Multi-criteria improvement of complex systems

• A general framework to design multi criteria improvements of complex systems.
• A MAUT preference model and a qualitative behavioral model jointly used to capture both managerial and operational aspects.
• An iterative improvement procedure supported by mathematical models and software tools to test our approach on a study case.
• A branch and bound algorithm that supports the search of adequate parameters to be applied on the system to improve it.

Designing the way a complex system should evolve to better match customers’ requirements provides an interesting class of applications for muticriteria techniques. The models required to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures decisions related to design preferences, whereas a fuzzy representation is proposed to model relationships between system parameters and the fulfillment of system assessment criteria. The way in which these models are jointly used throughout our entire design procedure highlights that both models must be used in tandem to address managerial and implementation issues involved in an improvement project. The iterative improvement process is supported by a mathematical model, in addition to a software tool that allows our approach to be tested in an industrial case study. The search for adequate parameters regarding the improvement design is supported by a branch and bound algorithm to compute the most relevant actions to be performed. The findings confirm the efficiency of the algorithm.