Journal
ADVANCED ENGINEERING INFORMATICS
Volume 48, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2021.101299
Keywords
Liner shipping; Heterogeneous fleet; Service frequency determination; Ship fleet deployment; Sailing speed optimization; Ship scheduling
Funding
- National Science Foundation (United States) [CMMI-1901109]
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Maritime transportation flows and container demand have been increasing, and one common strategy adopted by shipping lines is deploying large ships. However, there has been limited research on heterogeneous fleets in tactical liner shipping decisions. This study proposes an integrated optimization model to address all major tactical liner shipping decisions, including the deployment of a heterogeneous ship fleet and considering emissions generated throughout operations.
The maritime transportation flows and container demand have been increasing over time, although the COVID19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model's objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided.
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