TY - JOUR
T1 - Scheduling of container-handling equipment during the loading process at an automated container terminal
AU - Luo, Jiabin
AU - Wu, Yue
N1 - © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2020/11/1
Y1 - 2020/11/1
N2 - To improve the operational efficiency of container terminals, it is important to consider the coordination of different types of container-handling equipment, which typically include vehicles, yard cranes and quay cranes. This paper addresses the integration of scheduling each constituent of handling equipment in an automated container terminal, in order to minimise the loading element of the ship’s berthing time. A mixed-integer programming (MIP) model was developed to mathematically formulate this challenge. Small-sized problems can be solved optimally using existing solver. In order to obtain approximately optimal solutions for large-sized problems, an adaptive heuristic algorithm was created that can adjust the parameters of a genetic algorithm (GA), according to the observed performance. Experiments were carried out for both small-sized and large-sized problems to analyse the impact of equipment used in the loading process on berthing and computation times, as well as to test the efficiency of our proposed adaptive GA in solving this integrated problem.
AB - To improve the operational efficiency of container terminals, it is important to consider the coordination of different types of container-handling equipment, which typically include vehicles, yard cranes and quay cranes. This paper addresses the integration of scheduling each constituent of handling equipment in an automated container terminal, in order to minimise the loading element of the ship’s berthing time. A mixed-integer programming (MIP) model was developed to mathematically formulate this challenge. Small-sized problems can be solved optimally using existing solver. In order to obtain approximately optimal solutions for large-sized problems, an adaptive heuristic algorithm was created that can adjust the parameters of a genetic algorithm (GA), according to the observed performance. Experiments were carried out for both small-sized and large-sized problems to analyse the impact of equipment used in the loading process on berthing and computation times, as well as to test the efficiency of our proposed adaptive GA in solving this integrated problem.
UR - https://www.sciencedirect.com/science/article/pii/S0360835220305489
U2 - 10.1016/j.cie.2020.106848
DO - 10.1016/j.cie.2020.106848
M3 - Article
VL - 149
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
M1 - 106848
ER -