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Fuzzy set theory applications in production management research: a literature survey

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Abstract

Fuzzy set theory has been used to model systems that are hard to define precisely. As a methodology, fuzzy set theory incorporates imprecision and subjectivity into the model formulation and solution process. Fuzzy set theory represents an attractive tool to aid research in production management when the dynamics of the production environment limit the specification of model objectives, constraints and the precise measurement of model parameters. This paper provides a survey of the application of fuzzy set theory in production management research. The literature review that we compiled consists of 73 journal articles and nine books. A classification scheme for fuzzy applications in production management research is defined. We also identify selected bibliographies on fuzzy sets and applications.

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References

  • Bhattacharya, U., Rao, J. R. and Tiwari, R. N. (1992) Fuzzy multi-criteria facility location problem. Fuzzy Sets and Systems, 51(3), 277–287.

    Google Scholar 

  • Bhattacharya, U., Rao, J. R. and Tiwari, R. N. (1993) Bi-criteria multi facility location problem in fuzzy environment. Fuzzy Sets and Systems, 56(2), 145–153.

    Google Scholar 

  • Bradshaw, C. W. (1983) A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research, 13(4), 403–408.

    Google Scholar 

  • Buckley, J. J. (1989) Fuzzy PERT, in Applications of Fuzzy Set Methodologies in Industrial Engineering, Evans, G. W., Karwowski, W. and Wilhelm, M. R. (eds), Elsevier Science, Amsterdam, pp. 103–114.

    Google Scholar 

  • Chakraborty, T. K. (1988) A single sampling attribute plan of given strength based on fuzzy goal programming. Opsearch, 25(4), 259–271.

    Google Scholar 

  • Chakraborty, T. K. (1992) A class of single sampling plans based on fuzzy optimization. Opsearch, 29(1), 1120.

    Google Scholar 

  • Chakraborty, T. K. (1994a) Possibilistic parameter single sampling inspection plans. Opsearch, 31(2), 108–126.

    Google Scholar 

  • Chakraborty, T. K. (1994b) A class of single sampling inspection plans based on possibilistic programming problem. Fuzzy Sets and Systems, 63(1), 35–43.

    Google Scholar 

  • Chanas, S. and Kamburowski, J. (1981) The use of fuzzy variables in PERT. Fuzzy Sets and Systems, 5(1), 11–19.

    Google Scholar 

  • Chang, I. S., Tsujimura, Y., Gen, M. and Tozawa, T. (1995) An effcient approach for large scale project planning based on fuzzy Delphi method. Fuzzy Sets and Systems,76(2),277–288.

    Google Scholar 

  • Chen, S.-M. (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.

    Google Scholar 

  • Chung, K. and Tcha, D. (1992) A fuzzy set-theoretic method for public facility location. European Journal of Operational Research, 58(1), 90–98.

    Google Scholar 

  • Cummins, J. D. and Derrig, R. A. (1993) Fuzzy trends in property-liability insurance claim costs. Journal of Risk and Insurance, 60(3), 429–465.

    Google Scholar 

  • Darzentas, J. (1987) A discrete location model with fuzzy acces-sibility measures. Fuzzy Sets and Systems, 23(1), 149–154.

    Google Scholar 

  • DePorter, E. L. and Ellis, K. P. (1990) Optimization of project networks with goal programming and fuzzy linear programming. Computers and Industrial Engineering, 19(1–4), 500–504.

    Google Scholar 

  • Dubois, D. and Prade, H. (1985) Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, New York.

    Google Scholar 

  • Dweiri, F. and Meier, F. A. (1996) Application of fuzzy decision-making in facilities layout planning. International Journal of Production Research, 34(11), 3207–3225.

    Google Scholar 

  • Economakos, E. (1979) Application of fuzzy concepts to power demand forecasting. IEEE Transactions on Systems, Man and Cybernetics, 9(10), 651–657.

    Google Scholar 

  • Evans, G. W., Wilhelm, M. R. and Karwowski, W. (1987) A layout design heuristic employing the theory of fuzzy sets. International Journal of Production Research, 25(10), 1431–1450.

    Google Scholar 

  • Gaines, B. R. and Kohout, L. J. (1977) The fuzzy decade: a bib-liography of fuzzy systems and closely related topics. International Journal of Man-Machine Studies, 9(1), 1–68.

    Google Scholar 

  • Gen, M., Tsujimura, Y. and Ida, K. (1992) Method for solving multiobjective aggregate production planning problem with fuzzy parameters. Computers and Industrial Engineering, 23(1–4), 117–120.

    Google Scholar 

  • Glushkovsky, E. A. and Florescu, R. A. (1996) Fuzzy sets ap-proach to quality improvement. Quality and Reliability Engineering International, 12(1), 27–37.

