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Fast,effective Heuristics For The 0-1 Multi-dimensional Knapsack Problem

机译:0-1多维背包问题的快速有效启发式方法

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The objective of the multi-dimensional knapsack problem (MKP) is to find a subset of items with maximum value that satisfies a number of knapsack constraints.Solution methods for MKP,both heuristic and exact,have been researched for several decades.This paper introduces several fast and effective heuristics for MKP that are based on solving the LP relaxation of the problem.Improving procedures are proposed to strengthen the results of these heuristics.Additionally,the heuristics are run with appropriate deterministic or randomly generated constraints imposed on the linear relaxation that allow generating a number of good solutions.All algorithms are tested experimentally on a widely used set of benchmark problem instances to show that they compare favourably with the best-performing heuristics available in the literature.
机译:多维背包问题(MKP)的目的是找到满足多个背包约束条件的具有最大值的项目子集。已经研究了数十年的启发式方法和精确方法的MKP解决方法。在解决问题的LP松弛的基础上,提出了几种快速有效的MKP启发式方法。提出了改进程序以增强这些启发式方法的结果。此外,在适当的确定性或随机生成的线性松弛约束条件下运行启发式算法允许生成许多好的解决方案。所有算法都在一组广泛使用的基准问题实例上进行了实验测试,表明它们与文献中提供的性能最佳的启发式方法相比具有优势。

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