4.6 Article

A model integrating environmental concerns and supply risks for dynamic sustainable supplier selection and order allocation

Journal

SOFT COMPUTING
Volume 25, Issue 1, Pages 535-549

Publisher

SPRINGER
DOI: 10.1007/s00500-020-05165-3

Keywords

Dynamic supplier selection; Order allocation; Sustainable supply chain; Computing; Soft computing

Funding

  1. Key Realm R&D Program of GuangDong Province [2019B020214002]
  2. Beijing Social Science Foundation [18GLB022, 18GLA009, 17GLC066, 19GLC051, 19ZDA12]
  3. Beijing Intelligent Logistics System Collaborative Innovation Center
  4. Social science program of Beijing Municipal Education Commission [SM201810037001, SM201910037004]
  5. Beijing Wuzi University Major Research Projects [2019XJZD12]

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A novel two-stage mathematical model has been developed for selecting suppliers and assigning order quantities, aiming to help enterprises manage uncertainty in SCRM. The approach involves primary selection of alternative suppliers and establishment of a multiobjective mathematical model for dynamic supplier selection and order allocation.
Environmental factors are increasingly being considered in supply chain risk management (SCRM), which itself represents a growing trend. Accordingly, dynamic supplier selection and order allocation have become very important. Both qualitative and quantitative factors should be considered in the selection of eligible suppliers. In this study, a novel two-stage comprehensive mathematical model is developed for selecting a set of suppliers and assigning an order quantity to each supplier. The first stage involves a primary selection of alternative suppliers according to the risk value, which is determined using qualitative and quantitative methods based on the best-worst method, and the second stage involves establishing a multiobjective mathematical model for dealing with dynamic supplier selection and order allocation. The proposed approach, which helps enterprises manage uncertainty in SCRM, is applied in this study to the new energy vehicles industry.

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