The multidimensional knapsack problem (MKP) is an NP-hard combinatorial optimization problem whose solution consists of determining a subset of items of the maximum total profit that does not violate capacity constraints. Due to its hardness, large-scale MKP instances are usually a target for metaheuristics, a context in which effective feasibility maintenance strategies are crucial. In 1998, Chu and Beasley proposed an effective heuristic repair that is still relevant for recent metaheuristics. However, due to its deterministic nature, the diversity of solutions such heuristic provides is not sufficient for long runs. As a result, the search ceases to find new solutions after a while. This paper evaluates the use of efficiency groups to define a randomization strategy for the heuristic repair that increases the variability of the repaired solutions, without deteriorating quality and improves the overall results. We compared our randomized heuristic repair against the original one in 270 or-library instances, with improvements at the running time and solution quality found for many of them.

多维背包问题（MKP）是NP-hard组合优化问题，其解决方案包括确定最大总利润中不违反容量约束的项目子集。由于其硬度，大型MKP实例通常是元启发法的目标，在这种情况下，有效的可行性维护策略至关重要。1998年，Chu和Beasley提出了一种有效的启发式修复方法，该方法仍然与最近的元启发式方法有关。但是，由于其确定性，这种启发式方法提供的解决方案的多样性不足以长期运行。结果，搜索将在一段时间后停止寻找新的解决方案。本文评估了效率组的使用，为启发式修复定义了一种随机化策略，该策略增加了修复后的解决方案的可变性，而不会降低质量并改善了总体结果。我们将随机启发式修复与原始的270种进行了比较或库实例，其中许多实例在运行时间和解决方案质量方面都有改进。