Two-stage stochastic programming model and algorithm for mitigating supply disruption risk on aircraft manufacturing supply chain network design. (January 2023)

• Record Type: Journal Article
• Authors: Hu, Hongtao; Guo, Shuyuan; Qin, Yichen; Lin, Wenjin
• Is Part Of: Computers & industrial engineering. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: Complex product manufacturing -- Airframe structure supply chain network design -- Supply disruption -- Stochastic programming -- Improved Benders decomposition algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108880
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A supply chain network design problem with supply disruption risk is addressed. A two-stage stochastic programming model characterizing uncertainties is developed. Supplier selection and strategic partnership establishment are considered. An improved Bender's Decomposition algorithm is proposed for large-scale instances. Computational experiments analyze the effectiveness of the proposed exact algorithm. Abstract: Establishing business relationships with upstream supply enterprise is one of the features in aircraft manufacturing supply chain, given the long lead-time and high value-added components for airframe manufacturing. For the characteristics of multi-echelon, parallel modules, and multi-period structure in the aircraft manufacturing industry, the supply chain network design problem and the strategy of risk mitigation are investigated under uncertain supply disruption risk in this paper. The decisions for supplier selection, strategic partnership establishment and quantities of production and transportation for manufacturing components should be optimized under the uncertain supply disruptions. Therefore, a two-stage stochastic programming model is formulated. The overall objective is to minimize the cost of business relation establishment with upstreaming supply enterprises, production cost, inventory cost, transportation cost, as well as the penalty cost for unsatisfied demand under the environment of supply chain disruption risk. Given the structure of the mathematical model and the difficulties in resolving large-scale problem in practical situations, an improved Benders decomposition algorithm incorporating heuristic searching mechanism is designed to enhance the computational efficiency. The computational results manifest the effectiveness of the proposed algorithm in view of accuracy and efficiency of convergence, which brings practical values to aircraft manufacturing industry in handling supply risk.