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This study develops an integrated model to investigate the economic and environmental effects of a unilateral maritime emission regulation vis-a-vis a uniform maritime emission regulation. The proposed model explicitly incorporates the effects of competition between regional ports and between shipping companies, and captures operational considerations such as the inventory costs of in-transit cargo, and the tradeoff between enlarged fleet size and slow steaming. The behaviors of shipping companies and ports are modeled in a two-stage game so that market equilibria under alternative regulations can be solved and compared. The findings suggest that a unilateral regulation may actually lead to an increase in total emissions, whereas a uniform regulation always reduces total emissions. Under either type of regulation, there can be asymmetric effects on shipping companies and ports. Therefore, regulators and the maritime industry need to strike a balance between emission reduction and fair competition. Our study cautions against unilateral regulations, and emphasizes the importance to take into account the effects of alternative emission policies on the operations of shipping companies and ports.

By focusing on several critical speeds, this paper proposes a simple and applicable method to seek the optimal speed for carriers. The results show that carriers will reduce ship speed if the saved fuel cost outweighs the incurred capital and operating costs. The measure of critical fuel prices at different critical speeds is powerful to examine the optimal speed. Meanwhile, deploying extra ships and calling extra ports could be an optimal strategy for carriers in implementing slow steaming. The empirical result suggests that the optimal speeds for large ships will be higher than the ones for small ships.

The role of container repositioning has become more important under the severe cargo shipping environment, affected by world trade growth, trade imbalance, slow steaming strategy and high container manufacturing cost. Low cost, better routing, and supplying equipment to higher yield cargo become the top criteria. A yield-based container repositioning framework is developed, followed by a constrained linear programming optimizing the container repositioning from surplus to deficit locations. The model incorporated change of destinations of empty containers and adjustment factors handling upsurge demand. The model is applied to optimize daily container repositioning operations with a better route, costs and equipment supply.

This paper analyses slow steaming sustainability initiatives and generalizes the traditional discrete cost-based decision support model into novel continuous utility-based models. Two models based on logarithmic and linear utility functions are developed for risk-averse and risk-neutral decision makers respectively. The models, considering fuel consumption, carbon emission, and on time delivery, are applied to a Trans-pacific trade service route. A sensitivity analysis is conducted on parameters of sailing distance, expected transit time, quantity, and emission policies. The model contributes to ship liners on the optimal speed decisions in continuous utility-based slow steaming operations.

With a dominant volume of global transportation being conducted by sea, ocean container transport greatly impacts the global economy. Since sea vessels are drastically more fuel efficient when traveling at lower speeds, slow steaming has become a widely adopted practice to reduce bunker costs. However, this leads to a longer transportation time, which together with the unpredictability of the delay has been a big challenge. We propose a model to quantify the relationship among shipping time, bunker cost and delivery reliability. Our findings lead to a simple and implementable policy with a controlled cost and guaranteed delivery reliability.

This paper first calibrates the bunker consumption – sailing speed relation for container ships using historical operating data from a global liner shipping company. It proceeds to investigate the optimal sailing speed of container ships on each leg of each ship route in a liner shipping network while considering transshipment and container routing. This problem is formulated as a mixed-integer nonlinear programming model. In view of the convexity, non-negativity, and univariate properties of the bunker consumption function, an efficient outer-approximation method is proposed to obtain an e-optimal solution with a predetermined optimality tolerance level e. The proposed model and algorithm is applied to a real case study for a global liner shipping company.