Efficient assignment algorithms to minimize operation cost for supply chain networks in agile manufacturing. (June 2017)

• Record Type: Journal Article
• Authors: Jiang, Weiwen; Sha, Edwin H.-M.; Zhuge, Qingfeng; Wu, Lin
• Is Part Of: Computers & industrial engineering. Volume 108(2017)
• Page Start: 225
• Page End: 239
• Publication Date: 2017-06
• Subjects: Supply chain network -- Agile manufacturing -- Assignment algorithms -- Optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.04.014
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Strategic and tactical planning is integrally considered in agile manufacturing scenarios. Solutions of the optimal assignment problem for large-scale supply chains are presented. Mixed integer linear programming method is presented to model the assignment problem. Efficient dynamic programming algorithm is devised to obtain the optimal assignment. Abstract: While the production process evolves toward modularization and decentralization, the design of supply chain networks, in particular considering the agile manufacturing scenario, becomes challenging due to the following reasons: (1) supply chains that produce a network of components become large-scale; (2) the number of possible assignments is growing exponentially as the increasing choices of plants for components. In this paper, the assignment problem considers the strategic and tactical decisions together, which involves the mapping of components to geographically distributed plants, the selection of logistics services between the mapped plants, and the allocation of inventories in each plant. The goal of this paper is to find the optimal assignment with the minimum total cost under the constraint of production rate. We first mathematically formulate the problem as a mixed integer linear program. Then, by deriving the properties of pipelined production in supply chain networks, we develop dynamic programming algorithms to efficiently obtain the optimal assignments. By the consideration of high degree of pipelining, our techniques can make a good tradeoff between high production rate and low operation cost. Extensive computational experiments show that the proposed algorithms can find high quality solutions, which achieve significant improvement compared with the initiative approaches.