Multi-projects environments are complex and dynamic systems. They include many components and dependencies, and many changes may occur in the execution of projects. Moreover, projects are inherently distributed; each task may be completed by different resources or in different geographical locations and each project manager may be in different places.
MAS have been shown to deal with problems of complexity, openness (components of the system are not known in advance, can change over time, and are highly heterogeneous, dynamic in project management terms), with dynamical and unknown environments changing over time(uncertainty) and ubiquity (the activity is distributed over the complete structure).
In the particular case of multi-project systems, the agents can be abstracted as tasks, resources, project managers, etc. This design enables to distribute the management system in
elemental components directly identifiable in the target system, and hence giving the opportunity to create systems easier to design, to adapt and to maintain. Moreover, since the system is distributed according to its structure, any change in the structure can be easily translated to the management system.
This decentralized approach facilitates the design of market mechanisms to solve the scheduling problem by means of distributed approximations [2]. Recently, Lee, Kumara and Chatterjee [7] have proposed an agent-based dynamic resource scheduling for multiple distributed projects using market mechanisms. Following the same research line, Confessore etal. propose in [3] another iterative combinatorial auction mechanism. Other examples of agent-based approaches in project management can be found in the works of Kim and colleagues [6], Wu and Kotak [13], and Cabac [1].