    Google Scholar 

  • Grabot, B. and Geneste, L. (1994) Dispatching rules in schedul-ing: a fuzzy approach. International Journal of Production Research, 32(4), 903–915.

    Google Scholar 

  • Grobelny, J. (1987a) On one possible ‘fuzzy’ approach to facilities layout problems. International Journal of Production Research, 25(8), 1123–1141.

    Google Scholar 

  • Grobelny, J. (1987b) The fuzzy approach to facilities layout problems. Fuzzy Sets and Systems, 23(2), 175–190.

    Google Scholar 

  • Gutierrez, I. and Carmona, S. (1995) Ambiguity in multicriteria quality decisions. International Journal of Production Economics, 38(2/3), 215–224.

    Google Scholar 

  • Han, S., Ishii, H. and Fujii, S. (1994) One machine scheduling problem with fuzzy due dates. European Journal of Operational Research, 79(1), 1–12.

    Google Scholar 

  • Hapke, M., Jaszkiewicz, A. and Slowinski, R. (1994) Fuzzy pro-ject scheduling system for software development. Fuzzy Sets and Systems, 67(1), 101–117.

    Google Scholar 

  • Heshmaty, B. and Kandel, A. (1985) Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets and Systems, 15, 159–191.

    Google Scholar 

  • Inuiguchi, M., Sakawa, M. and Kume, Y. (1994) The usefulness of possibilistic programming in production planning problems. International Journal of Production Economics, 33(1–3), 45–52.

    Google Scholar 

  • Ishibuchi, H., Yamamoto, N., Misaki, S. and Tanaka, H. (1994) Local search algorithms for flow shop scheduling with fuzzy due-dates. International Journal of Production Economics, 33, 53–66.

    Google Scholar 

  • Ishii, H. and Tada, M. (1995) Single machine scheduling problem with fuzzy precedence relation. European Journal of Operational Research, 87(2), 284–288.

    Google Scholar 

  • Ishii, H., Tada, M. and Masuda, T. (1992) Two scheduling problems with fuzzy due-dates. Fuzzy Sets and Systems, 46(3), 339–347.

    Google Scholar 

  • Ishikawa, A., Amagasa, M., Tomizawa, G., Tatsuta, R. and Mieno, H. (1993) The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55(3), 241–253.

    Google Scholar 

  • Kacprzyk, J. and Staniewski, P. (1982) Long-term inventory policy-making through fuzzy decision-making models. Fuzzy Sets and Systems, 8(2), 117–132.

    Google Scholar 

  • Kanagawa, A. and Ohta, H. (1990) A design for single sampling attribute plan based on fuzzy sets theory. Fuzzy Sets and Systems, 37(2), 173–181.

    Google Scholar 

  • Kanagawa, A., Tamaki, F. and Ohta, H. (1993) Control charts for process average and variability based on linguistic data. International Journal of Production Research, 31(4), 913–922.

    Google Scholar 

  • Kandel, A. (1986) Fuzzy Mathematical Techniques with Applica-tions, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Kandel, A. and Yager, R. (1979) A 1979 bibliography on fuzzy sets, their applications, and related topics, in Advances in Fuzzy Set Theory and Applications, Gupta, M. M., Ragade, R. K. and Yager, R. R.(eds), North-Holland, Amsterdam, pp. 621–744.

    Google Scholar 

  • Karwowski, W. and Evans, G. W. (1986) Fuzzy concepts in production management research: a review. International Journal of Production Research, 24(1), 129–147.

    Google Scholar 

  • Kaufmann, A. (1986) On the relevance of fuzzy sets for opera-tions research. European Journal of Operational Research, 25, 330–335.

    Google Scholar 

  • Kaufmann, A. and Gupta, M. M. (1988) Fuzzy Mathematical Models in Engineering and Management Science, North-Holland, Amsterdam.

    Google Scholar 

  • Khoo, L. P. and Ho, N. C. (1996) Framework of a fuzzy quality deployment system. International Journal of Production Re-search, 34(2), 299–311.

    Google Scholar 

  • Lai, Y.-J. and Hwang, C.-L. (1994) Fuzzy Multiple Objective Decision Making Methods and Applications, Springer-Verlag, Berlin.

    Google Scholar 

  • Lee, Y. Y., Kramer, B. A. and Hwang, C. L. (1990) Part-period balancing with uncertainty: a fuzzy sets theory approach. International Journal of Production Research, 28(10), 1771–1778.

    Google Scholar 

  • Lee, Y. Y., Kramer, B. A. and Hwang, C. L. (1991) A com-parative study of three lot-sizing methods for the case of fuzzy demand. International Journal of Operations and Production Management, 11(7), 72–80.

