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Research Papers

Understanding the Impact of Subjective Uncertainty on Architecture and Supplier Identification in Early Complex Systems Design

[+] Author and Article Information
Yun Ye

Laboratoire Génie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92 295 Châtenay-Malabry Cedex, France e-mail: inesye@gmail.com

Marija Jankovic

Laboratoire Génie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92 295 Châtenay-Malabry Cedex, France e-mail: marija.jankovic@ecp.fr

Gül E. Kremer

Engineering Design and Industrial Engineering, The Pennsylvania State University, 213T Hammond Building, University Park, PA 16802 e-mail: gkremer@psu.edu

1Corresponding author.

Manuscript received September 30, 2014; final manuscript received April 2, 2015; published online July 1, 2015. Assoc. Editor: Alba Sofi.

ASME J. Risk Uncertainty Part B 1(3), 031005 (Jul 01, 2015) (11 pages) Paper No: RISK-14-1068; doi: 10.1115/1.4030463 History: Received September 30, 2014; Accepted April 27, 2015; Online July 01, 2015

The Architecture and Supplier Identification Tool (ASIT) is a design support tool, which enables identification of the most suitable architectures and suppliers in early stages of complex systems design, with consideration of overall requirements satisfaction and uncertainty. During uncertainty estimation, several types of uncertainties that are essential in early design (i.e., uncertainty of modules due to new technology integration, compatibility between modules, and supplier performance uncertainty) have been considered in ASIT. However, it remains unclear whether uncertainty due to expert estimation should be taken into account. From one perspective, expert estimation uncertainty may significantly influence the overall uncertainty, since early complex systems design greatly depends on expert estimation; whereas an opposing perspective argues that expert estimation uncertainty should be neglected given its relatively much smaller scale. In order to understand how expert estimation uncertainty influences the architecture and supplier identification, a comprehensive study of possible modeling approaches has been discussed within the context of ASIT; type-1 fuzzy sets and 2-tuple fuzzy linguistic representation are selected to integrate subjective uncertainty into ASIT. A powertrain design case is used to compare results between cases considering subjective uncertainty versus cases not considering subjective uncertainty. Finally, implications of considering subjective uncertainty in early conceptual design are discussed.

Copyright © 2015 by ASME
Topics: Uncertainty , Design
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References

