Table 4 Overview of challenges in port operation area
Title of challenge (Area) | Description | Source |
|---|---|---|
Optimizing ship stowage planning (Waterside) | Finding containers' optimal position on the ship Increasing economic and safety impact Optimizing cranes' planning using AI | Shen et al. (2017) |
Reducing sea going vessel delays (Waterside) | Not sailing in a predetermined time window Using infrastructure capacity better Using AI by including weather and route conditions in ETA | Parolas (2016) |
Predicting of inland vessel ETA (Waterside) | Estimating arrival time of inland vessel Planning infrastructure better Using AI instead of manually data entering in AIS | Meijer (2017) |
Optimizing ship queuing (Waterside) | Planning the sequence of loading/unloading of ships Reducing ship waiting time Finding the required numbers of infrastructure at the berth by using AI | |
Centralizing berth allocation (Waterside) | Assigning vessel to berth for loading/unloading Reducing turnaround time Simulating by including the planning of vessels and the number of berths | Leon et al. (2017) |
Optimizing quay Crane (QC) assignment (Waterside) | Assigning vessel to QC for loading/unloading Enhancing handling capacity Planning time of the QC by using AI | Atak et al. (2021) |
Detecting ship and ships traffic (Waterside) | Measuring and monitoring a ship's activity Increasing economic and maritime impact Identifying and classifying ships by AI | Song et al. (2020) |
Reducing vessel turnaround time (Waterside) | Reducing the time from arrival to departure of the vessel Increasing customer satisfaction and attracting more vessels Addressing vessel scheduling by using AI | Stepec et al. (2020) |
Predicting the risk range of ship's berthing velocity (Waterside) | Controlling the vessel's speed during mooring Reducing the occurrence of damage to the berth equipment or the hull Finding safe speed | Lee et al. (2020) |
Reducing vessel waiting time (Waterside) | Reducing the time between arrival and load/unloading Increasing advantages of container terminals and attracting more vessels Analyzing berthing time based on demand and capacity | |
Predicting loading and unloading container demand (Waterside) | Preparing guidelines of requirements at the quayside Optimizing the loading and unloading of vessels Predicting by Artificial Neural Network (ANN) | Yang and Chang (2020) |
Lowering emissions in shipping (Waterside) | Reducing emission Reducing air pollution Reducing wasted time and developing process | EI Mekkaoui et al. (2020) |
Optimizing yard truck routing (Landside) | Determining the optimal route for transporting containers between the yard and the quayside Enhancing the capacity of the yard and quayside Assigning a truck to a specific quay crane | Stojaković and Twrdy (2021) |
Optimizing of yard truck scheduling (Landside) | Controlling delay and waste of time Reducing congestion and waiting time of yard trucks Simulating trucks at the yard | Wang et al. (2015) |
Predicting container relocation (Landside) | Obtaining a sequence of containers moves Reducing required space to retrieve containers Retrieving container by including destination and departure time | Zhang et al. (2020) |
Optimizing scheduling of yard crane (Landside) | Scheduling YC to reduce the sum of job waiting times Facilitating yard operations Scheduling YC by including space and workloads | Sharif et al. (2012) |
Generating optimal yard block allocation (Landside) | Allocating of required space for container storage Reducing space limitations Considering containers information for assigning block | Kim and Park (2003) |
Reducing congestion at terminals' gates (Hinterland) | Generating inefficiency and costs in the hinterland Optimizing the pattern of truck arrival Employing AI technologies for upgrading equipment | Alagesan (2017) |
Predicting unforeseen trucks delays (Hinterland) | Affecting port's customer satisfaction index Gaining benefits from the prediction of trucks' arrival time Pre- collecting info on containers that are transported by road | Azab and Eltawil (2016) |
Optimizing truck queuing at gate (Hinterland) | Reducing long waiting times for trucks in queues Reducing freight costs Using data analysis for prioritizing gate-ins | Jin et al. (2021) |
Complex scheduling of rail mounted gantry crane (Hinterland) | Mapping of loading and unloading, as well as cargo storage tasks Scheduling by including complexity and workload | Wang and Zhu (2019) |
Reducing truck and train waiting time excess (Hinterland) | Reducing the unproductive time between arrival and load/unloading Reducing undesirable impact on the operation of other companies | Jin et al. (2021) |
Integrating individual appointment systems (Hinterland) | Appointment systems determine the arrival times based on internal capacity It contributes to reducing traffic and congestion | Ramadhan and Wasesa (2020) |
Reducing truck and train turnaround time (Hinterland) | Optimizing the total time spent in the terminal Reducing the number of resources Increasing economic and reducing environmental impact | Karam et al. (2019) |
Recognizing assets like containers, truck or vessels (All Port area) | Monitoring and positioning of the container Preventing inefficient movement | Mi et al. (2019) |
Registering container damage (All Port area) | Registering and centralizing container damage Reducing waste time to filling assertion | Panchapakesan et al. (2018) |
Predicting container demand (All Port area) | Predicting the precise number of containers entering the yard Enhancing the efficiency of container terminal | Yang and Chang (2020) |
Reducing container dwell time (All Port area) | Reducing the amount of time that a container spends within a port Enhancing throughput Planning by including space limitation and departure time | Heilig et al. (2020) |
Reducing emission and noise (All Port area) | Reducing prolong queues Reducing traffic congestion Reducing waiting and turnaround time | Xiaoju et al. (2013) |
Predicting fuel and energy consumption (All Port area) | Preventing global warming and energy shortages Reducing emission Predicting consumption of renewable energy by AI | Kim and Kim (2018) |