Abstract
The international logistics centers choice problem is a very important issue in International logistics. The location choice problem usually involves numbers and words in which all of the criteria are weighted using words and the performance evaluations for all sub-criteria are either numbers or words. How to aggregate all of these data without losing information is a very daunting task using a type-1 fuzzy set (T1 FS) approach. This paper applies a new methodology—Perceptual Computer (Per-C)—to help solve this hierarchical multi-person multi-criteria decision making problem. The Per-C has three components: encoder, computing with words (CWW) engine and decoder. First, the interval approach (IA) is used to obtain interval type-2 fuzzy set (IT2 FS) word models for the words in a pre-specified vocabulary. Second, a linguistic weighted average (LWA) is used to aggregate all the data including numbers and words modeled by IT2 FSs. Finally, a centroid-based ranking method is used to rank the location choices, and a similarity measure is used to obtain similarities of the location choices. The decision-maker decides the winning location choice as the one with the highest ranking and least similarity to other locations.
Similar content being viewed by others
References
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141–164.
Chen, C. T. (2001). A fuzzy approach to select the location of the distribution center. Fuzzy Sets and Systems, 118(1), 65–73.
Chen, Y. C. (2002). An application of fuzzy set theory to the external performance evaluation of distribution centers in logistics. Soft Computing, 6, 64–70.
Chou, C. C. (2010). An integrated quantitative and qualitative FMCDM model for location choices. Soft Computing, 14(7), 757–771.
Contreras, A. I., & Díaz, J. A. (2008). Scatter search for the single source capacitated facility location problem. Annals of Operation Research, 157, 73–89.
Correia, I., & Captivo, M. E. (2003). A Lagrangean heuristic for a modular capacitated location problem. Annals of Operation Research, 122, 141–161.
Deng, Y. (2006). Plant location selection based on fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 28, 839–844.
Deng, Y., & Cheng, S. (2006). Evaluating the main battle tank using fuzzy number arithmetic operations. Defence Science Journal, 56, 251–257.
Hsu, H. M., & Chen, C. T. (2002). Fuzzy credibility relation method for multiple criteria decision-making problems. Information Sciences, 96, 79–91.
Hwang, C. L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin: Springer.
Karmik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information Sciences, 132(1–4), 195–220.
Kim, C. S., Kim, D. S., & Park, J. S. (2000). A new fuzzy resolution principle based on the antonym. Fuzzy Sets and Systems, 113, 299–307.
Liang, G. S., & Wang, M. J. (1991). A fuzzy multiple criteria decision making method for facilities site selection. International Journal of Production Research, 29(11), 2313–2330.
Liu, F., & Mendel, J. M. (2008a). Encoding words into interval type-2 fuzzy sets using an interval sets. IEEE Transactions on Fuzzy Systems, 16, 1503–1521.
Liu, F., & Mendel, J. M. (2008b). Aggregation using the fuzzy weighted average, as computed by the KM algorithms. IEEE Transactions on Fuzzy Systems, 16, 1–12.
Mendel, J. M. (2001). Uncertain rule-based fuzzy logic system: introduction and new directions. Upper Saddle River: Prentice-Hall.
Mendel, J. M. (2002). An architecture for making judgments using computing with words. International Journal of Applied Mathematics and Computer Science, 12(3), 325–335.
Mendel, J. M. (2003). Fuzzy sets for words: a new beginning. In Proc. FUZZY-IEEE 2003 (pp. 37–42). St. Louis, MO.
Mendel, J. M. (2007). Type-2 fuzzy sets and systems: an overview. IEEE Computational Intelligence Magazine, 2, 20–29.
Mendel, J. M. (2007). Computing with words and its relationships with fuzzistics. Information Sciences, 177, 998–1006.
Mendel, J. M., & Wu, D. (2010). Perceptual computing: aiding people in making subjective judgments. New York: Wiley-IEEE Press.
Resende, M. G., & Werneck, R. F. (2007). A fast swap-based local search procedure for location problems. Annals of Operation Research, 150, 205–230.
Rietveld, P., & Ouwersloot, H. (1992). Ordinal data in multi-criteria decision making, a stochastic dominance approach to sitting nuclear power plants. European Journal of Operational Research, 56, 249–262.
Soto, A. D., & Trillas, E. (1999). On antonym and negate in fuzzy logic. International Journal of Intelligent Systems, 17, 295–303.
Villegas, J. G., Palacios, F., & Medaglia, A. L. (2006). Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example. Annals of Operation Research, 147, 109–141.
Wu, D., & Mendel, J. M. (2007). Enhanced Karnik-Mendel algorithms. In Proc. NAFIPS 2007, San Diego, CA.
Wu, D., & Mendel, J. M. (2007). Aggregation using the linguistic weighted average and interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 15(6), 1145–1161.
Wu, D., & Mendel, J. M. (2008). Corrections to aggregation using the linguistics weighted average and interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 16(6), 1664–1666.
Wu, D., & Mendel, J. M. (2009). Enhanced Karnik-Mendel algorithms. IEEE Transactions on Fuzzy Systems, 17(4), 923–934.
Wu, D., & Mendel, J. M. (2009). A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets. Information Sciences, 179(8), 1169–1192.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8, 199–249.
Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 4, 103–111.
Zadeh, L. A. (1999). From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems. I, Fundamental Theory and Applications, 4, 105–119.
Zadeh, L. A. (2001). A new direction in AI-Toward a computational theory of perceptions. The AI Magazine, 22(1), 73–84.
Zadeh, L. A. (2005). Toward a generalized theory of uncertainty (GTU)—An outline. Information Sciences, 172, 1–40.
Zadeh, L. A. (2008). Toward human level machine intelligence—is it achievable? The need for a new paradigm shift. IEEE Computational Intelligence Magazine, 3, 11–22.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Han, S., Mendel, J.M. A new method for managing the uncertainties in evaluating multi-person multi-criteria location choices, using a perceptual computer. Ann Oper Res 195, 277–309 (2012). https://doi.org/10.1007/s10479-011-0956-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-011-0956-6