Table 13 Challenges preferences list details

Recognizing assets like containers, truck or vessels: according to the AI solutions' list of preferences, this challenge cannot address by AI solutions in this study. This challenge does not belong to the list of preferences of any AI solutions. Therefore, it will not be considered in the matching algorithm process

Registering container damage: this challenge is not concerned with any AI solution provided in this study. AI solutions did not rank this challenge in their preferences list. Hence, if this challenge is involved in the matching algorithm, it will not match any AI solutions. It is better to remove it from the algorithm process

Reducing sea going vessel delays: this challenge has been ranked by "Lock optimization" solution, then it is obvious there is no issue with ranking AI solutions in the list of preferences of this challenge. This list only has one member, and it is "Lock optimization"

Optimizing ship queuing: according to the preferences list of AI solutions. This challenge exists only in one list, which belongs to the "Resource allocation" solution. Therefore, the list of preferences for this challenge includes this solution

Reducing vessel waiting time: "Lock optimization" accommodated this challenge in its preferences list. Hence, this challenge can be solved by this solution and not with the rest of them. The list of preferences associated with this challenge only includes "Lock optimization"

Predicting loading and unloading container demand: " demand prediction " can only solve this challenge among all the AI solutions presented in this study. Thus, ranking AI solutions for this challenge is not difficult because its list of preferences solely contains "Demand prediction"

Optimizing of yard truck scheduling: list of preferences of this challenge comprising "Truck guidance system" AI solution. Undoubtedly only this solution ranked this challenge in its preferences list

Optimizing scheduling of yard crane: AI solutions' list of preferences indicates "Truck guidance system" can tackle this challenge. The rest of the AI solutions did not consider this challenge in their preferences list. Drawn on this, the list of preferences for this challenge consists of the "Truck guidance system"

Optimizing truck queuing at gate: preferences list of this challenge only encompasses one AI solution: "Resource allocation." Because among all the AI solutions, only this solution ranked this challenge in its preferences list

Complex scheduling of rail mounted gantry crane: "Truck guidance system" AI solution corresponds to this challenge. The rest of the AI solutions can not address this challenge. Therefore, the preferences list for this challenge is foreseeable to include only this solution

Reducing truck and train waiting time excess: among all the AI solutions in this study, only "Lock optimization" complies with this challenge, and this solution is involved in this challenge's preferences list

Integrating individual appointment systems: amid AI solutions in this research, only the "Truck guidance system" can tackle this challenge. Subsequently, this AI solution is a lonely member of this challenge's preferences list

Predicting container demand: "Demand prediction" is the only AI solution that can send a proposal to this challenge. Thus, the list of preferences associated with this challenge solely contains this solution

Centralizing berth allocation: The title of this challenge is self-explanatory. This challenge is about the assignment berth to vessels. "Resource allocation" and "Booking of slots" have accommodated this challenge in their preferences list. It means they can address this challenge. "Resource allocation" concerns gate allocation in the warehouse, and "Booking of slots" works on optimizing the matching of free slots in container terminals. The above explanations of AI solutions suffice to distinguish these AI solutions. Therefore, "Resource allocation" corresponds to this challenge more than "Booking of slots." Drawn on this result, the list of preferences for this challenge is as follows:

Centralizing berth allocation LP: Resource allocation, Booking of slots

Optimizing quay Crane (QC) assignment: This challenge involves two AI solutions' preferences list. These AI solutions are "Resource allocation" and "Booking of slots." Both of these AI solutions rely on assignment problems, but either of them has different goals. "Resource allocation" effort to reduce distance and "Booking of slots" can make faster handling time. The most significant reason for optimizing QC is to reduce idle time and handle more containers with them. Therefore, "Booking of slots" can be placed at the first spot in the list of preferences for this challenge. The preferences list for this challenge is presented below

Optimizing quay Crane (QC) assignment LP: Booking of slots, Resource allocation

Detecting ship and ships traffic: this challenge encompasses measuring and monitoring the ship's activity. Two solutions can solve this challenge among all AI solutions, so they have put this challenge in their preferences list. These AI solutions are "Smart waterway" and "Detection of fouling using AI". According to this challenge, "Smart waterway" is a better choice. "Smart waterway" was developed to detect obstacles in voyages with affordable sensors, but "Detection of fouling using AI" only detects fouling on the ship's hull and propeller. Therefore, a list of preferences for this challenge is provided as follows:

Detecting ship and ships traffic LP: Smart waterway, Detection of fouling using AI

Reducing vessel turnaround time: the time from arrival to departure of the vessel is the turnaround time. There are many measures to reduce it, but in this study, only two AI solutions can tackle it. "Optimizing maintenance scheduling" and "Boat landing" have ranked these challenges in their preferences list. Both of these solutions have the same objective: "Predicting accessibility for offshore assets taking context, weather, vessel, routes," but "Boat landing" is more related to this challenge. "Boat landing" can consider all needs to reduce the turnaround time, but "Optimizing maintenance scheduling" only concentrates on repair planning. Based on this result, the preferences list for this challenge figures out below

