single-jc.php

JACIII Vol.20 No.1 pp. 5-12
doi: 10.20965/jaciii.2016.p0005
(2016)

Paper:

Fuzzy Nonlinear Programming for Production Inventory Based on Statistical Data

Lily Lin* and Huey-Ming Lee**

*Department of International Business, China University of Technology
56, Section 3, Hsing-Lung Road, Taipei 116, Taiwan

**Department of Information Management, Chinese Culture University
55 Hwa-kang Road, Yang-Ming-Shan, Taipei 11114, Taiwan

Received:
December 29, 2013
Accepted:
April 8, 2015
Online released:
January 19, 2016
Published:
January 20, 2016
Keywords:
interval-valued fuzzy number, confidence interval, nonlinear programming
Abstract

The purpose of this study is to develop a crisp nonlinear method of programming to address production inventory problems based on both production inventory and conditions. In addition, we use a statistical confidence interval to derive level (1-β, 1-α) interval-valued fuzzy numbers, in order to solve problems in nonlinear programming for production inventory in the fuzzy sense.

References
  1. [1] L.A. Zadeh, “Fuzzt sets,” Information and Control, Vol.8, pp. 338-353, 1965.
  2. [2] G. Bojadziev and M. Bojadziev, “Fuzzy Logic for Business, Management,” World Scientific Publishing Co., 1997.
  3. [3] H.-J. Zimmermann, “Fuzzy Set Theory and Its Applications,” 4th Ed., Kluwer Academic Publishers, Boston/Dordrecht/London, 2001.
  4. [4] H.-M. Lee and J. Chiang, “Fuzzy production inventory based on signed distance,” J. of Information Science and Engineering, Vol.23, pp. 1939-1953, 2007.
  5. [5] H.-M. Lee and J.-S. Yao, “Economic production quantity for fuzzy demand quantity and fuzzy production quantity,” European J. of Operation Research, Vol.109, pp. 203-211, 1998.
  6. [6] D.-C. Lin and J.-S. Yao, “Fuzzy economic production for production inventory, “ Fuzzy Sets and Systems, Vol.111, pp. 465-495, 2000.
  7. [7] D. Dufois and H. Prade, “System of linear fuzzy constraints,” Fuzzy Sets and Systems, Vol.13, pp. 1-10, 1982.
  8. [8] H. Tanaka and K. Asal, “Fuzzy linear programming problem with fuzzy numbers,” Fuzzy Sets and Systems, Vol.13, pp. 1-10, 1984.
  9. [9] M. B. Gorzalezany, “A method of inference in approximate reasion based on interval-valued fuzzy sets,” Fuzzy Sets and Systems, Vol.21, pp. 1-17, 1987.
  10. [10] P. S. Mann, “Statistics for Business and Economics,” John Wiley & Sons, Inc., New York, 1995.
  11. [11] A. Kaufmann and M. M. Gupta, “Introduction to Fuzzy Arithmetic Theory and Application,” Van Nostrand Reinhold New York, 1991.
  12. [12] H.-M. Lee C.-F. Fuh and J.-S. Su, “Fuzzy parallel system reliability analysis based on level (λ,ρ) interval-valued fuzzy numbers,” Int. J. of Innovative Computing, Information and Control, Vol.8, No.8, pp. 5703-5713, 2012.
  13. [13] J.-S. Yao and K. Wu, “Ranking fuzzy numbers based on decomposition principle and signed distance,” Fuzzy Sets and Systems, Vol.116, pp. 275-288, 2000.
  14. [14] F.-S. Budnick, D. Mcleavey, and R. Mjena, “Principles of operations research for management,” IRWIN, 1988.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Oct. 16, 2017