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A heuristic and hybrid method for the tank allocation problem in maritime bulk shipping

机译:海运散货运输中油罐分配问题的启发式和混合方法

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摘要

In bulk shipping, ships often have multiple tanks and carry multiple inhomogeneous products at a time. When operating such ships it is therefore a major challenge to decide how to best allocate cargoes to available tanks while taking into account tank capacity, safety restrictions, ship stability and strength as well as other operational constraints. The problem of finding a feasible solution to this tank allocation problem has been shown to be NP-Complete. We approach the problem on a tactical level where requirements for computation time are strict while solution quality is less important than simply finding a feasible solution. We have developed a heuristic that can efficiently find feasible cargo allocations. Computational results show that it can solve 99 % of the considered instances within 0.4 s and all of them if allowed longer time. We have also modified an optimality based method from the literature. The heuristic is much faster than this modified method on the vast majority of considered instances. However, the heuristic struggles on two instances which are relatively quickly solved by the modified optimality based method. These two methods therefore complement each other nicely and so, we have created a hybrid method that first runs the heuristic and if the heuristic fails to solve the problem, then runs the modified optimality based method on the parts of the problem that the heuristic did not solve. This hybrid method cuts between 90 and 94 % of the average running times compared to the other methods and consistently solves more instances than the other methods within any given time limit. In fact, this hybrid method is fast enough to be used in a tactical setting.
机译:在散装运输中,船舶通常有多个储罐,并且一次运送多个不均匀的产品。因此,在操作此类船舶时,要决定如何最佳地将货物分配到可用的油箱,同时考虑到油箱容量,安全限制,船舶稳定性和强度以及其他操作限制,这是一个重大挑战。为该储罐分配问题寻找可行解决方案的问题已显示为NP-Complete。我们在战术层面上解决该问题,在该层面上,对计算时间的要求非常严格,而解决方案的质量并不比简单地找到可行的解决方案重要。我们开发了一种启发式方法,可以有效地找到可行的货物分配。计算结果表明,它可以在0.4 s内解决99%的实例,如果允许更长的时间,则可以解决所有实例。我们还从文献中修改了基于最优性的方法。在绝大多数考虑的实例上,启发式方法比这种修改方法要快得多。然而,在两种情况下的启发式斗争很难通过改进的基于最优性的方法来解决。因此,这两种方法可以很好地互补,因此,我们创建了一个混合方法,该方法首先运行启发式算法,如果启发式算法无法解决问题,然后针对启发式算法未解决的部分问题运行改进的基于最优性的方法解决。与其他方法相比,此混合方法节省了90%至94%的平均运行时间,并且在任何给定的时间限制内,与其他方法相比,能够始终解决更多实例。实际上,这种混合方法足够快,可以在战术环境中使用。

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