Ye, Y., Jankovic, M., Kremer, G., and Bocquet, J.-C., 2014, “Managing Uncertainty in Potential Supplier Identification,” Artif. Intell. Eng. Des. Anal. Manuf., 28(4), pp. 339–351. 10.1017/S0890060414000511
Nguyen Van, T., 2006, “System Engineering for Collaborative Data Management Systems: Application to Design/Simulation Loops,” Ecole Centrale Paris, Paris.
Airbus Group, 2014, “Airbus—Global Website—Our Suppliers,” Available: http://www.airbus-group.com/airbusgroup/int/en/our-company/Our-suppliers.html (accessed Apr. 14, 2014).
(Ray) Wang, J., 2001, “Ranking Engineering Design Concepts Using a Fuzzy Outranking Preference Model,” Fuzzy Sets Syst., 119(1), pp. 161–170. 10.1016/S0165-0114(99)00104-9
Zhai, L.-Y., Khoo, L.-P., and Zhong, Z.-W., 2009, “Design Concept Evaluation in Product Development Using Rough Sets and Grey Relation Analysis,” Expert Syst. Appl., 36(3), pp. 7072–7079. 10.1016/j.eswa.2008.08.068
Fiod-Neto, M., and Back, N., 1994, “Assessment of Product Conception: A Critical Review,” Proceedings of the 1994 Lancaster International Workshop on Engineering Design, Lancaster University, Engineering Design Centre, Lancaster.
Meyer, M. A., and Booker, J. M., 2001, Eliciting and Analyzing Expert Judgment: A Practical Guide, SIAM, Philadelphia.
Medsker, L., Tan, M., and Turban, E., 1995, “Knowledge Acquisition From Multiple Experts: Problems and Issues,” Expert Syst. Appl., 9(1), pp. 35–40. 10.1016/0957-4174(94)00046-X
Booker, J. M., Anderson, M. C., and Meyer, M. A., 2003, “The Role of Expert Knowledge in Uncertainty Quantification (Are We Adding More Uncertainty or More Understanding?),” Proceedings of the 9th Annual U.S. Army Conference on Applied Statistics, University of California, Davis, CA.
Dror, I. E., and Charlton, D., 2006, “Why Experts Make Errors,” J. Forensic Ident., 56(4), pp. 600–616.
Oberkampf, W. L., Helton, J. C., and Sentz, K., 2001, “Mathematical Representation of Uncertainty,” American Institute of Aeronautics and Astronautics Non-Deterministic Approaches Forum, Seattle, WA.
Ng, K.-C., and Abramson, B., 1990, “Uncertainty Management in Expert Systems,” IEEE Expert, 5(2), pp. 29–48. 10.1109/64.53180
Asadoorian, M. O., and Kantarelis, D., 2005, Essentials of Inferential Statistics, University Press of America, Lanham, MD.
Walley, P., 1991, Statistical Reasoning With Imprecise Probabilities, Chapman and Hall, London.
Coolen, F. P. A., 2004, “On the Use of Imprecise Probabilities in Reliability,” Qual. Reliab. Eng. Int., 20(3), pp. 193–202. 10.1002/(ISSN)1099-1638
Dempster, A. P., 1967, “Upper and Lower Probabilities Induced by a Multivalued Mapping,” Ann. Math. Statist., 38(2), pp. 325–339. 10.1214/aoms/1177698950
Shafer, G., 1976, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.
Zadeh, L. A., 1965, “Fuzzy Sets,” Inf. Control, 8(3), pp. 338–353. 10.1016/S0019-9958(65)90241-X
Dalalah, D., and Magableh, S., 2008, “Diagnosis and Management of Strep Throat: Remote Multicriteria Fuzzy Reasoning Approach,” Telemedicine Ehealth J., 14(7), pp. 656–665. 10.1089/tmj.2007.0120
Zadeh, L. A., 1999, “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets Syst., 100(Suppl. 1), pp. 9–34. [CrossRef]
Dubois, D. A., and Prade, H. M., 1988, Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, New York.
Cooman, G. D., Ruan, D., and Kerre, E. E., 1995, Foundations and Applications of Possibility Theory: Proceedings of FAPT ’95: Ghent, Belgium, Dec. 13–15, World Scientific Publishing Company, Incorporated, Hackensack, NJ.
Moore, R. E., 1979, Methods and Applications of Interval Analysis, SIAM, Philadelphia.
Kearfott, R. B., and Kreinovich, V., 1996, Applications of Interval Computations, Springer, Dordrecht.
Moore, R., and Lodwick, W., 2003, “Interval Analysis and Fuzzy Set Theory,” Fuzzy Sets Syst., 135(1), pp. 5–9. 10.1016/S0165-0114(02)00246-4
