Optimization Model of Fleet Deployment Plan of Liners

Song, Yajie; Yue, Yixiang

doi:10.1016/j.proeng.2016.01.273

Cited by 13 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most of the existing container fleet deployment [12,13] models, it is assumed that the sailing speed is independent of the optimal fleet deployment decisions and set as constant, but in reality, the sailing speed is variable. Gelareh and Meng [14] studied the CSFD problem and proposed a MIP model with speed as a decision variable.…”

Section: Literature Reviewmentioning

confidence: 99%

Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

Gao

2021

Sustainability

View full text Add to dashboard Cite

In recent years, low energy consumption has become the common choice of economic development in the world. In order to control energy consumption, shipping line speed optimization has become strategically important. to reduce fuel consumption, this study optimizes the container ship fleet deployment problem by adopting the strategy of adjusting each leg of each route’s sailing speed. To calculate fuel consumption more accurately, both sailing speed and the ship's payload are considered. A multi-objective mixed integer nonlinear programming model is established to optimize the allocation of liner routes with multiple ship types on multiple routes. A linear outer-approximation algorithm and an improved piecewise linear approximation algorithm are used for linearization. If segments of an interval increase, the results will be more accurate but will take more time to compute. As fuel prices increase, to make trade-offs among economic and environmental considerations, the shipping company is adopting the “adding ship and slow down its speed” strategy, which verifies the validity and applicability of the established model.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

Gao

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…The common methods for solving the bi-objective optimization problem include the ideal point method, the constraint method [19], and the construction of Pareto solutions [20], [21]. The first two types of methods mainly transform the multi-objective problem into a single-objective problem, which can reduce complexity, but both methods need to make decisions first and then search.…”

Section: Introductionmentioning

confidence: 99%

Placement Approach for the Data Storage Management Function in a RAN-CN Converged Network

Huang,

Zong,

Wang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Recently, the control plane of the core network (CN) has introduced a flat service-based architecture to improve flexibility, openness, and intelligence. And a novel radio access network (RAN)-CN converged network architecture was proposed to leverage the advantages of virtualization and cloudification, reduce network delay and overhead, and increase the network processing efficiency. In this paper, in order to fully utilize the advantages of the RAN-CN converged network and move services closer to user equipment, a multi-layer network architecture is proposed, where some CN network functions (NFs) are moved from the CN to the edge node (EN) and integrate with corresponding RAN functions to avoid unnecessary signaling and data forwarding. And all NFs in the EN except converged data storage management function (cDSM) are designed to be stateless to decouple the storage resources from these NFs. Based on the proposed multilayer network architecture, a cDSM placement approach based on improved non-dominated sorting genetic algorithm II (iNSGA-II) is proposed to balance the data forwarding cost and the maximum load on cDSMs. Simulation results demonstrate that compared with the conventional NSGA-II, the convergence speed and distribution of the proposed iNSGA-II are improved. And according to different network requirements, the proposed scheme can achieve a trade-off between the data forwarding cost and the maximum load on cDSMs.INDEX TERMS radio access network, core network, network architecture, data storage management function, NSGA-II

show abstract

“…Firstly, from the perspective of a single ship, the sailing speed or route is optimized according to the relationship between fuel consumption and speed, as well as the environmental conditions (Wang et al, 2016;Yan et al, 2015). Secondly, from the point of view of a fleet of ships and its logistics, the fleet's economic navigation schedule and overall route planning is formulated to reduce costs and emissions (Andersson et al, 2015;Song and Yue, 2016). Thirdly, from the perspective of a ship's design, the optimization of both the hull's shape and propulsion system's design can be adopted to reduce energy consumption and greenhouse gas emissions (Zhao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel method for joint optimization of the sailing route and speed considering multiple environmental factors for more energy efficient shipping

Wang

Huang

et al. 2020

Ocean Engineering

View full text Add to dashboard Cite

Energy saving and emission reduction have attracted a great deal of attention in the maritime industry. The optimization of a ship's energy efficiency can reduce energy consumption and CO2 emissions effectively. However, most of the available studies only focus on either the sailing speed or route optimization, and the interaction between speed and route under the influence of multiple environmental factors was not accounted properly. In this paper, a novel joint optimization method of the sailing route and speed, which considers the interaction between route and speed as well as multiple environmental factors, is proposed to fully exploit the energy efficiency's potential. Moreover, a joint optimization model of the sailing route and speed, which is based on an energy consumption model that considers multiple environmental factors, is established. Next, a solution algorithm for the joint optimization model is investigated in order to achieve joint decision-making with regard to the sailing route and speed. Finally, a case study is conducted that demonstrates the effectiveness of the proposed method. The results show that the proposed method can achieve the optimal sailing route and speed under complex environmental conditions, as well as a reduction in fuel consumption and CO2 emissions of about 4%.

show abstract

Optimization Model of Fleet Deployment Plan of Liners

Cited by 13 publications

References 5 publications

Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

Placement Approach for the Data Storage Management Function in a RAN-CN Converged Network

A novel method for joint optimization of the sailing route and speed considering multiple environmental factors for more energy efficient shipping

Contact Info

Product

Resources

About