Supplier selection and purchase problem for multi-echelon defective supply chain system with stochastic demand


Senyigit E.

NEURAL COMPUTING & APPLICATIONS, cilt.22, ss.403-415, 2013 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 22 Konu: 2
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s00521-011-0704-5
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Sayfa Sayıları: ss.403-415

Özet

In the real production process, some members in the supply chain system sometimes cannot effectively complete their production task because of defects involving the production or purchasing of components. A supply chain system that has defects in at least one echelon is called a multi-echelon defective supply chain (MDSC) system. Most supply chain systems are MDSC systems. Determining parts or components supply quota from different suppliers with limited suppliers, factories and distribution centers capacities in the supply chain system are becoming an important issue for businesses. In this study, we propose a new heuristic (H2) which is an extension of H1 heuristic that was previously presented. The MDSC system was formed with the mixed integer linear programming by LINDO software for calculation of the lower bound. The heuristics and MDSC system were modeled by using ProModel software. The heuristics were applied to a case from the Turkish furniture industry. The heuristics were compared with each other by considering different coefficients of variation, service levels, and deviation from lower bound. Simulation experiments showed that the proposed H2 heuristic outperformed the H1 heuristic.
In the real production process, some members in the supply chain system sometimes cannot effectively complete their production task because of defects involving the production or purchasing of components. A supply chain system that has defects in at least one echelon is called a multi-echelon defective supply chain (MDSC) system. Most supply chain systems are MDSC systems. Determining parts or components supply quota from different suppliers with limited suppliers, factories and distribution centers capacities in the supply chain system are becoming an important issue for businesses. In this study, we propose a new heuristic (H2) which is an extension of H1 heuristic that was previously presented. The MDSC system was formed with the mixed integer linear programming by LINDO software for calculation of the lower bound. The heuristics and MDSC system were modeled by using ProModel software. The heuristics were applied to a case from the Turkish furniture industry. The heuristics were compared with each other by considering different coefficients of variation, service levels, and deviation from lower bound. Simulation experiments showed that the proposed H2 heuristic outperformed the H1 heuristic.