Pawlak, Z., 1982, “Rough Sets,” Int. J. Comput. Inf. Sci., 11(5), pp. 341–356. 10.1007/BF01001956
Pawlak, Z., 1997, “Rough Set Approach to Knowledge-Based Decision Support,” Eur. J. Oper. Res., 99(1), pp. 48–57. 10.1016/S0377-2217(96)00382-7
Chen, C.-T., Lin, C.-T., and Huang, S.-F., 2006, “A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management,” Int. J. Prod. Econ., 102(2), pp. 289–301. 10.1016/j.ijpe.2005.03.009
Boran, F. E., Genç, S., Kurt, M., and Akay, D., 2009, “A Multi-Criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection With TOPSIS Method,” Expert Syst. Appl., 36(8), pp. 11363–11368. 10.1016/j.eswa.2009.03.039
Kahraman, C., Cebeci, U., and Ulukan, Z., 2003, “Multi-Criteria Supplier Selection Using Fuzzy AHP,” Logist. Inf. Manage., 16(6), pp. 382–394. [CrossRef]
Haq, A. N., and Kannan, G., 2006, “Fuzzy Analytical Hierarchy Process for Evaluating and Selecting a Vendor in a Supply Chain Model,” Int. J. Adv. Manuf. Technol., 29(7–8), pp. 826–835.
Chan, F. T. S., Kumar, N., Tiwari, M. K., Lau, H. C. W., and Choy, K. L., 2008, “Global Supplier Selection: a Fuzzy-AHP Approach,” Int. J. Prod. Res., 46(14), pp. 3825–3857. 10.1080/00207540600787200
Bottani, E., and Rizzi, A., 2008, “An Adapted Multi-Criteria Approach to Suppliers and Products Selection—An Application Oriented to Lead-Time Reduction,” Int. J. Prod. Econ., 111(2), pp. 763–781. 10.1016/j.ijpe.2007.03.012
Vinodh, S., Anesh Ramiya, R., and Gautham, S. G., 2011, “Application of Fuzzy Analytic Network Process for Supplier Selection in a Manufacturing Organisation,” Expert Syst. Appl., 38(1), pp. 272–280. 10.1016/j.eswa.2010.06.057
Önüt, S., Kara, S. S., and Işik, E., 2009, “Long Term Supplier Selection Using a Combined Fuzzy MCDM Approach: A Case Study for a Telecommunication Company,” Expert Syst. Appl., 36(2), Part 2, pp. 3887–3895. 10.1016/j.eswa.2008.02.045
Amid, A., Ghodsypour, S. H., and O’Brien, C., 2006, “Fuzzy Multiobjective Linear Model for Supplier Selection in a Supply Chain,” Int. J. Prod. Econ., 104(2), pp. 394–407. 10.1016/j.ijpe.2005.04.012
Bayrak, M. Y., Çelebi, N., and Taşkin, H., 2007, “A Fuzzy Approach Method for Supplier Selection,” Prod. Plann. Control, 18(1), pp. 54–63. 10.1080/09537280600940713
Chou, S.-Y., and Chang, Y.-H., 2008, “A Decision Support System for Supplier Selection Based on a Strategy-Aligned Fuzzy SMART Approach,” Expert Syst. Appl., 34(4), pp. 2241–2253. 10.1016/j.eswa.2007.03.001
Bevilacqua, M., Ciarapica, F. E., and Giacchetta, G., 2006, “A Fuzzy-QFD Approach to Supplier Selection,” J. Purchasing Supply Manage., 12(1), pp. 14–27. 10.1016/j.pursup.2006.02.001
Dalalah, D., Hayajneh, M., and Batieha, F., 2011, “A Fuzzy Multi-Criteria Decision Making Model for Supplier Selection,” Expert Syst. Appl., 38(7), pp. 8384–8391. 10.1016/j.eswa.2011.01.031
Chen, S.-M., and Lee, L.-W., 2010, “Fuzzy Multiple Attributes Group Decision-Making Based on the Interval Type-2 TOPSIS Method,” Expert Syst. Appl., 37(4), pp. 2790–2798. 10.1016/j.eswa.2009.09.012
Wang, W.-P., 2010, “A Fuzzy Linguistic Computing Approach to Supplier Evaluation,” Appl. Math. Modell., 34(10), pp. 3130–3141. 10.1016/j.apm.2010.02.002
Mendel, J. M., and John, R. I. B., 2002, “Type-2 Fuzzy Sets Made Simple,” IEEE Trans. Fuzzy Syst., 10(2), pp. 117–127. 10.1109/91.995115
Zadeh, L. A., 1975, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—I,” Inf. Sci., 8(3), pp. 199–249. 10.1016/0020-0255(75)90036-5
Atanassov, K. T., 1986, “Intuitionistic Fuzzy Sets,” Fuzzy Sets Syst., 20(1), pp. 87–96. 10.1016/S0165-0114(86)80034-3
Herrera, F., and Martinez, L., 2000, “A 2-Tuple Fuzzy Linguistic Representation Model for Computing With Words,” IEEE Trans. Fuzzy Syst., 8(6), pp. 746–752. 10.1109/91.890332
Delgado, M., Verdegay, J. L., and Vila, M. A., 1993, “On Aggregation Operations of Linguistic Labels,” Int. J. Intell. Syst., 8(3), pp. 351–370. 10.1002/(ISSN)1098-111X
