Title: Integration of Machine Learning and Optimization for Supply Chain Optimization

Abstract: We are trying to develop the AI techniques that can be explained by an automatic synthesis of decision-making models through the integration of machine learning and optimization. By constructing a feedback loop of machine learning and optimization, a closed-loop data-driven system that can improve the machine learning accuracy and significantly reduce the computational time required for optimization through machine learning methods even in a shortage of teaching data. From the viewpoint, our recent topics on dynamic multi-commodity network design problem, virtualization of supply chains, mass customization of product design and supply chain planning, inverse optimization for scheduling problems, imitative learning will be discussed.