Reducing vessel turnaround time LP: Boat landing, Optimizing maintenance scheduling

Optimizing yard truck routing: this challenge concerned determining the optimal route for transporting containers between the yard and quayside. Two AI solutions are related to this challenge. "Truck guidance system" and "Optimizing shelf placement" have considered this challenge in their preferences list. Regarding their objectives, "Truck guidance system" is more practical to tackle this challenge than "Optimizing shelf placement." "Truck guidance system" optimizes route directly but "Optimizing shelf placement" decides to "where is the right place for goods" and as a result, the milage which needs to take will reduce. Hence, the "Truck guidance system" was placed at the first spot in the preferences list of this challenge

Optimizing yard truck routing LP: Truck guidance system, Optimizing shelf placement

Reducing truck and train turnaround time: the time from arrival to departure of the truck and train is the turnaround time. "Optimizing maintenance scheduling" and "Boat landing" have been ranked this challenge in their list of preferences like "Reducing vessels turnaround time." Both of these challenges have the same objective, then all the reason for ranking AI solutions is like the reasons which figure out for "Reducing vessels turnaround time." Therefore, the preferences list for this challenge is like "Reducing vessels turnaround time"

Reducing truck and train turnaround time LP: Boat landing, Optimizing maintenance scheduling

Reducing container dwell time: this challenge is about reducing the time a container spends within a port. If the "Optimizing maintenance scheduling" and "Boat landing" tackle challenges associated with the turnaround time of vessels, trucks, and trains, this challenge will also be fixed. Therefore, the AI solutions which can address this challenge are like "Reducing turnaround time," and all the reasons for ranking these solutions are the same. Hereby the list of preferences for this challenge is presented below

Reducing container dwell time LP: Boat landing, Optimizing maintenance scheduling

Optimizing ship stowage planning: Containers' optimal position on a ship is called ship stowage planning. This challenge is one of the members of the preferences list of three AI solutions which are "Optimizing shelf placement," "Resource-efficient AI," and "Booking of slots." Among these solutions, "Booking of slots" is the most related solution to this challenge because it is exactly concerned with optimizing the allocation of the free slots to containers. "Optimizing shelf placement" and "Resource-efficient AI" work on warehousing goods, but among these solutions, "Optimizing shelf placement" is the better choice to solve this challenge. The main objective of "Resource-efficient AI" is developing warehousing robots, but "Optimizing shelf placement" relies on assigning shelves to goods in a warehouse. Eventually, the preferences list for this challenge is presented as follows:

Optimizing ship stowage planning LP: Booking of slots, Optimizing shelf placement, Resource efficient AI

Predicting the risk range of ship's berthing velocity: The vessel's speed may go high during mooring, and there is the possibility of damaging the berth equipment or the hull. Therefore, the simulation approach maybe can find a safe speed. Three AI solutions have been considered for this challenge in their list of preferences. Among them, "Large scale simulation" is the most match solution. "Smart waterway" and "Boat landing" must also rank here. Both AI solutions rely on predicting some data relevant to vessels, but "Smart waterway" because of exploiting affordable sensors and predicting data like obstacles is better than "Boat landing." "Boat landing" works on predicting the accessibility of vessels based on route and weather. Finally, the preferences list for this challenge is provided as follows:

Predicting the risk range of ship's berthing velocity LP: Large scale simulation, Smart waterway, Boat landing

Generating optimal yard block allocation: This challenge is about allocating the required space for container storage. This challenge is one of the members of the preferences list of three AI solutions which are "Optimizing shelf placement," "Resource-efficient AI," and "Booking of slots." Among these solutions, "Booking of slots" is the most related solution to this challenge because it is exactly concerned with optimizing the allocation of the slots to containers. "Optimizing shelf placement" and "Resource-efficient AI" are concerned with warehousing goods, but among these solutions, "Optimizing shelf placement" is the better one to solve this challenge. The primary objective of "Resource-efficient AI" is to develop warehousing robots, but "Optimizing shelf placement" is assigning shelves to warehouse goods. Lastly, the preferences list for this challenge is as follows:

Generating optimal yard block allocation LP: Booking of slots, Optimizing shelf placement, Resource efficient AI

Predicting unforeseen trucks delays: This particular challenge is one of the members of the list of preferences for "Predictive planning," "Lock optimization," and "Large scale simulation" solutions. Among these solutions, the best one to tackle this challenge is "Predictive planning." "Predictive planning" forecasts traffic conditions for trucks to optimize the planning. The second choice for solving this challenge is "Lock optimization" because it predicts the ETA of the vessel. Lastly, "Large scale simulation" can address this challenge by simulation power that exists in this solution. This challenge's preferences list figure out below

Predicting unforeseen trucks delays LP: Predictive planning, Lock optimization, Large scale simulation