Gao, S., Zhang, Z., and Cao, C., 2009, “Multiplication Operation on Fuzzy Numbers,” J. Software, 4(4), p. 331. 10.4304/jsw.4.4.331-338
Ross, T. J., 2009, Fuzzy Logic with Engineering Applications, John Wiley & Sons, Hoboken, NJ.
Garibaldi, J. M., and John, R. I., 2003, “Choosing Membership Functions of Linguistic Terms,” Proceedings of the 12th IEEE International Conference on Fuzzy Systems, FUZZ ‘03, Vol. 1, pp. 578–583.
Pedrycz, W., 1994, “Why Triangular Membership Functions?” Fuzzy Sets Syst., 64(1), pp. 21–30. 10.1016/0165-0114(94)90003-5
Dubois, D. A., and Prade, H., 1978, “Operations on Fuzzy Numbers,” Int. J. Syst. Sci., 9(6), pp. 613–626. 10.1080/00207727808941724
Chang, D.-Y., 1996, “Applications of the Extent Analysis Method on Fuzzy AHP,” Eur. J. Oper. Res., 95(3), pp. 649–655. 10.1016/0377-2217(95)00300-2
Triantaphyllou, E., 2000, “Fuzzy Multi-Criteria Decision Making,” Multi-Criteria Decision Making Methods: A Comparative Study, Springer, Dordrecht, pp. 241–262.
Chiou, H.-K., Tzeng, G.-H., and Cheng, D.-C., 2005, “Evaluating Sustainable Fishing Development Strategies Using Fuzzy MCDM Approach,” Omega, 33(3), pp. 223–234. 10.1016/j.omega.2004.04.011
Tzeng, G.-H., and Huang, J.-J., 2011, Multiple Attribute Decision Making: Methods and Applications, CRC Press, Boca Raton.
Runkler, T. A., 1997, “Selection of Appropriate Defuzzification Methods Using Application Specific Properties,” Trans. Fuzzy Syst., 5(1), pp. 72–79. 10.1109/91.554449
Serway, R., and Jewett, J., 2013, Physics for Scientists and Engineers with Modern Physics, Cengage Learning, Boston.

Figures

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Fig. 2

Generation of all possible architectures

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Function satisfaction level by modules

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Requirement-function relations

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Requirements satisfaction (without considering expert uncertainty)

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Uncertainty information

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Uncertainty (without considering expert uncertainty)

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Fuzzy numbers for satisfaction levels

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Requirements satisfaction by using type-1 fuzzy sets (values)

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Requirements satisfaction by using type-1 fuzzy sets (membership functions)

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Fuzzy membership function for possibilities

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Uncertainty using type-1 fuzzy sets (values)

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Uncertainty using type-1 fuzzy sets (membership functions)

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Fuzzy results that are partly above the threshold

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Using α-cut to represent the tolerance level

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Requirements satisfaction after defuzzification

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Uncertainty after defuzzification

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Requirements satisfaction using 2-tuple linguistic representation

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Uncertainty using 2-tuple fuzzy linguistic representation

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Fig. 20

Comparison of supplier identification results

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Fig. 21

Changing thresholds without considering subjective uncertainty

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