Modeling the Impacts of Tides and the Virtual Arrival Policy in Berth Allocation

Du, Yanliang; Chen, Qiushuang; Lam, Jasmine Siu Lee; Xu, Youpeng; Cao, Jin Xin

doi:10.1287/trsc.2014.0568

Cited by 102 publications

(54 citation statements)

References 36 publications

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“…Du et al [7] proposed a mixed-integer second-order cone programming model for a BAP considering the fuel consumption and vessel emissions. Du et al [8] considered the impacts of tides on the BAP. Hu et al [13] further involved the QC allocation into the BAP and also considered the fuel consumption and emissions from vessels.…”

Section: Related Workmentioning

confidence: 99%

“…Bunker consumption is the major part of operating costs as mentioned above. This factor has been considered in the BAP studies [8,19]. According to Du et al [8], the formula for estimating bunker consumption is as follows.…”

Section: Functions For the Bunker Consumption Costsmentioning

confidence: 99%

“…Here the length of the planning horizon |T | is usually one week as most shipping liner services are operated on a weekly basis [3]. Constraints (15)(16)(17) state the domains for the variables α i and ε i based on Formulae (7)(8).…”

Section: Indices and Setsmentioning

confidence: 99%

“…With the identification of the types, the coefficients (i.e., k, k , and u) and the highest speed of Vessel i (i.e., v max i ) can be determined. We set the coefficients referring to the range mentioned by Du et al [8]. For instance, the k, k , and u for feeders are 702, 0.0153, and 3.5, respectively.…”

Section: Generation Of Test Casesmentioning

confidence: 99%

See 3 more Smart Citations

Terminal allocation problem in a transshipment hub considering bunker consumption

Zhen

Wang

2016

Naval Research Logistics

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This 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.

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Section: Related Workmentioning

confidence: 99%

Section: Functions For the Bunker Consumption Costsmentioning

confidence: 99%

Section: Indices and Setsmentioning

confidence: 99%

Section: Generation Of Test Casesmentioning

confidence: 99%

See 2 more Smart Citations

Terminal allocation problem in a transshipment hub considering bunker consumption

Zhen

Wang

2016

Naval Research Logistics

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“…[13] provides approaches for the extraction of vessel routes and anomaly detection in movement patterns for decision support systems. On the other hand [14] is investigating the impacts of tides on seaside operations in container ports. Both are focusing on specific factors that influence the arrival times.…”

Section: Related Workmentioning

confidence: 99%

Factors Affecting Ocean-Going Cargo Ship Speed and Arrival Time

Filtz

Cerda

Weber

et al. 2015

Lecture Notes in Business Information Processing

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Abstract. Due to the high density of ocean traffic and the influence of marine weather on the route planning of vessels, as well as berth allocation in harbors, it is important to be able to predict arrival times as precise as possible. This paper shows the influence of marine weather on ship speed by analyzing publicly available ship traffic and weather data from different sources. A linear regression model is created to explain recorded ship speed in terms of certain ship properties and marine weather. The model has an adjusted R 2 value of 83.98% with a significant correlation of many weather related data such as wind direction (0.211), significant wave height (0.195), peak wave period (0.133), as well as ship-related data including ship type, dead weight tonnage, and gross register tonnage. Given the variables in the model the speed of the ship could be estimated fairly well. These variables along with other factors are tested regarding their usefulness for the prediction of arrival times.

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Subsidy design in a vessel speed reduction incentive program under government policies

Zhuge

Wang

Zhen

et al. 2020

Naval Research Logistics

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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.

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Modeling the Impacts of Tides and the Virtual Arrival Policy in Berth Allocation

Cited by 102 publications

References 36 publications

Terminal allocation problem in a transshipment hub considering bunker consumption

Terminal allocation problem in a transshipment hub considering bunker consumption

Factors Affecting Ocean-Going Cargo Ship Speed and Arrival Time

Subsidy design in a vessel speed reduction incentive program under government policies

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