Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

Gumus A. T., GÜNERİ A. F., Keles S.

EXPERT SYSTEMS WITH APPLICATIONS, vol.36, no.10, pp.12570-12577, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 10
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2009.05.034
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.12570-12577
  • Keywords: Supply chain network design, Neuro-fuzzy, Mixed integer linear programming, Artificial neural networks, MULTIECHELON INVENTORY MANAGEMENT, PROGRAMMING APPROACH, DEMAND, CONFIGURATION, FRAMEWORK, ALGORITHM, SELECTION, SYSTEMS, ISSUES, MODEL
  • Yıldız Technical University Affiliated: Yes


In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving. (C) 2009 Elsevier Ltd. All rights reserved.