A general enhancement of the Benders’ decomposition (BD) algorithm can be achieved through the improved use of large neighbourhood search heuristics within mixed-integer programming solvers. While mixed-integer programming solvers are endowed with an array of large neighbourhood search heuristics, few, if any, have been designed for BD. Further, typically the use of large neighbourhood search heuristics is limited to finding solutions to the BD master problem. Given the lack of general frameworks for BD, only ad hoc approaches have been developed to enhance the ability of BD to find high quality primal feasible solutions through the use of large neighbourhood search heuristics. The general BD framework of SCIP has been extended with a trust region based heuristic and a general enhancement for large neighbourhood search heuristics. The general enhancement employs BD to solve the auxiliary problems of all large neighbourhood search heuristics to improve the quality of the identified solutions. The computational results demonstrate that the trust region heuristic and a general large neighbourhood search enhancement technique accelerate the improvement in the primal bound when applying BD.

Benders分解（BD）算法的总体增强可以通过在混合整数规划求解器中改进对大型邻域搜索启发式算法的使用来实现。虽然混合整数编程求解器具有大量的大型邻域搜索启发式方法，但为BD设计的很少（如果有的话）。此外，通常，使用大型邻域搜索试探法仅限于找到BD主问题的解决方案。鉴于缺乏BD的通用框架，仅开发了专门方法来增强BD通过使用大型邻域搜索启发式方法找到高质量的原始可行解决方案的能力。SCIP的通用BD框架已扩展了基于信任区域的启发式方法和对大型邻域搜索启发式方法的常规增强。通用增强功能使用BD来解决所有大型邻域搜索启发式算法的辅助问题，以提高所识别解决方案的质量。计算结果表明，在应用BD时，信任区域启发式算法和常规的大邻域搜索增强技术可加速原始边界的改进。