TY - GEN
T1 - Stowage Planning with Optimal Ballast Water
AU - Jia, Beizhen
AU - Fagerholt, Kjetil
AU - Reinhardt, Line Blander
AU - Rytter, Niels Gorm Maly
PY - 2020
Y1 - 2020
N2 - Stowage planning is at the essence of a maritime supply chain, especially for short sea Ro-Ro ships. This paper studies stowage optimisation of Ro-Ro ships with a focus on stability constraints and the applicability of models. The paper contributes to short sea Ro-Ro ship stowage in two ways. First, we propose an integrated approach of designing stowage models with the consideration of loading computers. Second, we present a mathematical formulation of the Ro-Ro Ship Stowage Problem with Ballast Water with a discretisation method, to generate an optimal stowage plan which meets stability requirements by means of the weight of cargoes instead of excess ballast water, i.e. excess fuel consumption. Computational tests based on empirical data indicate significant savings and potential of model application in the real world. Preliminary results show 57.69% ballast water reduction, equivalent to 6.7% fuel savings and CO2 reduction. Additional tests on instances with various cargo weight distribution and discretisation levels are conducted, and finally, improvements are suggested for further research considerations.
AB - Stowage planning is at the essence of a maritime supply chain, especially for short sea Ro-Ro ships. This paper studies stowage optimisation of Ro-Ro ships with a focus on stability constraints and the applicability of models. The paper contributes to short sea Ro-Ro ship stowage in two ways. First, we propose an integrated approach of designing stowage models with the consideration of loading computers. Second, we present a mathematical formulation of the Ro-Ro Ship Stowage Problem with Ballast Water with a discretisation method, to generate an optimal stowage plan which meets stability requirements by means of the weight of cargoes instead of excess ballast water, i.e. excess fuel consumption. Computational tests based on empirical data indicate significant savings and potential of model application in the real world. Preliminary results show 57.69% ballast water reduction, equivalent to 6.7% fuel savings and CO2 reduction. Additional tests on instances with various cargo weight distribution and discretisation levels are conducted, and finally, improvements are suggested for further research considerations.
KW - Ballast water
KW - Environmental impact
KW - Maritime transportation
KW - Stowage optimisation
UR - https://doi.org/10.1007/978-3-030-59747-4_50
U2 - 10.1007/978-3-030-59747-4_6
DO - 10.1007/978-3-030-59747-4_6
M3 - Article in proceedings
SN - 978-3-030-59746-7
T3 - Lecture Notes in Computer Science
SP - 84
EP - 100
BT - Computational Logistics. ICCL 2020
A2 - Lalla-Ruiz, Eduardo
A2 - Mes, Martijn
A2 - Voß, Stefan
PB - Springer
T2 - 11th International Conference on Computational Logistics
Y2 - 28 September 2020 through 30 September 2020
ER -