scholarly journals Fuzzy goal programming applied to multi-objective programming problem with FREs as constraints

2015 ◽  
Vol 4 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Kailash Lachhwani
Keyword(s):  
Programming Problem ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Fuzzy Goal ◽  
Objective Programming

Bi-level multi-objective programming problem with fuzzy demands: a fuzzy goal programming algorithm

OPSEARCH ◽  
2013 ◽  
Vol 51 (2) ◽  
pp. 280-296 ◽  
Author(s):  
Ibrahim A. Baky ◽  
Mohamed H. Eid ◽  
Mohamed A. El Sayed
Keyword(s):  
Programming Problem ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Programming Algorithm ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Fuzzy Goal ◽  
Objective Programming

FUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM

2013 ◽  
Vol 22 ◽  
pp. 757-761 ◽  
Author(s):  
KAILASH LACHHWANI
Keyword(s):  
Programming Problem ◽  
Membership Function ◽  
Goal Programming ◽  
Fuzzy Programming ◽  
Fuzzy Goal Programming ◽  
Programming Approach ◽  
Perpendicular Distance ◽  
Multi Objective Programming ◽  
Fuzzy Goals ◽  
Fuzzy Goal

This paper presents the comparison between two solution methodologies Fuzzy Goal Programming (FGP) and ordinary Fuzzy Programming (FP) for multiobjective programming problem. Ordinary fuzzy programming approach is used to develop the solution algorithm for multiobjective functions which works for the minimization of the perpendicular distances between the parallel hyper planes at the optimum points of the objective functions. Suitable membership function is defined as the supremum perpendicular distance and a compromise optimum solution is obtained as a result of minimization of supremum perpendicular distance. Whereas, In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them and suitable membership function is defined for each objectives. Then achievement of the highest membership value of each of fuzzy goals is formulated by minimizing the negative deviational variables.

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