Distributed decision-making in supply chains and private e-marketplaces

This paper develops a distributed decision-making framework for the players in a supply chain or a private e-marketplace to collaboratively arrive at a global Pareto-optimal solution. In this model, no player has complete knowledge about all the costs and constraints of the other players. The decision-making framework employs an iterative procedure, based on the Integer L-shaped method, in which a master problem is solved to propose global solutions, and each player uses his local problems to construct feasibility and optimality cuts on the master problem. The master problem is modeled as a mixed-integer program, and the players' local problems are formulated as linear programs. Collaborative planning scenarios in private e-marketplaces and in supply chains were formulated and solved for test data. The results show that this distributed model is able to achieve near-optimal solutions considerably faster than the traditional centralized approach.