箱装:设置箱数,希望最小化最大箱重
给定 n 个无限容量的箱子,我想将 m 个物品装入其中(每个物品都有特定的重量),同时最大限度地减少最重箱子的重量。
这不是传统的垃圾箱包装/背包问题,其中垃圾箱的容量有限,并且您试图最大限度地减少垃圾箱的使用量;我有一定数量的垃圾箱,并且想将它们全部使用,以使最重的垃圾箱的重量尽可能低。
这个问题有名字吗?我查阅了很多带有关键词的论文,但没有发现类似的内容。
干杯。
Given n bins of infinite capacity, I want to pack m items into them (each with a specific weight), whilst minimizing the weight of the heaviest bin.
This isn't a traditional bin packing / knapsack problem where a bin has a finite capacity and you attempt to minimize the amount of bins used; I have a set amount of bins and want to use them all in order to make the heaviest bin's weight as low as possible.
Is there a name for this problem? I have looked through a number of papers with key words, but I have found nothing similar.
Cheers.
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如果垃圾箱的数量是约束,而不是垃圾箱的容量,那么它就不是垃圾箱包装,而是 多处理器调度问题。
通常,您可以通过 LPT 算法来解决这个问题,并获得很好的结果。不过,需要进行优化,这就是乐趣所在。
If the amount of bins is the constraint, instead of the capacity of bins, then it's not a bin packing, it's a multiprocessor scheduling problem.
Usually, you can approach this by a LPT algorithm with pretty good results. Optimizations will be needed though and that's where the fun lies.
这是2D装箱问题的一种形式。第一个维度是对每个垃圾箱容量的限制(= 硬约束),第二个维度是最小化最重垃圾箱的重量( =软约束)。
使用Drools Planner,我会从云平衡示例 并按如下方式实现:
It's a form of a 2D bin packing problem. The first dimension is a limit on capacity per bin (= hard constraint), the second dimension is to minimize the weight of the heaviest bin (= soft constraint).
With Drools Planner, I 'd start from the cloud balance example and implement it like this: