With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism.

随着信息技术（IT）外包行业的不断发展，许多企业将IT服务外包以降低成本。但是，IT外包（ITO）项目的进度风险可能会给企业带来巨大的经济损失。在本文中，委托-代理理论用于控制ITO项目的进度风险。建立了一个两级数学模型来描述客户和供应商的决策过程。随着子项目和活动数量的增加，问题的规模将变得非常大。最终的优化是具有连续域的NP难题。因此，设计了一种遗传算法（GA）来解决该模型。实验进行了测试该算法的能力。仿真分析中的一些见解-委托-代理理论和两级数学模型-适用于描述原理与代理之间的合作关系。通过与蚁群优化和模拟退火的比较，提出的遗传算法在收敛性，可靠性和效率上都表现出强大的优化能力，是解决此类优化问题的良好工具。接近最优的计划显着降低了项目的进度风险，这是决策者的科学定量建议。本研究为从业人员对进度风险与ITO项目之间的关系提供了见识，本文的设计模型和算法为从业人员降低ITO项目在其运营中的进度风险提供了有效的潜在方法。然而，在未来的工作中应考虑关键因素和多重因素的不确定性。随机规划和蒙特卡洛模拟方法是处理不确定因素的两个潜在工具。此外，拟议的遗传算法可能会在融合方面得到改进。可以将其他智能算法的优势应用于GA，以提高其搜索能力，例如禁忌机制。