Combinatorial Optimization Algorithms for Problem Solving Systems

在各种实际情况下，往往需要实现最优化。这项研究的目的是开发强大的、通用的组合优化算法作为问题解决的工具。在这项研究中，我们在前人工作的基础上，考虑了一些提高算法性能的技术，并提出了可以处理更广泛问题的扩展模型。为了验证算法的有效性，我们对排班、护士调度、资源受限调度等问题进行了计算实验。

In various real situations, it is often required to achieve optimization. The objective of this study is to develop powerful and general-purpose combinatorial optimization algorithms as problem-solving tools. In this study, based on previous works, we considered some techniques for improving the performance of algorithms, and proposed extended models that can handle a wider range of problems. In order to demonstrate the usefulness of our algorithms, we conducted computational experiments for timetabling, nurse scheduling, resource-constrained scheduling and other problems.