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AbstractMaritime transportation is the backbone of international trade but ships emit a large amount of air pollutants at ports. To encourage ships to use clean energy while berthing at port, subsidies are provided to ship operators that use clean energy and the subsidy amount is generally determined based on subjective judgment. We, therefore, examine the optimal subsidy design for government‐operated ports, aiming at balancing the environmental benefits and subsidy expenses. Considering the uncertainty of energy requirements by ships, we build a stochastic optimization model. Taking advantage of the problem structure, we convert the model into a deterministic one by applying sample average approximation and a binomial distribution. The model is then linearized and solved by CPLEX. A series of numerical experiments with realistic parameters are conducted to validate the model and useful managerial insights are obtained.

AbstractThis article investigates the method of allocating arriving vessels to the terminals in transshipment hubs. The terminal allocation decision faced by a shipping alliance has the influence on the scheduled arrival time of vessels and further affects the bunker consumption cost for the vessels. A model is formulated to minimize the bunker consumption cost as well as the transportation cost of inter‐terminal transshipment flows/movements. The capacity limitation of the port resources such as quay cranes (QCs) and berths is taken into account. Besides the terminal allocation, the QC assignment decision is also incorporated in the proposed model. A local branching based method and a particle swarm optimization based method are developed to solve the model in large‐scale problem instances. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 14% of the cost when compared with the "First Come First Served" decision rule. Moreover, the proposed solution methods not only solve the proposed model within a reasonable computation time, but also obtain near‐optimal results with about 0.1∼0.7% relative gap. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 529–548, 2016

As a green port and shipping-related policy, the vessel speed reduction incentive program (VSRIP) involves using a subsidy to induce ships to reduce their speed in a port area so that the emissions can be reduced at the port. However, this program may attract new ships to visit the port because of the subsidy; in this case, the port's profit will grow due to more ship visits, but its total emissions may also increase, which is counter to the original intention of the subsidy. The government could then intervene by providing part of the subsidy for the VSRIP or by collecting air emission taxes for the increased emission at the port. This paper studies how to design suitable subsidies for ships participating in a VSRIP. Two bilevel subsidy design models are formulated based on a Stackelberg game to maximize the port's profit (related to the profits from original and new ships, the subsidy provided by the port, and air emission taxes) and to minimize the government's cost (related to the damage cost of air emissions, the subsidy provided by the government, and air emission taxes). We determine which policy (including a sharing subsidy policy, no government intervention, and an air emission tax policy) should be implemented by the government in different cases and how much subsidy should be provided by the port under each government policy. We find that these decisions are affected by several practical factors, such as the damage cost of air emissions per ton of fuel and the subsidy sensitivities of original and new ships. We also outline several meaningful insights based on the analysis of these practical factors.

AbstractAs a green port and shipping‐related policy, the vessel speed reduction incentive program (VSRIP) involves using a subsidy to induce ships to reduce their speed in a port area so that the emissions can be reduced at the port. However, this program may attract new ships to visit the port because of the subsidy; in this case, the port's profit will grow due to more ship visits, but its total emissions may also increase, which is counter to the original intention of the subsidy. The government could then intervene by providing part of the subsidy for the VSRIP or by collecting air emission taxes for the increased emission at the port. This paper studies how to design suitable subsidies for ships participating in a VSRIP. Two bilevel subsidy design models are formulated based on a Stackelberg game to maximize the port's profit (related to the profits from original and new ships, the subsidy provided by the port, and air emission taxes) and to minimize the government's cost (related to the damage cost of air emissions, the subsidy provided by the government, and air emission taxes). We determine which policy (including a sharing subsidy policy, no government intervention, and an air emission tax policy) should be implemented by the government in different cases and how much subsidy should be provided by the port under each government policy. We find that these decisions are affected by several practical factors, such as the damage cost of air emissions per ton of fuel and the subsidy sensitivities of original and new ships. We also outline several meaningful insights based on the analysis of these practical factors.

AbstractThis study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two‐stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two‐stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.

AbstractGas and particulate emissions from ship transportation have been increasing in recent years. In order to mitigate ship emissions near coastal areas, voluntary vessel speed reduction incentive programs (VSRIPs) were put in place by a number of ports. This paper studies a schedule design problem faced by liner shipping companies under VSRIPs. It proposes a mixed‐integer nonlinear mathematical model for the minimization of the total cost, consisting of fuel cost, as well as operating cost, minus dockage refunds. The model balances three determinants, that is, the compliance of VSRIPs, the speed limit (the maximum physical speed of ships and the upper speed limit imposed by VSRIPs), and the limited number of ships. An enumerative algorithm and a piecewise‐linear approximation algorithm are developed, based on some properties of the nonlinear model. The efficiency of the proposed algorithms is validated through extensive computational experiments.

Maritime transport is the backbone of international trade. The amount of total international maritime trade in million tonnes loaded was 8408 in 2012 and had increased to 11.076 by 2019, for an average annual increase of 3.12%. In early 2020, the world fleet contained 98.140 ships of 100 gross tonnes and above with 2.06 million dead weight tonnage of capacity. The greenhouse gas (GHG) emissions from shipping activities are not negligible. According to the fourth GHG study commissioned by the International Maritime Organization (IMO), in 2018, global shipping emitted a total of 1056 million tonnes of carbon dioxide (CO 2 ), accounting for around 2.89% of global anthropogenic CO 2 emissions. Due to the international nature of shipping, efforts to control CO 2 emissions from ships are absent from the Kyoto Protocol and the Paris Agreement. In an attempt to phase out carbon emissions from shipping entirely, the IMO formulated a strategy to cut the total annual GHG emissions from shipping by at least 50% from their 2008 levels by 2050; however, no mandatory rules have been promulgated since the release of this strategy. Given the insufficient progress made by the IMO, the European Union (EU) decided to take a leading role in promoting the reduction of CO2 emissions from maritime transport. In 2015, the EU issued regulations on the monitoring, reporting, and verification (MRV) of CO 2 emissions from ships with a gross tonnage above 5000 arriving at, within, or departing from ports under the jurisdiction of an EU member state, to come into force at the beginning of 2018. It should be noted that, under the MRV regime, even if only one port on a voyage is within the European Economic Area (EEA) and the other is not (e.g., a voyage from Rotterdam directly to Singapore), the ship must still report the total CO 2 emissions of the whole voyage, rather than just the emissions of the part of the voyage within EU waters. The MRV regime has been in operation for over two years, and the CO 2 emissions data for the 2018 and 2019 ...

AbstractThis study examines the effect of changes in the number of shipping lines on port charges and profits. A two‐stage noncooperative game‐theoretic model with two ports and multiple identical shipping lines is developed. In the first stage, the ports decide their container handling charges, and in the second stage, the shipping lines decide on the number of calls to make at each port, given the port charges. The model is then applied to the case of competition between two ports in New Zealand, the Port of Tauranga and the Port of Auckland, to derive managerial insights. Our study extends the literature on port competition by incorporating competition between shipping lines. We also demonstrate that a decrease in the number of shipping lines may force ports to increase their handling charges. Furthermore, we show that each shipping line ships more cargo than the industry optimal via the port with lower costs.