Multi-criteria improvement of complex systems

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. (C) 2014 Elsevier Inc. All rights reserved.