part of the queuing algorithm. Thus this solution might help optimize truck queuing at the gate. Although this solution is employed for assigning trucks to the gate, it might solve ship queuing like truck queuing at the berth. Finally, the preferences list of this solution is presented based on the above reasons

Resource allocation LP: Centralizing berth allocation, Optimizing quay Crane (QC) assignment, Reducing congestion at terminals' gates, Optimizing truck queuing at gate, Optimizing ship queuing

Optimizing shelf placement: this solution finds the optimal place to put goods on a shelf in a distribution center. Hence, the personnel can take the least amount of kilometers per day. Therefore, yard block allocation and ship stowage planning are the best matches. The reason for yard block allocation is more related to this solution's goal than the reason for ship stowage planning. The container terminal will prepare ship stowage planning because of safety and security

After ship stowage planning, the most appropriate problem this solution can solve is predicting container relocation because allocating blocks to the container can help predict the future movement of containers. One of the goals for tackling yard truck routing is to reduce the number of kilometers that trucks take. Therefore, this solution can address the yard truck routing problem partly. Lastly, the list concerned preferences of this solution is as follows:

Optimizing shelf placement LP: Generating optimal yard block allocation, Optimizing ship stowage planning, Predicting container relocation, Optimizing yard truck routing

Resource efficient AI: this solution creates a computational power- and resource-efficient autonomous robotic platform for an industrial warehouse setting that can scale to multiple interacting agents. Therefore, at first, this solution leads to reduce emissions. Also, according to the warehousing knowledge in this solution, the challenges related to allocating containers to block and relocate them in the yard or vessels might be solved by this solution. This solution's challenges related to warehousing have been ranked like the previous solution. At last, the list of preferences indicates the ranking associate challenges for this solution

Resource efficient AI LP: Reduction of emission and noise, Generating optimal yard block allocation, Optimizing ship stowage planning, Predicting container relocation

Data driven control: this solution is an innovation project of catalysts, the spearhead cluster that facilitates innovation in the Flemish chemistry and plastics sectors. These methods specifically could be of value in process and system control. Process control can reduce emissions. Hence, this solution only can solve one challenge. The list of preferences for this solution is as follows: Data driven control LP: Reduction of emission and noise

Lock optimization Novimove: this solution is about end-to-end navigation and scheduling for inland waterway transport. Thus, this solution is the most appropriate to predict the ETA of an inland vessel. After that, sea-going vessels delay can be reduced through this solution. Also, this solution leads to reduced waiting times at bridges, locks, and docks while creating smoother sailing. This solution can address challenges like the above but in the hinterland. It can reduce truck delays and truck waiting time. The list of preferences for this solution is as follows:

Lock optimization Novimove LP: Predicting of inland vessel ETA, Reducing sea going vessel delays, Reducing vessel waiting time, Predicting unforeseen trucks delays, Reducing truck and train waiting time excess

Large scale simulation: this solution has been implemented for larger-scale simulations. Other simulation frameworks cannot simulate large-scale experiments in enough detail with an acceptable runtime. This simulation scheme was developed to be applied in traffic cases and could tackle future data prediction challenges. Therefore, the most related challenge with this solution is predicting truck delay. This solution also can address predicting ETA based on the power of the simulation method. This simulation can solve the prediction of container relocation and berthing risk range. However, "prediction of container relocation" is more related to the traffic concept than "prediction of berthing risk range", which is located higher in the preferences list

Large scale simulation LP: Predicting unforeseen trucks delays, Predicting of inland vessel ETA, Predicting container relocation, Predicting the risk range of ship's berthing velocity

Control flexibility in industrial processes: this solution combines model predictive control and deep learning techniques. Also, it considers the characteristics of energy-intensive industrial processes. Thus, the best match for this solution is the challenge of fuel and energy consumption prediction

Developing applied building photovoltaics: this solution develops tools to improve the energy yield prediction of solar panels, guidelines to maximize performance and reliability, and techniques for predictive maintenance. Therefore, according to the use of solar panels in vessels, this solution can reduce carbon emissions by vessels. Besides, if solar panels are energy sources, this solution can also predict energy consumption. However, this is not common, so the challenge of "predicting energy consumption" is ranked second in this solution's list of preferences

Developing applied building photovoltaics LP: Lowering emissions in shipping, Predicting fuel and energy consumption

Electric vehicle charging: this solution control strategies for the smart charging of electric vehicles. Also, their goals are to shift part of energy consumption towards more appropriate moments of the day or week, increasing the consumption of locally produced renewable energy, reducing peak loads, and avoiding grid congestion

Since the equipment in the terminal often uses electricity fuel thus, this solution can help reduce energy consumption, and as a result, this solution can reduce emissions. Also this measure can perform for vessels as well. However, this can occur for some engines in vessels, not all parts of vessels. Therefore, the challenge of "Lowering emissions in shipping" accommodates the second rank in this solution's list of preferences

Electric vehicle charging LP: Reduction of emission and noise, Lowering emissions in shipping

