基于改进蚁群优化的多目标资源受限项目调度方法

多目标资源受限项目调度是一类典型的NP难组合优化问题,具有广泛的实际应用背景.本文提出了一种带局部搜索的改进蚁群优化算法用于求解多目标资源受限项目调度问题,优化指标为最小化项目工期和资源投资.首先,采用改进的蚁群优化算法获取Pareto解集;其次,通过基于带逻辑约束的Insert和Swap邻域搜索方法对已获得的非支配解进行局部搜索,进一步提高算法的性能;最后,基于PSPLIB国际标准测试集的数值仿真实验与现有最好的算法比较,验证了所提算法的有效性和高效性.

Multi-objective resource constrained project scheduling problem based on improved ant colony optimization

Multi-objective resource constrained project scheduling problem is a typical NP-hard combinational optimization problem with a wide range of application background. In this paper, an improved ant colony optimization with local search is proposed to address the multi-objective resource-constrained project scheduling problem, the aim is to minimize the makespan and resource investment criteria. Firstly, the Pareto sets are obtained by using the improved ant colony optimization (IACO). Secondly, the performance of IACO is enhanced by the logic constraints based local searches, i.e., Insert and Swap, and the non-dominated solutions are further improved. Numerical simulations and comparisons with the state-of-the-art algorithms based on the international standard benchmarks PSPLIB for MORCPSP are carried out, which demonstrate the effectiveness and efficiency of the proposed algorithm.