TY - JOUR
T1 - Optimal dual cycling operations in roll-on roll-off terminals
AU - Jia, Beizhen
AU - Tierney, Kevin
AU - Reinhardt, Line Blander
AU - Pahl, Julia
N1 - Funding Information:
The research was partially supported by European Interreg Baltic Sea Region Program (ECOPRODIGI project) and the RoRoGreen Project funded by the Innovation Fund Denmark . We wish to thank our industrial partner DFDS for their openness and innovation and valuable support throughout the conduct of research. We thank the Paderborn Center for Parallel Computation (PC) for the use of the OCuLUS cluster.
Funding Information:
The research was partially supported by European Interreg Baltic Sea Region Program (ECOPRODIGI project) and the RoRoGreen Project funded by the Innovation Fund Denmark . We wish to thank our industrial partner DFDS for their openness and innovation and valuable support throughout the conduct of research. We thank the Paderborn Center for Parallel Computation (PC2) for the use of the OCuLUS cluster.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/3
Y1 - 2022/3
N2 - Roll-on roll-off (RoRo) shipping plays an important role in freight transport on the European continent, and is faced with the challenge of reducing its CO2 emissions while increasing its efficiency. Dual cycling, in which loading and discharging processes are carried out simultaneously, achieves this goal by reducing the turnaround time of vessels in port and thus the CO2 output of handling equipment in port and fuel consumption through slow steaming at sea. Optimizing the dual cycling operations on RoRo vessels has not yet been investigated in the literature. This paper presents the novel RoRo dual cycling problem (RRDCP), and formulates it using integer programming (IP) with the objective to minimize the total makespan of discharging and loading operations. We further prove that the RRDCP is NP-complete by a reduction from a general machine scheduling problem, and introduce a novel heuristic to solve the problem called a generalized random key algorithm (GRKA). We evaluate the IP model and GRKA approach on both generated and industrial instances, showing that the GRKA heuristic finds optimal or near-optimal solutions to real-world problems in just seconds. We provide managerial insights on industrial instances, which indicate that our approach leads to a reduction in fuel consumption and CO2 emissions of up to 25% for RoRo operations.
AB - Roll-on roll-off (RoRo) shipping plays an important role in freight transport on the European continent, and is faced with the challenge of reducing its CO2 emissions while increasing its efficiency. Dual cycling, in which loading and discharging processes are carried out simultaneously, achieves this goal by reducing the turnaround time of vessels in port and thus the CO2 output of handling equipment in port and fuel consumption through slow steaming at sea. Optimizing the dual cycling operations on RoRo vessels has not yet been investigated in the literature. This paper presents the novel RoRo dual cycling problem (RRDCP), and formulates it using integer programming (IP) with the objective to minimize the total makespan of discharging and loading operations. We further prove that the RRDCP is NP-complete by a reduction from a general machine scheduling problem, and introduce a novel heuristic to solve the problem called a generalized random key algorithm (GRKA). We evaluate the IP model and GRKA approach on both generated and industrial instances, showing that the GRKA heuristic finds optimal or near-optimal solutions to real-world problems in just seconds. We provide managerial insights on industrial instances, which indicate that our approach leads to a reduction in fuel consumption and CO2 emissions of up to 25% for RoRo operations.
KW - Dual cycling
KW - RoRo shipping
KW - Scheduling complexity
KW - Maritime Shipping
U2 - 10.1016/j.tre.2022.102646
DO - 10.1016/j.tre.2022.102646
M3 - Journal article
AN - SCOPUS:85125482543
SN - 1366-5545
VL - 159
JO - Transportation Research Part E: Logistics and Transportation Review
JF - Transportation Research Part E: Logistics and Transportation Review
M1 - 102646
ER -