Two-level principal–agent model for schedule risk control of IT outsourcing project based on genetic algorithm. (May 2020)

• Record Type: Journal Article
• Authors: Bi, Hualing; Lu, Fuqiang; Duan, Shupeng; Huang, Min; Zhu, Jinwen; Liu, Mengying
• Is Part Of: Engineering applications of artificial intelligence. Volume 91(2020)
• Publication Date: 2020-05
• Subjects: IT outsourcing project -- Schedule risk -- Risk control -- Principal–agent theory -- Genetic algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103584
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.