(Georgia Institute of Technology, 2023-06)
Pothen, Ashwin; Inan, Mahmut Metin; Montreuil, Benoit; Lauras, Matthieu; Benaben, Frederick; Xie, Yao
Supply chain networks today comprise of various decentralized actors, subject to constantly evolving challenges and customer expectations, and operate in a volatile, uncertain, and disruption-prone environment. These challenges and complexities bring in informational and material flow distortions, making it hard to align demand and supply with agility. Building a centralized optimization model for such complex systems tends to be computationally expensive and unscalable for real-world application. With this motivation, we propose a novel, real-world applicable multi-agent-based approach for collaborative and agile demand-supply alignment, through dynamic prediction-driven planning and operational decision-making. We first demonstrate the applicability and configurability of our approach with a real-world supply chain network operating in a stochastic and disruptive environment, with the desired characteristics in congruence with the Physical Internet framework. We then demonstrate the simulation-testing capability of our approach by highlighting the potential benefits of leveraging a hyperconnected network of open certified production options.
Model-Driven Engineering (MDE) is meant, above other usages mainly in software design, to support the process of system design, especially the transitions and the coherency between models of different levels and different point of views. In the presentation, Dr. Benaben will introduce an original and innovative Artificial Intelligence (AI) framework dedicated to include MDE in AI technologies. An instantiation of this framework on the domain of crisis management will be presented as an illustrative example of providing sentience, agility and resilience in the handling of an instable situation. Then, the usage of this research framework will be extended to provide avenues and perspectives in ways to connect Industrial Engineering and Artificial Intelligence to support (collaborations of) organizations, decision making and management of organizations.