Decentralised scheduling with confidentiality protection

As resources become rare, collaboration is increasingly important for players in a decentralised supply chain. One of the main challenges in collaboration is to find close to globally optimal solutions without sharing individual players' private data. For problems that are amenable to MILP formulations with a global objective as a sum of linear objective-functions of individual players, we present a Lagrangian relaxation and Secure Sum Protocol based methodology that can find correct global lower- and upper-bounds without needing to share any private data.