    Google Scholar 

  • Lehtimaki, A. K. (1987) An approach for solving decision problems of master scheduling by utilizing theory of fuzzy sets. International Journal of Operations and Production Management, 25(12), 1781–1793.

    Google Scholar 

  • Lootsma, F. A. (1989) Stochastic and fuzzy PERT. European Journal of Operational Research, 43(2), 174–183.

    Google Scholar 

  • Lorterapong, P. (1994) A fuzzy heuristic method for resource-constrained project scheduling. Project Management Journal, 25(4), 12–18.

    Google Scholar 

  • Maiers, J. and Sherif, Y. S. (1985) Applications of fuzzy set theory. IEEE Transactions on Systems, Man and Cybernetics, 15(1), 175–189.

    Google Scholar 

  • McCahon, C. S. (1993) Using PERT as an approximation of fuzzy project-network analysis. IEEE Transactions on Engineering Management, 40(2), 146–153.

    Google Scholar 

  • McCahon, C. S. and Lee, E. S. (1988) Project network analysis with fuzzy activity times. Computers and Mathematics with Applications, 15(10), 829–838.

    Google Scholar 

  • McCahon, C. S. and Lee, E. S. (1990) Job sequencing with fuzzy processing times. Computers and Mathematics with Applications, 19(7), 31–41.

    Google Scholar 

  • McCahnon, C. S. and Lee, E. S. (1992) Fuzzy job sequencing for a flow shop. European Journal of Operational Research, 62(3), 294–301.

    Google Scholar 

  • Mital, A. and Karwowski, W. (1989) A framework of the fuzzy linguistic approach to facilities location problem, in Applications of Fuzzy Set Methodologies in Industrial Engineering, Evans, G. W., Karwowski, W. and Wilhelm, M. R.(eds), Elsevier Science Publishers B. V., Amsterdampp. 323–330.

    Google Scholar 

  • Mital, A., Kromodihardjo, S., Metha, M. and Karwowski, W. (1988) Facilities location: quantifying subjective criteria using fuzzy linguistic approach, in Recent Developments in Production Research, Mital, A.(ed.), Elsevier Science Publishers B. V., Amsterdam, pp. 307–314.

    Google Scholar 

  • Murray, T. J., Pipino, L. L. and vanGigch, J. P. (1985) A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5(1), 76–80.

    Google Scholar 

  • Narasimhan, R. (1979) A fuzzy subset characterization of a site-selection problem. Decision Sciences, 10(4), 618–628.

    Google Scholar 

  • Nasution, S. H. (1994) Fuzzy critical path. IEEE Transactions on Systems, Man and Cybernetics, 24(1), 48–57.

    Google Scholar 

  • Negoita, C. V. (1981) The current interest in fuzzy optimization. Fuzzy Sets and Systems, 6(3), 261–269.

    Google Scholar 

  • Ohta, H. and Ichihashi, H. (1988) Determination of single-sampling-attribute plans based on membership functions. International Journal of Production Research, 26(9), 1477–1485.

    Google Scholar 

  • Park, K. S. (1987) Fuzzy-set theoretic interpretation of economic order quantity. IEEE Transactions on Systems, Man and Cybernetics, 17(6), 1082–1084.

    Google Scholar 

  • Prade, H. (1979) Using fuzzy set theory in a scheduling problem: a case study. Fuzzy Sets and Systems,2(2), 153–165.

    Google Scholar 

  • Raoot, A. and Rakshit, A. (1991) A ‘fuzzy’ approach to facilities layout planning. International Journal of Production Research, 29(4), 835–857.

    Google Scholar 

  • Raoot, A. and Rakshit, A. (1993) A “lingusitic pattern” approach for multiple criteria facility layout problems. International Journal of Production Research, 31(1), 203–222.

    Google Scholar 

  • Raoot, A. and Rakshit, A. (1994) A ‘fuzzy’ heuristic for the quadratic assignment formulation to the facility layout problem. International Journal of Production Research, 32(3), 563–581.

    Google Scholar 

  • Raz, T. and Wang, J. (1990) Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning and Control, 1(3), 147–157.

    Google Scholar 

  • Rinks, D. B. (1981) A heuristic approach to aggregate production scheduling using linguistic variables, in Applied Systems and Cybernetics, Vol. VI, Lasker, G. E (ed.), Pergamon Press, New York, pp. 2877–2883.

    Google Scholar 

  • Rinks, D. B. (1982a) The performance of fuzzy algorithm models for aggregate planning under differing cost structures, in Fuzzy Information and Decision Processes, Gupta, M. M. and Sanchez, E.(eds), North-Holland Publishing, Amsterdam, pp. 267–278.

