Modeling and Optimization Algorithms in Ship Weather Routing

Walther, Laura; Rizvanolli, Anisa; Wendebourg, Mareike; Jahn, Carlos

doi:10.1016/j.enavi.2016.06.004

Cited by 45 publications

(25 citation statements)

References 12 publications

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“…The marine weather routing literature has developed a range of different shortest path algorithms. The different approaches were classified into calculus of variations, grid based approaches and evolutionary optimisation (Walther et al, 2016). A time dependent approach based on the calculus of variations solved the shortest path problem through identifying the shortest path to take through considering a series of fronts reached within incremental time steps (Bijlsma, 1975).…”

Section: Literature Reviewmentioning

confidence: 99%

Uncertainty in marine weather routing

Dickson

Farr

Sear

et al. 2019

Applied Ocean Research

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Section: Literature Reviewmentioning

confidence: 99%

Uncertainty in marine weather routing

Dickson

Farr

Sear

et al. 2019

Applied Ocean Research

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“…Regarding trajectory optimization for marine vehicles, the constant need to increase economic feasibility under the premise of the vehicle's safety motivates various implementations of vehicle routing systems [29]. to repeatedly compute the reachable region until the goal point is involved [33].…”

Section: Introductionmentioning

confidence: 99%

Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters

Hildre

Zhang

2020

IEEE J. Oceanic Eng.

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Ship intelligence has been a hot topic in recent years. How to achieve autonomous maneuvers in a complex marine environment in a safe, efficient, and low-cost manner is a fundamental task that ocean engineers face. This paper presents a two-stage trajectory planning scheme to address the minimumtime maneuvering problem in close-range encounters. The scheme is robust and versatile, as it can deal with the complex spatial variability, such as sea current, state constraints, marine traffic, and physical constraints, of close-range maneuvering. In the first stage, a directed graph with variable length is generated according to the sea current distribution. A wavefront search is applied on the graph to explore the reachability, the cost of state constraints, and the risk of collision. After a discrete solution has been found, the second stage involves searching for a smooth solution. A Bézier curve based parameter optimization approach is proposed to get rid of limited moving directions in the directed graph and explore around the discrete path. The result will be a near-optimal, smooth path. The proposed scheme has been tested to solve the Zermelo's ship steering problem and several other close-range maneuvering problems. The results demonstrate that the scheme is efficient in generating smoothed minimum-time trajectories for surface vessels when maneuvering in close-range encounters.

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“…Meanwhile, extensive logistics management has become a bottleneck in the economic development of China and the world [2]. The intelligent logistics service model has become an important factor in improving the efficiency of logistics operations, reducing logistics costs, and increasing the competitiveness of logistics and the whole society [3][4]. Based on a variety of resources such as processors, memories, and application software on the virtualized Internet, Cloud computing technology builds a unified resource pool, unified management, and unified scheduling, then provides various computing services to users, and supports expansion according to customer needs [5].…”

Section: Introductionmentioning

confidence: 99%

Cloud Intelligent Logistics Service Selection Based on Combinatorial Optimization Algorithm

Yan¹,

Cao²,

Yang³

2019

JESA

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The selection of intelligent logistics service model has become an important factor in the competition of the entire social logistics industry. Using the technologies such as the Internet of Things (IoT) and cloud computing, the service platform of cloud intelligent logistics (CIL) virtualizes and accesses to the distributed physical logistics resources and logistics capabilities, and relies on its powerful processing and control capabilities to obtain the best service portfolio of CIL. The paper proposes the service combinatorial optimization algorithm (COA). Based on this, it studies the cloud intelligent logistics service and service combination method. The results show that compared with clustering algorithm and differential evolution algorithm, COA algorithm has greater superiority and stability in selection and combination of CIL service; the CIL service has the characteristics of dynamicity and diversity, heterogeneity and distribution, abstraction and similarity; the service portfolio of CIL is divided into three stages: service classification, service negotiation and optimal combination. The application of COA in the CIL selection can greatly reduce the time consumption of combined logistics service and improve the overall service quality of combinatorial service.

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Modeling and Optimization Algorithms in Ship Weather Routing

Cited by 45 publications

References 12 publications

Uncertainty in marine weather routing

Uncertainty in marine weather routing

Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters

Cloud Intelligent Logistics Service Selection Based on Combinatorial Optimization Algorithm

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