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

作者：Nishi Tatsushi^*; Shinozaki Ryuichi; Konishi Masarni

来源：IEEE Transactions on Automation Science and Engineering, 2008, 5(2): 259-274.

DOI：10.1109/TASE.2007.894727

• augmented Lagrangian relaxation
• business-to-business (B2B)
• distributed optimization method
• multiple companies
• supply chain planning

Planning coordination for multiple companies has received much attention from viewpoints of global supply chain management. In practical situations, a plausible plan for multiple companies should be created by mutual negotiation and coordination without sharing such confidential information as inventory costs, setup costs, and due date penalties for each company. In this paper, we propose a framework for distributed optimization of supply chain planning using an augmented Lagrangian decomposition and coordination approach. A feature of the proposed method is that it can derive a near-optimal solution without requiring all of the information. The proposed method is applied to supply chain planning problems for a petroleum complex, and a midterm planning problem for multiple companies. Computational experiments demonstrate that the average gap between a solution derived by the proposed method and the optimal solution is within 3% of the performance index, even though only local information is used to derive a solution for each company.
Note to Practitioners-Supply Chain Management (SCM) has received much attention with growing mass customization and global market demands. The global performance of SCM is achieved through cooperation and interaction among several organizations. For the petroleum industry, multiple companies within a chemical complex are depending on their suppliers to provide raw materials or intermediates through a pipeline connected with multiple companies to provide efficient delivery with lower costs. In most plants, production instructions are given to each production section in each company. Each company is thoroughly capable in regard to its own production planning and scheduling. Therefore, each company may have its own desirable supply and demand plan for each section. The supply and demand plan must be coordinated by mutual negotiations across the supply chain. Such coordination has been performed by the communications among human operators. However, in recent years, the decision making for each company is becoming increasingly complex with the increase of the number of alternative for the plan with a number of partner companies. Conventional planning systems has been configured to obtain a near-optimal plan using the detailed information about multiple companies. However, organizations generally have their own private coordination methods, and that accessing others' private information or intruding on their decision-making authority should be avoided. In practical situations, a plan must be created without sharing such confidential information as production costs, inventory holding costs, or price of products for competitive companies. In this paper, we propose a framework for distributed supply chain planning system without requiring all of information for multiple companies. Planning coordination can be efficiently automated by the proposed system. Computational experiments demonstrate that the performance of the solution is 3% of gap from an optimal solution with a reasonable computation time, even though only local information is used to derive a solution for each company.

• 出版日期2008-4