Near-Optimal Weather Routing by Using Improved A* Algorithm

Abstract

With soaring oil prices worldwide, determining the most optimal routes for economical ship operation has become an important issue. Optimizing ship routes is economically important for ship operation, but it is also essential to meet the standards of environmental regulations recently imposed by the International Maritime Organization. For this purpose, various algorithms for determining ship routes have been developed to ensure the economical operation of ships via utilization of marine climate data and Automatic Identification System (AIS) data. However, such algorithms require a large amount of computational time and do not provide optimal routes because they do not consider practical operating conditions, such as weather and ocean conditions. In this study, an improved A* algorithm using AIS and weather data is proposed to overcome the limitation of the original A* algorithm, one of the most widely used path-finding algorithms. The improved A* algorithm uses an adaptive grid system that efficiently explores nodes according to map grid deformation by latitude. It finds economical routes by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration. For verification of the proposed method, the original A* algorithm and improved A* algorithm were compared through a case study.

Prediction of Arrival Time of Vessels Considering Future Weather Conditions

“…The accuracy of the prediction method in Section 3 is verified in this section. Among the previous methods described in 2.1, those of Shin et al [14], Pennino et al [15], and Liu et al [16] calculate the optimal voyage route but do not predict the arrival time. Moreover, the study of Szelangiewicz et al [19] only focused on calculating the future voyage speed.…”

“…However, unlike other modes of transportation, vessels have some freedom in selecting routes, which has resulted in numerous studies on vessel routing. In recent years, the Dijkstra algorithm [12], which is often used to find the shortest path in problems, and its derivative, the A* algorithm [13][14][15], have been used in studies to calculate routes considering weather conditions. The machine learning models of support vector regression (SVR) and differential evolution (DE) have also been combined for use in route prediction [16].…”

“…However, it focused on short-distance routes with a linear system model; therefore, long-distance routes from departure to destination may not be estimated accurately. Although future weather conditions have been considered in other routing studies [14,15], they used the Dijkstra method or the A* algorithm to determine the shortest route to the destination port. However, as Fujii et al have shown, the shortest route is not always taken in practice because of safety considerations.…”

Path planning of scenic spots based on improved A* algorithm

“…Das et al proposed an online path planning method based on an improved real-time A* algorithm, which plans the optimal path by avoiding obstructions and minimizing time, energy, and distance as the cost 18 . Shin et al proposed an improved A* algorithm using Automatic Identification System (AIS) and weather data, and it finds the optimal paths by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration 19 . Alani et al proposed a new technique that consists of a hybridizing of A* algorithm and ant colony optimization, and the new technology can more accurately find the best parking path 20 .…”

Thresholding Methods in Non-Intrusive Load Monitoring to Estimate Appliance Status

“…From a mathematical perspective, weather routing involves solving optimization problems in order to find the optimal route [3], [4]. This typically involves using algorithms such as dynamic programming [5], [6] or graph search [7], [8] to find the best combination of route and speed that will minimize the time and fuel consumption of the vessel.…”