    Google Scholar 

  • Rinks, D. B. (1982b) A heuristic approach to aggregate planning production scheduling using linguistic variables: methodology and application, in Fuzzy Set and Possibility Theory, Yager, R. R.(ed.), Pergamon Press, New York, pp. 562–581.

    Google Scholar 

  • Roy, U. and Zhang, X. (1996) A heuristic approach to n/m job shop scheduling fuzzy dynamic scheduling algorithms. Production Planning and Control, 7(3), 299–311.

    Google Scholar 

  • Shipley, M. F., De Korvin, A. and Omer, K. (1996) A fuzzy logic approach for determining expected values: a project management application. Journal of the Operational Research Society, 47(4), 562–569.

    Google Scholar 

  • Shnaider, E. and Kandel, A. (1989) The use of fuzzy set theory for forecasting corporate tax revenues. Fuzzy Sets and Systems, 31(2), 187–204.

    Google Scholar 

  • Singh, N. and Mohanty, B. K. (1991) A fuzzy approach to multi-objective routing problem with applications to process planning in manufacturing systems. International Journal of Production Research, 29(6), 1161–1170.

    Google Scholar 

  • Sommer, G. (1981) Fuzzy inventory scheduling in Applied Systems, in Applied Systems and Cybernetics, Vol. VI, Lasker, G. E.(ed.), Pergamon Press, New York, pp. 3052–3060.

    Google Scholar 

  • Song, Q. and Chissom, B. S. (1993a) Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277.

    Google Scholar 

  • Song, Q. and Chissom, B. S. (1993b) Forecasting enrollments with fuzzy time series part I. Fuzzy Sets and Systems, 54(1), 1–9.

    Google Scholar 

  • Song, Q. and Chissom, B. S. (1994) Forecasting enrollments with fuzzy time series-part II. Fuzzy Sets and Systems, 62(1), 1–8.

    Google Scholar 

  • Song, Q., Leland, R. P. and Chissom, B. S. (1995) A new fuzzy time-series model of fuzzy number observations. Fuzzy Sets and Systems, 73(3), 34–348.

    Google Scholar 

  • Sullivan, J. and Woodall, W. H. (1994) A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems, 64(3), 279–293.

    Google Scholar 

  • Tanaka, H., Uejima, S. and Asai, K. (1982) Linear regression analysis with fuzzy model. IEEE Transactions on Systems, Man and Cybernetics, 12(6), 903–907.

    Google Scholar 

  • Tsujimura, Y., Park, S. H., Chang, I. S. and Gen, M. (1993) An effective method for solving flow shop scheduling problems with fuzzy processing times. Computers and Industrial Engineering, 25(1–4), 239–242.

    Google Scholar 

  • Turksen, I. B. (1988a) Approximate reasoning for production planning. Fuzzy Sets and Systems, 26(1), 23–37.

    Google Scholar 

  • Turksen, I. B. (1988b) An approximate reasoning framework for aggregate production planning, in Computer Integrated Manufacturing, NATO ASI Series, Vol. F. 49, Turksen, I. B. (ed.), Springer-Verlag, Berlin, pp. 243–266.

    Google Scholar 

  • Turksen, I. B. (1992) Fuzzy expert systems for IE/OR/MS. Fuzzy Sets and Systems, 51(1), 1–27.

    Google Scholar 

  • Wang, J.H. and Raz, T. (1990) On the construction of control charts using linguistic variables. International Journal of Production Research, 28(3), 477–487.

    Google Scholar 

  • Wang, R.-C. and Chen, C.-H. (1995) Economic statistical np-control chart designs based on fuzzy optimization. International Journal of Quality and Reliability Management, 12(1), 82–92.

    Google Scholar 

  • Ward, T. L., Ralston, P. A. S. and Davis, J. A. (1992) Fuzzy logic control of aggregate production planning. Computers and Industrial Engineering, 23(1–4), 137–140.

    Google Scholar 

  • Yongting, C. (1996) Fuzzy quality and analysis on fuzzy probability. Fuzzy Sets and Systems, 83(2), 283–290.

    Google Scholar 

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

    Google Scholar 

  • Zhang, H.-C. and Huang, S. H. (1994) A fuzzy approach to process plan selection. International Journal of Production Research, 32(6), 1265–1279.

    Google Scholar 

  • Zimmerman, H.-J. (1983) Using fuzzy sets in operational research. European Journal of Operational Research, 13(3), 201–216.

    Google Scholar 

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  1. Department of Industrial Engineering, State University of New York at Buffalo, 342 Bell Hall, Buffalo, NY, 14260, USA

    Alfred L. Guiffrida & Rakesh Nagi

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Guiffrida, A.L., Nagi, R. Fuzzy set theory applications in production management research: a literature survey. Journal of Intelligent Manufacturing 9, 39–56 (1998). https://doi.org/10.1023/A:1008847308326

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