Truck guidance system: this solution's first goal is optimizing the truck guidance system, and the second one is optimizing logistic flows when global information is available. Therefore, the most relevant challenge to this solution is the routing of trucks in the yard. After this challenge scheduling of trucks is most related to this solution. If one optimizes the routing of the truck, then the traffic congestion will reduce

The second goal of this solution is to address challenges like Integrating individual appointment systems and scheduling cranes in the yard and hinterland, respectively. The appointment system impacts all logistics flow, so it accommodates right before scheduling cranes. Also, the yard crane is more involved in logistics than the rail-mounted gantry crane. Because the yard crane handles all containers, the rail-mounted gantry crane only handles containers moved by rail mode. Finally, by solving problems like the above, this solution can also reduce emissions. According to the above reasons list of preferences for this solution is provided as follows:

Truck guidance system LP: Optimizing yard truck routing, Optimizing of yard truck scheduling, Reducing congestion at terminals' gates, Integrating individual appointment systems, Optimizing scheduling of yard crane, Complex scheduling of rail mounted gantry crane, Reduction of emission and noise

Predictive planning: This solution is based on "optimizing statistical forecasting" and "forecasting traffic conditions for trucks to optimize the planning." This solution can predict data related to traffic and cause traffic reduction. Hence, the most relevant challenge to this solution is "Reducing congestion at terminals' gates". After that, the most related challenge is predicting delays that lead to traffic. As this solution is related to trucks, the challenge of "Predicting unforeseen truck delays" ranks second—also, the challenge of "Predicting of inland vessel ETA" rank as a third challenge. Lastly, as this solution is related to predicting data for reducing traffic, the prediction of the amount of energy and fuel that vehicles or vessels need can be addressed by this solution. The list of preferences for this solution is presented below

Predictive planning LP: Reducing congestion at terminals' gates, Predicting unforeseen trucks delays, Predicting of inland vessel ETA, Predicting fuel and energy consumption

Booking of slots: this solution relies on optimizing matching free slots in container terminals and making faster handling time. Hence, yard block allocation and ship stowage planning are the most appropriate challenges that can be solved by this solution, respectively. The yard block allocation is more related to this solution than ship stowage planning because the container terminal will prepare ship stowage planning because of safety and security aspects that do not make faster handling time. After ship stowage planning, the most related problem to this solution is predicting container relocation because allocating blocks to the container can help predict the future movement of containers. Although this solution is concerned with assigning slots, a challenge like "berth allocation" could be solved by this solution. Also, the challenges of assigning tasks like "Quay crane assignment" could be related to place assignment. Finally, it is obvious; if this solution optimizes the "allocation gate to the truck," traffic congestion will be reduced. Now, the list of preferences shows the rank of each challenge

Booking of slots LP: Generating optimal yard block allocation, Optimizing ship stowage planning, Predicting container relocation, Centralizing berth allocation, Optimizing quay Crane (QC) assignment, Reducing congestion at terminals' gates

Optimizing maintenance scheduling: this solution works on predicting accessibility for offshore assets taking context, weather, vessel, and routes. Also, it is related to anomaly detection, semantic stream reasoning, and rule mining for optimizing maintenance

The most related problem to this solution is "Predicting of inland vessel ETA" because this time's prediction involves all the contexts like weather, vessel, and routes. The other problem with this solution is reducing turnaround time by preventive asset maintenance. As this solution was developed for vessels, "Reducing vessel turnaround time" ranks higher than "Reducing truck and train turnaround time" in the list of preferences

Generally, this solution would address the "Reducing container dwell time" challenge in all the port's operation areas, so this solution also has a weak connection with this solution. The list of preferences for this solution testifies to all the above reasons

Optimizing maintenance scheduling LP: Predicting of inland vessel ETA, Reducing vessel turnaround time, Reducing truck and train turnaround time, Reducing container dwell time

Boat landing: This solution is exactly like the previous solution. Still, in the context of the boat landing, the most related challenge to this solution is "Predicting the risk range of the Ship's berthing velocity" Then, other challenges are like the previous solution, even rank of them in the preferences list

Boat landing LP: Predicting the risk range of ship's berthing velocity, Predicting of inland vessel ETA, Reducing vessel turnaround time, Reducing truck and train turnaround time, Reducing container dwell time

Detection of fouling using AI: the goals of this solution are modeling the performance of a ship, preventing extreme fouling on the hull and propeller of ship, and reducing fuel consumption. The first goal is more important than the second one, and the second one is more important than the last one. Therefore, the most relevant challenge for this solution is "Detecting ship and ship traffic". Also, challenges like "Lowering emissions in shipping" and "Reduction of emission and noise" are related to the third goal ranked respectively after "Detecting ship and ships traffic". The reason for ranking "Lowering emissions in shipping" higher than "Reduction of emission and noise" is the relation of the solution with the ship. The list of preferences provide for this solution is as follows:

Detection of fouling using AI LP: Detecting ship and ships traffic, Lowering emissions in shipping, Reduction of emission and noise