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March 25, 2026IET conference proceedings.0 citations

A decision-making method for ship liquid tank transfer based on local optimization algorithms

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ZZZhihua ZuoPXPing XinXYXudong Yang

Key Points

  • This research aims to develop a fast decision-making method for liquid tank transfers on ships using local optimization algorithms.
  • Development of a nonlinear optimization model for mass movement and free surface correction.
  • Transformation of tank level adjustment into a multi-constraint optimization problem.
  • Use of local optimization algorithms, specifically SLSQP, BOBYQA, and COBYLA, for solution comparison.
  • SLSQP algorithm demonstrated better accuracy in handling equality constraints than BOBYQA and COBYLA algorithms.
  • The proposed method generates transfer plans in approximately 0.39 seconds under small-angle conditions, fulfilling rapid adjustment needs for ship stability.

Abstract

To address the need for rapid decision-making in ship liquid tank transfer operations, a rapid transfer decision-making method based on local optimization algorithms is proposed. A nonlinear optimization model that includes mass movement and free surface correction effects is constructed, and tank level adjustment is thus transformed into a multi-constraint optimization problem. By introducing a criterion for minimizing the center of gravity transfer distance, efficient solutions are obtained using local optimization algorithms such as SLSQP (Sequential Least-Squares Quadratic Programming). Case studies show: (1) The accuracy of the SLSQP algorithm, which supports equality constraints, is higher than the BOBYQA (Bound Optimization by Quadratic Approximation) and COBYLA (Constrained Optimization by Linear Approximations) algorithms, which use penalty functions to handle equality constraints; (2) This decision-making method can generate transfer plans within 0.39 seconds under small-angle scenarios, meeting the rapidity requirements for adjusting the floating state of a ship.

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Cite This Study

Zuo et al. (2026) studied this question.

synapsesocial.com/papers/69c37bc2b34aaaeb1a67e88ahttps://doi.org/10.1049/icp.2026.0112
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