Due to European Union (EU) oil sanctions, tanker shipping companies need to redeploy their tankers by moving tankers between ship routes with the consideration of flag states of tankers, but the literature lacks quantitative methods for this problem. To fill this research gap, this paper studies an integrated problem of fleet deployment, fleet repositioning, round trip completion, and speed optimization with the consideration of flag states of tankers. The problem is formulated as a nonlinear integer programming model to minimize the total cost, including the fleet repositioning cost, the mismatch cost, and the fuel cost, during the planning period while satisfying the total crude oil transportation demand of each voyage and the minimum shipping frequency. Some linearization methods are used to transform the nonlinear model to a linear one which can be directly solved by Gurobi. The average solving time required for 17 computational instances is 4.5 minutes, which validates the effectiveness of the proposed model. Sensitivity analyses, including the influences of the unit fuel price, the total crude oil transportation demand, the mismatch cost of completing a round trip by a deployed tanker, and the repositioning cost for each deployed tanker, on operations decisions, are conducted to obtain managerial insights.

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.