Biblio

Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.

The purpose of this research work is to develop an approach based on risk management with a view to provide managers and decision-makers with assistance and appropriate guidelines to combine Lean and Green in a successful and integrated way. Risk cannot be managed if not well-identified; hence, a classification of supply chain risks in a Lean Green context was provided. Subsequently to risk identification an approach based on Weighted Product Method (WPM) was proposed; for risk assessment and prioritization, for its ease of use, flexibility and board adaptability. The output of this analysis provides visibility about organization's position toward desired performance and underlines crucial risks to be addressed which marks the starting point of the way to performance improvement. A case study was introduced to demonstrate the applicability and relevance of the developed framework.