Master in Artificial Intelligence (UPC-URV-UB) Master of Science Thesis Scalable decentralized supply chain formation through binarized belief propagation

Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. Very recently, the decentralized SCF problem has been cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Unfortunately, the memory and communication requirements of this approach largely hinder its scalability. This paper presents a novel encoding of the problem into a binary factor graph (containing only binary variables) along with an alternative algorithm. These allow to scale up to form supply chains in markets with higher degrees of competition.