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An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems

Jie Lu, Da Ruan (UGent) , Fengjie Wu and Guangquan Zhang
(2007) SOFT COMPUTING. 11(3). p.259-267
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Organization
Abstract
Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree alpha to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions.
Keywords
optimization, fuzzy multiple objective linear programming, multiple objective linear programming, approximate algorithm

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Citation

Please use this url to cite or link to this publication:

MLA
Lu, Jie, Da Ruan, Fengjie Wu, et al. “An Α-fuzzy Goal Approximate Algorithm for Solving Fuzzy Multiple Objective Linear Programming Problems.” SOFT COMPUTING 11.3 (2007): 259–267. Print.
APA
Lu, J., Ruan, D., Wu, F., & Zhang, G. (2007). An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems. SOFT COMPUTING, 11(3), 259–267. Presented at the 6th International FLINS conference on Applied Artificial Intelligence.
Chicago author-date
Lu, Jie, Da Ruan, Fengjie Wu, and Guangquan Zhang. 2007. “An Α-fuzzy Goal Approximate Algorithm for Solving Fuzzy Multiple Objective Linear Programming Problems.” Soft Computing 11 (3): 259–267.
Chicago author-date (all authors)
Lu, Jie, Da Ruan, Fengjie Wu, and Guangquan Zhang. 2007. “An Α-fuzzy Goal Approximate Algorithm for Solving Fuzzy Multiple Objective Linear Programming Problems.” Soft Computing 11 (3): 259–267.
Vancouver
1.
Lu J, Ruan D, Wu F, Zhang G. An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems. SOFT COMPUTING. 2007;11(3):259–67.
IEEE
[1]
J. Lu, D. Ruan, F. Wu, and G. Zhang, “An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems,” SOFT COMPUTING, vol. 11, no. 3, pp. 259–267, 2007.
@article{2918946,
  abstract     = {Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree alpha to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions.},
  author       = {Lu, Jie and Ruan, Da and Wu, Fengjie and Zhang, Guangquan},
  issn         = {1432-7643},
  journal      = {SOFT COMPUTING},
  keywords     = {optimization,fuzzy multiple objective linear programming,multiple objective linear programming,approximate algorithm},
  language     = {eng},
  location     = {Blankenberge, Belgium},
  number       = {3},
  pages        = {259--267},
  title        = {An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems},
  url          = {http://dx.doi.org/10.1007/s00500-006-0067-5},
  volume       = {11},
  year         = {2007},
}
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