Alexey Lyutov

• The field of logistics and supply chain management deals with various supply network problems on multiple levels starting from strategic years-long decisions regarding network topology, down to operational weekly-based decisions. Moreover, due to the interaction of customers and random accidents, the system becomes stochastic and difficult to control based only on logistic experience. To help with the problem of supply chain design and management, scientists try to approach the field with existing instruments including network science, mathematical modeling, control theory, machine learning, etc. In this thesis, a complex approach that addresses different aspects of supply network management is demonstrated. First, an automatization scheme for the daily management of logistic requirements is proposed. Second, an in-depth investigation of non-conventional usage of natural language processing and machine learning algorithms is presented. The developed approach can be used to enhance the process of requirement management by extracting additional knowledge about the supply network operation. Third, the strategic problem of designing robust supply networks is addressed by developing a minimalistic model of a supply network. The model is designed to simulate a scenario of supply-demand imbalance and generate networks that satisfy the imbalance in a robust way. The overall outcome of the work is a better understanding of separate supply network aspects and an attempt to holistically improve the way how supply networks are managed.