ABSTRACT
The onset of 2020 is marked by stricter restrictions on maritime sulfur emissions and the spread of Coronavirus Disease 2019 (COVID-19). In this background, liner companies now face the challenge to find suitable sulfur reduction technologies, make reasonable decisions on fleet renewal, and prepare stable operation plans under the highly uncertain shipping market. Considering three sulfur reduction technologies, namely, fuel-switching, scrubber, and liquefied natural gas (LNG) dual-fuel engine, this paper develops a robust optimization model based on two-stage stochastic linear programming (SLP) to formulate a decision plan for container fleet, which can deal with various uncertainties in future: freight demand, ship charter rate, fuel price, retrofit time and Sulfur Emission Control Area (SECA) ratio. The main decision contents include ship acquisition, ship retrofit, ship sale, ship charter, route assignment, and speed optimization. The effectiveness of our plan was verified through a case study on two liner routes from the Far East to Northwest America, operated by COSCO Shipping Lines. The results from SLP model show that large-capacity fuel-switching ships and their LNG dual-fuel engine retrofits should be included in the long-term investment and operation plan; slow-steaming is an important operational decision for ocean liner shipping; if the current SECA boundary is not further expanded or the sulfur emission restrictions not further tightened, the scrubber ship will have no advantage in investment cost and operation. However, considering the probabilities of more flexible scenarios, the results from the robust model suggest that it is beneficial to install scrubber on medium-capacity fuel-switching ships, and carry out more LNG dual-fuel engine retrofits for large-capacity fuel-switching ships. Compared with SLP, this robust strategy greatly reduces sulfur emissions while slightly pushing up carbon emissions.
ABSTRACT
With coronavirus disease 2019 reshaping the global shipping market, many ships in the Europe-Asia trades that need to sail through the Suez Canal begun to detour via the much longer route, the Cape of Good Hope. In order to explain and predict the route choice, this paper employs the least absolute shrinkage and selection operator regression to estimate fuel consumption based on the automatic identification system and ocean dataset and designed a multiobjective particle swarm optimization to find Pareto optimal solutions that minimize the total voyage cost and total voyage time. After that, the weighted sum method was introduced to deal with the route selection. Finally, a case study was conducted on the real data from CMA CGM, a leading worldwide shipping company, and four scenarios of fuel prices and charter rates were built and analyzed. The results show that the detour around the Cape of Good Hope is preferred only in the scenario of low fuel price and low charter. In addition, the paper suggests that the authority of Suez Canal should cut down the canal toll according to our result to win back the ships because we have verified that offering a discount on the canal roll is effective. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)