Two-phase approach to optimal weather routing using geometric programming

Park, Jinmo; Kim, Nakwan

doi:10.1007/s00773-015-0321-6

Cited by 22 publications

(7 citation statements)

References 14 publications

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“…The fuel saving potential of speed profile optimisation in the voyages studied was 1.1% across all studied ships, which is in line with the results of similar studies on speed profile optimisation [14,22,24]. This value is not all that significant considering that the error margins of the model and uncertainty in weather predictions may also shift the actual result.…”

Section: Discussionsupporting

confidence: 86%

“…Speed profile optimisation in relation to voyages from port A to port B with a fixed schedule were studied in [14,[20][21][22][23][24][25]. In [14], the authors developed a forward dynamic programming algorithm for solving the route and speed profile of a ship.…”

Section: Introductionmentioning

confidence: 99%

“…The authors demonstrated that the developed optimisation model was capable of producing a robust fuel budget under varying environmental conditions for the studied route. The authors in [24] developed a two-stage weather routing algorithm where first the route was optimised with A* by keeping the speed of the vessel constant. Once the optimal route was found, a novel geometric program was employed to find the optimal speed profile for the vessel.…”

Section: Introductionmentioning

confidence: 99%

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Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule

Huotari

Manderbacka

Ritari

et al. 2021

JMSE

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We present a novel convex optimisation model for ship speed profile optimisation under varying environmental conditions, with a fixed schedule for the journey. To demonstrate the efficacy of the proposed method, a combined speed profile optimisation model was developed that employed an existing dynamic programming approach, along the novel convex optimisation model. The proposed model was tested with 5 different ships for 20 journeys from Houston, Texas to London Gateway, with differing environmental conditions, which were retrieved from actual weather forecasts. As a result, it was shown that the combined model with both dynamic programming and convex optimisation was approximately 22% more effective in developing a fuel saving speed profile compared to dynamic programming alone. Overall, average fuel savings for the studied voyages with speed profile optimisation was approximately 1.1% compared to operation with a fixed speed and 3.5% for voyages where significant variance in environmental conditions was present. Speed profile optimisation was found to be especially beneficial in cases where detrimental environmental conditions could be avoided with minor speed adjustments. Relaxation of the fixed schedule constraint likely leads to larger savings but makes comparison virtually impossible as a lower speed leads to lower propulsion energy needed.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule

Huotari

Manderbacka

Ritari

et al. 2021

JMSE

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“…Choi et al proposed a speed optimisation model for ice regions, considering that ice areas can change over time [19]; they used the A * algorithm to plan an optimal navigation path. Park et al [20] proposed a two-stage weather routing model. In the first stage, the ship's speed is maintained and the A * algorithm is employed to achieve route path optimisation.…”

Section: Literature Reviewmentioning

confidence: 99%

Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

et al. 2022

Journal of Advanced Transportation

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The implementation of ship speed control is extremely important in the shipping industry. It is affected by various factors, such as water depth, obstacles, and environmental factors. Traditional speed control methods only consider geographical constraints, which is difficult to achieve the goal of safe navigation and maritime traffic efficiency simultaneously. Accordingly, a two-stage speed dynamic control model is proposed in this study. In the first stage, certain safety navigation factors, including obstacles, sea environment conditions, and limit of estimated time of arrival to destination port, are considered. In the second stage, the speed dynamic control model considering safety and environmental factors is established by combining multisource data and particle swarm optimisation algorithm. The model’s superiority and advantage are validated by experiments conducted on an ocean-going ship. The experimental results show that the proposed dynamic speed control model can reduce the ship’s fuel consumption and improve energy efficiency while ensuring the safety navigation. The study is anticipated to be used as a reference for speed dynamic control.

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“…Szłapczynska and Smierzchalski (2009) introduced multi-objective approaches in weather routing, and Szlapczynski and Szlapczynska (2012) used evolutionary methods for the multi-ship trajectory planning problem. Park and Kim (2015) combined the A* algorithm with a speed scheduling phase problem in order to minimize fuel consumption and achieved reductions between 2 and 3% in their case studies. Bentin et al (2016) use the A* algorithm on a bulk carrier and optimize fuel consumption considering also wind-assisted propulsion via Flettner rotors.…”

Section: Pathfinding and Genetic Algorithmsmentioning

confidence: 99%