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Supplier Selection Decisions: A Fuzzy Logic Model Based on Quality Aspects of Delivery

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Advances in Computational Intelligence (IPMU 2012)

Abstract

This paper presents a decision making model to address uncertainty in requirement planning. The model proposes a DSS to evaluate the quality of suppliers where quality is categorized into three primary areas dealing with delivery specifics, front office quality, and support specific quality. The application of the model is restricted to delivery specifics with two quality criteria illustrated of on-time delivery and accuracy of shipping. Results of the model provide ranking of suppliers based on belief that each supplier can provide average or greater performance. Extension of the model will determine overall fuzzy-set based rankings based upon all considered quality parameters.

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References

  1. Lui, B.: Fuzzy criterion models for inventory systems with partial backorders. Annals of Operations Research 87(1-4), 117–126 (1999)

    MathSciNet  Google Scholar 

  2. Das, K., Roy, T.K., Maiti, M.: Buyer-seller fuzzy inventory model for a deteriorating item with discount. International Journal of Systems Science 35(8), 457–466 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Usenik, J., Bogata, M.: A fuzzy set approach for a location-inventory model. Transportation Planning & Technology 28(6), 447–464 (2005)

    Article  Google Scholar 

  4. Pan, J.C.-H., Yang, M.-F.: Integrated inventory models with fuzzy annual demand and fuzzy production rate in a supply chain. International Journal of Production Research 46(3), 753–770 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Kara, Y., Gokcen, H., Atasagun, Y.: Balancing parallel assembly lines with precise and fuzzy goals. International Journal of Production Research 48(6), 1685–1703 (2010)

    Article  MATH  Google Scholar 

  6. Liang, T.-F.: Integrating production-transportation planning decision with fuzzy multiple goals in supply chains. International Journal of Production 46(6), 1477–1494 (2008)

    Article  MATH  Google Scholar 

  7. Tsai, W.-H., Hung, S.-J.: A fuzzy goal programming approach for green supply chain optimization under activity based costing and performance evaluation with a value-chain structure. International Journal of Production Research 47(18), 4991–5017 (2009)

    Article  MATH  Google Scholar 

  8. Shu, M.H., Wu, H.-C.: Measuring the manufacturing process yield based on fuzzy data. International Journal of Production Research 48(6), 1627–1638 (2010)

    Article  MATH  Google Scholar 

  9. Lau, H.C.W., Hui, I.K., Chan, F.T.S., Wong, C.W.Y.: Monitoring the supply of products in a supply chain environment: a fuzzy neural approach. Expert Systems 19(4), 235–243 (2002)

    Article  Google Scholar 

  10. Che, Z.H.: Using fuzzy analytic hierarchy process and particle swarm optimization for balanced and defective supply chain problems considering WEEE/RoHS directives. International Journal of Production Research 46(11), 3355–3381 (2010)

    Article  Google Scholar 

  11. Sen, C.G., Sen, S., Basligil, H.: Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology. International Journal of Production Research 48(6), 1603–1625 (2010)

    Article  MATH  Google Scholar 

  12. Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C., Choy, K.L.: Global supplier selection: a fuzzy AHP approach. International Journal of Production Research 46(14), 3825–3857 (2008)

    Article  MATH  Google Scholar 

  13. Chan, F.T.S., Kumar, N., Choy, K.L.: Decision-making approach for the distribution centre location problem in a supply chain network using the fuzzy-based hierarchical concept. Proceedings of the Institute of Mechanical Engineers-Part B- Engineering Manufacture 221(4), 725–739 (2007)

    Article  Google Scholar 

  14. Cigolini, R., Rossi, T.: Evaluating supply chain integration: A case study using fuzzy logic. Production Planning & Control 19(3), 242–255 (2008)

    Article  Google Scholar 

  15. Bevilacqua, M., Petroni, A.: From traditional purchasing to supplier management: A fuzzy logic-based approach to supplier selection. International Journal of Logistics: Research and Applications 5(3), 235–255 (2002)

    Google Scholar 

  16. Bayrak, M.Y., Celebi, N., Taskin, H.: A fuzzy approach for supplier selection. Production Planning & Control 18(1), 54–63 (2007)

    Article  Google Scholar 

  17. Jain, V., Wadhwa, S., Deshmukh, S.G.: Supplier selection using fuzzy association rules mining approach. International Journal of Production Research 45(6), 1323–1353 (2007)

    Article  MATH  Google Scholar 

  18. Sevkli, M.: An application of the fuzzy ELECTRE method for supplier selection. International Journal of Production Research 48(12), 3393–3405 (2010)

    Article  MATH  Google Scholar 

  19. Zadeh, L.: Generalized Theory of Uncertainty (GTU)-Principal Concepts and Ideas. Computational Statistics & Data Analysis 51(1), 15046 (2007)

    MathSciNet  Google Scholar 

  20. Bellman, R., Zadeh, L.: Decision making in a fuzzy environment. Management Science 17, 141–164 (1970)

    Article  MathSciNet  Google Scholar 

  21. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)

    MATH  Google Scholar 

  22. Freeling, A.: Fuzzy sets and decision analysis. IEEE Transactions on Systems, Man, and Cybernetics SMC-10, 1341–1354 (1980)

    Google Scholar 

  23. Yager, R.: On Some Classes of Implication Operators and Their Role in Approximate Reasoning. Information Sciences 167(1-4), 193–216 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  24. Kaufmann, A., Gupta, M.: An introduction to fuzzy sets arithmetic. Nosfrand Reinhold Co., New York (1985)

    Google Scholar 

  25. Klir, G., Folger, T.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  26. Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  27. Zadeh, L.: Fuzzy logic and approximate reasoning. Syntheses 30, 407–428 (1975)

    Article  MATH  Google Scholar 

  28. Dubois, D., Prade, H.: Decision making under fuzziness. In: Gupta, M., Ragade, R., Yager, R. (eds.) Advances in Fuzzy Set Theory and Applications. North Holland, Amsterdam (1979)

    Google Scholar 

  29. Zebda, A.: The investigation of cost variances: A fuzzy set theory approach. Decision Sciences 15, 359–389 (1984)

    Article  Google Scholar 

  30. Yager, R., Kreinovich, V.: Entropy Conserving Probability Transforms and the Entailment Principle. Fuzzy Sets & Systems 158(12), 1397–1405 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Shipley, M.F., Stading, G.L. (2012). Supplier Selection Decisions: A Fuzzy Logic Model Based on Quality Aspects of Delivery. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_1

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  • DOI: https://doi.org/10.1007/978-3-642-31724-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31723-1

  • Online ISBN: 978-3-642-31724-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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