Optimal model and improved genetic algorithm of containership stowage on full route
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摘要: 以集装箱船舶稳性、强度、载荷为约束条件, 以全航线倒箱量最小和吃水差最优为目标函数, 建立了集装箱船舶全航线多目标配载优化模型。利用启发式算法获得初始可行解, 利用改进了的遗传算法进行优化, 并用1 841TEU、3开口的集装箱船舶进行实例验证。计算结果表明: 与传统遗传算法相比, 改进的遗传算法能在1.967s内求得全航线上5个挂靠港口的配载计划, 并能求得5个港口的满意解; 在求得的满意解中, 船舶倒箱量均为0, 吃水差的绝对值分别为0.003 5、0.000 8、0.109 7、0.001 1、0.371 2m, 均在船舶行驶的合理范围0~0.5m内; 对于不同挂靠港数量的其他航线, 改进的遗传算法能在5s内快速获得合理的配载计划。可见, 优化模型与改进的遗传算法可行。Abstract: The stability, strength and load of containership were taken as constraint conditions, the minimum shift and the optimal trim on full route were taken as objective functions, and the multi-objective optimal model of containership stowage on full route was established.The initial feasible solution was obtained by using heuristic algorithm and was optimized by using improved genetic algorithm, and the example verification was carried out on the practical containership with capacity of 1 841 TEU and 3 hatches.Calculation result shows that by using the improved genetic algorithm, the stowage plans of 5 call ports can be obtained within 1.967 s, and the reasonable solution in each port can be obtained compared with traditional genetic algorithm.In the obtained reasonable solutions, all the shift amounts are 0, and the absolute values of trim are 0.003 5, 0.000 8, 0.109 7, 0.001 1 and 0.371 2 mrespectively, which are within a reasonable range between 0 to 0.5 m.For the other routes of call ports with different amounts, the reasonable stowage plan can be highly achieved within 5 s by using the improved genetic algorithm.So, the optimal model and improved genetic algorithm are feasible.
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Key words:
- container transportation /
- route stowage /
- genetic algorithm /
- full route /
- trim
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图 8 传统遗传算法与改进遗传算法优化结果比较
Figure 8. Comparison of optimal results for general GA and improved GA
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