Predicting fuel and energy consumption: "Demand prediction," "Developing applied building photovoltaics," and "Predictive planning" ranked this challenge in their preferences list. The most appropriate solution for tackling this challenge is "Demand prediction" because consumption is the same as demand. In the end, demand consumes by the user. The second choice for this challenge is "Developing applied building photovoltaics." "Developing applied building photovoltaics" improves the energy yield prediction of solar panels, then maybe it can solve this challenge too. Finally, "Predictive planning" Optimizes statistical forecasting, but it does this in a general situation, but the previous solution at least predicts some energy-related data. The list of presences for this challenge is provided below

Predicting fuel and energy consumption LP: Demand prediction, Developing applied building photovoltaics, Predictive planning

Lowering emissions in shipping: Four AI solutions can solve this challenge based on their power to reduce emissions. The first alternative is "Detection of fouling using AI." This solution can reduce the ship's fuel consumption, and as a consequence, the emission will reduce too. The second choice is "Electric vehicle charging." This solution also can reduce emissions, but not emissions associated with ships. The third one is "Developing applied building photovoltaics," which can reduce emissions using solar panels. However, using solar panels is not so custom on the ship. Finally, the "Smart waterway" by optimizing the path for ships may reduce emissions. The list of preferences for this challenge is determined based on the above reasons

Lowering emissions in shipping LP: Detection of fouling using AI, Electric vehicle charging, Developing applied building photovoltaics, Smart waterway

Predicting container relocation: This challenge has been accommodated in the list of preferences of four AI solutions. The most related AI solution among all of them is "Large scale simulation." This solution, by simulation, can obtain a sequence of container moves. The second related solution is "Booking of slots." This solution can find the best slot for the container so it knows about the relocation of the containers before. The third one is "Optimizing shelf placement." This solution also is related to this challenge but not exactly for relocating containers in slots. Lastly, "Resource-efficient AI" with warehousing knowledge can tackle this problem, but the first objective of this solution is developing warehousing robots. The list of preferences for this challenge is prepared as follows:

Predicting container relocation LP: Large scale simulation, Booking of slots, Optimizing shelf placement, Resource efficient AI

Reducing congestion at terminals' gates: Four AI solutions have considered this challenge. These AI solutions are "Predictive planning," "Truck guidance system," "Resource allocation," and "Booking of slots." The best choice for addressing this challenge is "Predictive planning" because it forecasts truck traffic conditions to optimize the planning. The second alternative is the "Truck guidance system" because it optimizes logistic flows when global information is available. It does not mention traffic, but logistics flow could be related to traffic. The third one is "Resource allocation" because it can optimize the allocation of gates to trucks. Hence, the traffic can be reduced as a result. Eventually, "Booking of slots" can also optimize the gate's allocation to the truck, but it has been employed for allocating slots to containers. The list of preferences for this challenge is as follows:

Reducing congestion at terminals' gates LP: Predictive planning, Truck guidance system, Resource allocation, Booking of slots

Predicting of inland vessel ETA: Five AI solutions have ranked this challenge in their preferences list. These solutions are "Lock optimization," "Boat landing," "Optimizing maintenance scheduling," "Large scale simulation," and "Predictive planning." The best option for addressing this challenge is "Lock optimization." It is employed for the prediction of ETA and accurate vessel positioning. "Boat landing," "Optimizing maintenance scheduling," predict accessibility for offshore assets taking context, weather, vessel, and routes, but "Boat landing" is more related to this challenge because it concentrates on landing and "Optimizing maintenance scheduling," relies on repair planning of vessels. "Large scale simulation" is more related than "Predictive planning" to this challenge because it can find out accurate ETA by simulation, but "Predictive planning" optimizes statistical forecasting and does not mention exact to ETA. The preferences list of current challenges is presented below

Predicting of inland vessel ETA LP: Lock optimization, Boat landing, Optimizing maintenance scheduling, Large scale simulation, Predictive planning

Reduction of emission and noise: Six AI solutions have ranked this challenge. These are "Electric vehicle charging," "Control flexibility in industrial processes," "Resource-efficient AI," "Data-driven control," "Truck guidance system," and "Detection of fouling using AI." Among all these solutions, "Electric vehicle charging" is the best option to address this challenge. That reduces the port's carbon emissions because electric vehicles exist everywhere. The second one is "Control flexibility in industrial processes." That can control the emission as the first objective, then reduce CO2 emission afterward. "Resource-efficient AI," by Designing computational power- and resource-efficient autonomous robotic platforms for an industrial warehouse, can help to reduce emissions. Hence, it can be ranked in third place. The next place belongs to "Data-driven control" because it only reduces emissions related to chemical plants. One of the "Truck guidance system" objectives is to optimize logistics flow, which can cause to reduce emissions. The last choice is "Detection of fouling using AI" because it only reduces emissions related to ships. Finally, the list of preferences for this challenge is presented based on the above reasons

Reduction of emission and noise LP: Electric vehicle charging, Control flexibility in industrial processes, Resource-efficient AI, Data-driven control, Truck guidance system, Detection of fouling using AI