Application of FUZZY-AHP for Industrial Cluster Identification

  • Netsanet Jote
  • Daniel Kitaw
  • Jakub Štolfa
  • Svatopluk Štolfa
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


Identifying industrial cluster has become a key strategic decision, during recent years. However, the nature of these decisions is usually uncertain and vague. From the existing methods, there is no single method which handles the uncertainty. This paper proposes a Fuzzy-AHP based industrial cluster identification model to solve the pitfalls with the exiting cluster identification methods. As a result, quantitative and qualitative factors including geographical proximity, sectorial concentration, market potential, support services, resource potential and potential entrepreneurs are found to be critical factors in cluster identification. In this paper, linguistic values are used to assess the ratings and weights of the factors. Then, AHP model based on fuzzy-sets theory will be proposed in dealing with the cluster selection problems. Finally, Ethiopian Tanning industries were taken to prove and validate the procedure of the proposed method. A sensitivity analysis is also performed to justify the results.


Fuzzy-AHP Industrial cluster Cluster identification 


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  1. 1.
    Andersen, T., Bjerre, M., Emily, W.H.: The cluster benchmarking project: pilot project report-benchmarking clusters in the knowledge based economy. Nordic Innovation center, For A (2006)Google Scholar
  2. 2.
    Bergman, E.M., Feser, E.J.: Industrial and regional clusters: concepts and comparative applications. WVU Regional Research Institute, Virginia (1999)Google Scholar
  3. 3.
    Choua, Y.C., Sunb, C.C., Yenc, H.Y.: Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach. Applied Soft Computing 12, 64–71 (2011)CrossRefGoogle Scholar
  4. 4.
    Choudhary, D., Shankar, R.: An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy 42, 510–521 (2012)CrossRefGoogle Scholar
  5. 5.
    Durán, O.: Computer-aided maintenance management systems selection based on a fuzzy AHP approach. Advances in Engineering Software 42, 821–829 (2011)CrossRefGoogle Scholar
  6. 6.
    Hofe, R.V., Bhatta, S.D.: Method for identifying and domestic industrial clusters using interregional commodity trade data. The Industrial Geographer 4, 1–27 (2007)Google Scholar
  7. 7.
    Isaai, M.T., Kanani, A., Tootoonchi, M., Afzali, H.R.: Intelligent timetable evaluation using fuzzy AHP. Expert Systems with Applications 38, 3718–3723 (2011)CrossRefGoogle Scholar
  8. 8.
    Javanbarg, M.B., Scawthorn, C., Kiyono, J., Shahbodaghkhan, B.: Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Systems with Applications 39, 960–966 (2011)CrossRefGoogle Scholar
  9. 9.
    Kilincci, O., Onal, S.A.: Fuzzy AHP approach for supplier selection in a washing machine company. Expert systems with Applications 38, 9656–9664 (2011)CrossRefGoogle Scholar
  10. 10.
    Netsanet, J., Birhanu, B., Daniel, K., Abraham, A.: AHP-Based Micro and Small Enterprises’ Cluster Identification. In: Fifth International Conference on Soft Computing and Pattern Recognition (2013)Google Scholar
  11. 11.
    Pedro, C.O., Hélcio, M.T., Márcio, L.P.: Relationships, cooperation and development in a Brazilian industrial cluster. International Journal of Productivity and Performance Management 60, 115–131 (2011)CrossRefGoogle Scholar
  12. 12.
    Shamsuzzaman, M., Ullah, A.M.M.S., Bohez, L.J.: Applying linguistic criteria in FMS selection: fuzzy-set-AHP approach 3, 247–254 (2003)Google Scholar
  13. 13.
    Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S.: Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications 39, 8182–8192 (2012)CrossRefGoogle Scholar
  14. 14.
    Stejskal, J.: Comparison of often applied methods for industrial cluster identification. In: Development, Energy, Environment, Economics, pp. 282–286 (2010)Google Scholar
  15. 15.
    Tetsushi, S., Keijiro, O.: Strategy for cluster-based industrial development in developing countries. Foundation for advanced studies on international development and national graduate institute for policy studies (2006)Google Scholar
  16. 16.
    USAID: Agricultural Growth Project -Livestock Market Development Value Chain Analysis for Ethiopia: Meat and Live Animals, Hides, Skins and Leather, Dairy, AGP-Livestock Market Development Project (2013)Google Scholar
  17. 17.
    Wang, Y.M., Chin, K.S.: Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. International Journal of Approximate Reasoning 52, 541–553 (2010)CrossRefGoogle Scholar
  18. 18.
    Yoo, K.Y.: Method for identifying industry clusters: assessment of the state of the art. University of North Carolina, Chapel Hill (2003)Google Scholar
  19. 19.
    Zheng, G., Zhu, N., Tian, Z., Chen, Y., Sun, B.: Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety Science 50, 228–239 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Netsanet Jote
    • 1
  • Daniel Kitaw
    • 1
  • Jakub Štolfa
    • 2
  • Svatopluk Štolfa
    • 2
  • Václav Snášel
    • 2
  1. 1.Addis Ababa Institute of Technology, School of Mechanical and Industrial EngineeringAddis AbabaEthiopia
  2. 2.Department of Computer ScienceVSB - Technical University of OstravaOstrava-PorubaCzech Republic

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