Exact and heuristic algorithms for the interval data robust assignment problem
Exact and heuristic algorithms for the interval data robust assignment problem
复制标题
区间数据鲁棒分配问题的精确启发式算法
DOI：
10.1016/j.cor.2010.11.009
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
I. Averbakh
中科院分区：
文献类型：
--
作者：
J. Pereira;I. Averbakh
我们考虑具有区间数据的分配问题，其中假设每个成本系数仅知道上界和下界。它需要找到一个极小极大后悔分配。该问题是强NP-难的。我们提出并比较计算几个确切的和启发式的方法，包括Benders分解，使用CPLEX，一个可变深度的邻域局部搜索，和两个混合人口为基础的算法。我们报告了大量的计算实验的结果。
We consider the Assignment Problem with interval data, where it is assumed that only upper and lower bounds are known for each cost coefficient. It is required to find a minmax regret assignment. The problem is known to be strongly NP-hard. We present and compare computationally several exact and heuristic methods, including Benders decomposition, using CPLEX, a variable depth neighborhood local search, and two hybrid population-based heuristics. We report results of extensive computational experiments.