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Table 7 Challenges' preferences list

From: Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches

Title of challenges

Code of challenges

List of preferences

Optimizing ship stowage planning

C1

S14, S4, S5

Reducing sea going vessel delays

C2

S7

Predicting of inland vessel ETA

C3

S7, S16, S15, S8, S13

Optimizing ship queuing

C4

S3

Centralizing berth allocation

C5

S3, S14

Optimizing quay Crane (QC) assignment

C6

S14, S3

Detecting ship and ships traffic

C7

S1, S17

Reducing vessel turnaround time

C8

S16, S15

Predicting the risk range of ship's berthing velocity

C9

S8, S1, S16

Reducing vessel waiting time

C10

S7

Predicting loading and unloading container demand

C11

S2

Lowering emissions in shipping

C12

S11, S17, S10, S1

Optimizing yard truck routing

C13

S12, S4

Optimizing of yard truck scheduling

C14

S12

Predicting container relocation

C15

S8, S14, S4, S5

Optimizing scheduling of yard crane

C16

S12

Generating optimal yard block allocation

C17

S14, S4, S5

Reducing congestion at terminals' gates

C18

S13, S12, S5, S14

Predicting unforeseen trucks delays

C19

S13, S7, S8

Optimizing truck queuing at gate

C20

S3

Complex scheduling of rail mounted gantry crane

C21

S12

Reducing truck and train waiting time excess

C22

S7

Integrating individual appointment systems

C23

S12

Reducing truck and train turnaround time

C24

S16, S15

Recognizing assets like containers, truck or vessels

C25

 

Registering container damage

C26

 

Predicting container demand

C27

S2

Reducing container dwell time

C28

S16, S15

Reduction of emission and noise

C29

S11, S9, S5, S6, S12, S17

Predicting fuel and energy consumption

C30

S2, S10, S13