干扰情形下重复性项目快速修复策略模型与算法

重复性项目受到干扰事件影响后，如何使其低成本，快速修复到基准调度计划，是项目管理者面临的重要问题。本文研究了干扰情形下重复性项目的反应性调度问题。首先提出了一种新的快速修复策略模型，旨在使项目以较低的成本快速修复到基准调度计划；针对问题特点，设计了一种Q-learning与遗传算法结合的混合算法进行求解；最后通过一个高速公路项目和蒙特卡洛模拟验证了本文模型和算法的有效性。结果表明：本文所提出的修复策略可以显著降低反应性调度成本；在一定范围内，增加修复的范围可以有效降低反应性调度成本；Q-learning与遗传算法混合算法在该问题上的求解质量和效率优于遗传算法。本文可以为重复性项目管理者进行反应性调度提供决策依据。

The model and algorithm for quickly repairing strategy of repetitive projects under interference scenarios

Repetitive projects are usually interfered by uncertainties, and the most important challenge for project managers is how to quickly repair the interfered plan to the baseline scheduling at minimum cost. The reactive scheduling model and algorithm for repetitive projects are studied in this paper. Firstly, a new model of repairing strategy was proposed to quickly repair the plan to the baseline schedule at a minimum cost. Then a hybrid algorithm combining Q-learning and genetic algorithm was designed to solve the optimization problem. Finally, a highway project and Monte Carlo simulation were used to illustrate the effectiveness of the model and algorithm. Results show that the repairing strategy proposed in this paper can reduce reactive scheduling costs, and increasing the extent of repairing can effectively reduce the reactive scheduling costs. Moreover, the proposed hybrid algorithm is superior to the genetic algorithm in terms of solution quality and efficiency for this problem. It can help project managers to deal with reactive scheduling problems of repetitive projects.