An augmented Lagrangian approach for decentralized supply chain planning for multiple companies

Coordination and optimization of supply chain planning among multiple companies have widely been received much attention from viewpoints of global supply chain management. Conventional system for supply chain planning is configured on the assumption that correct information for entire company is available by sharing the detailed information among multiple companies. It is required to generate a near optimal plan for multiple companies without sharing the confidential information such as inventory costs, set up costs and due date penalties among competing companies. In this paper, we propose a framework of a distributed supply chain planning for multiple companies by using an augmented Lagrangian relaxation approach. The proposed method features that a feasible solution can be derived without using the entire information by exchanging the data which is not directly related to cost data. From the computational experiments, it has been shown that the average gap between the solution derived by the proposed method and an optimal solution is within 1% of the performance index even though only the local information is used to derive a solution for each company.