Outperforming Several Heuristics for the Multidimensional Assignment Problem

C. E. Valencia, Carlos A. Alfaro, F. Martínez, Marcos Cesar Vargas Magana, Sergio Luis Pérez-Pérez
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引用次数: 0

Abstract

The multidimensional assignment problem (MAP), also known as multi-index assignment problem, is a natural extension of the assignment problem. A MAP deals with the question of how to assign elements from s disjoint sets with n1,…,ns items on each. A MAP with s dimensions is called a sAP. Local search heuristics and Memetic algorithms have been proven to be the most effective techniques to solve MAP. In this work we use an exact technique that outperforms some local searches and Memetic algorithms on several instances of MAP. Then, we design a heuristic that uses repeatedly our exact technique in order to provide high quality solutions for harder types of instances of MAP. We perform an experimental evaluation of our exact technique and of our heuristic and we show its effectiveness against more complex local searches and some Memetic algorithms for MAP.
多维分配问题的几种启发式算法
多维分配问题(MAP)又称多索引分配问题,是分配问题的自然延伸。MAP处理的问题是如何从s个不相交的集合中分配元素,每个集合上有n1,…,ns个元素。具有5个维度的MAP被称为sAP。局部搜索启发式和模因算法已被证明是求解MAP的最有效技术。在这项工作中,我们使用了一种精确的技术,在几个MAP实例上优于一些局部搜索和模因算法。然后,我们设计了一个启发式算法,重复使用我们的精确技术,以便为MAP的较难类型的实例提供高质量的解决方案。我们对我们的精确技术和我们的启发式进行了实验评估,并展示了它对更复杂的局部搜索和MAP的一些模因算法的有效性。
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