论文部分内容阅读
在后续港装载状态未知情况下,针对始发港混装bay位的排箱问题提出不出现倒箱条件下,实现bay位重心位置和横倾力矩最优的多目标优化数学模型,并通过离散粒子群算法进行求解,给出粒子位置的矩阵表达形式,并通过交叉和局部搜索策略对粒子位置进行更新.该算法简便有效,收敛速度较快,可增加种群的多样性,有效抑制早熟出现.实例结果表明,该模型和求解算法可实现多港bay位排箱优化.
Under the condition that the loading status of subsequent ports is unknown, a multi-objective optimization mathematical model is proposed to achieve the optimal barycenter position and yawing moment under the condition of no boxing for the baying problem of mixed bay at the port of origin. Discrete Particle Swarm Optimization (PSO) is used to solve the problem, and the particle representation of the particle location is given, and the particle location is updated by cross and local search strategy.The algorithm is simple and effective, has fast convergence speed, increases the population diversity and effectively restrain the premature emergence The case study shows that the model and the algorithm can achieve the optimization of multi - bay bay cabinets.