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
Lui, B.: Fuzzy criterion models for inventory systems with partial backorders. Annals of Operations Research 87(1-4), 117–126 (1999)
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)
Usenik, J., Bogata, M.: A fuzzy set approach for a location-inventory model. Transportation Planning & Technology 28(6), 447–464 (2005)
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)
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)
Liang, T.-F.: Integrating production-transportation planning decision with fuzzy multiple goals in supply chains. International Journal of Production 46(6), 1477–1494 (2008)
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)
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)
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)
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)
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)
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)
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)
Cigolini, R., Rossi, T.: Evaluating supply chain integration: A case study using fuzzy logic. Production Planning & Control 19(3), 242–255 (2008)
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)
Bayrak, M.Y., Celebi, N., Taskin, H.: A fuzzy approach for supplier selection. Production Planning & Control 18(1), 54–63 (2007)
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)
Sevkli, M.: An application of the fuzzy ELECTRE method for supplier selection. International Journal of Production Research 48(12), 3393–3405 (2010)
Zadeh, L.: Generalized Theory of Uncertainty (GTU)-Principal Concepts and Ideas. Computational Statistics & Data Analysis 51(1), 15046 (2007)
Bellman, R., Zadeh, L.: Decision making in a fuzzy environment. Management Science 17, 141–164 (1970)
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)
Freeling, A.: Fuzzy sets and decision analysis. IEEE Transactions on Systems, Man, and Cybernetics SMC-10, 1341–1354 (1980)
Yager, R.: On Some Classes of Implication Operators and Their Role in Approximate Reasoning. Information Sciences 167(1-4), 193–216 (2004)
Kaufmann, A., Gupta, M.: An introduction to fuzzy sets arithmetic. Nosfrand Reinhold Co., New York (1985)
Klir, G., Folger, T.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, Englewood Cliffs (1988)
Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Zadeh, L.: Fuzzy logic and approximate reasoning. Syntheses 30, 407–428 (1975)
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)
Zebda, A.: The investigation of cost variances: A fuzzy set theory approach. Decision Sciences 15, 359–389 (1984)
Yager, R., Kreinovich, V.: Entropy Conserving Probability Transforms and the Entailment Principle. Fuzzy Sets & Systems 158(12), 1397–1405 (2007)
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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
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