| Title of paper:
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Intuitionistic fuzzy T-sets based solution technique for multiple objective linear programming problems under imprecise environment
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| Author(s):
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| Arindam Garai
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| Department of Mathematics, Sonarpur Mahavidyalaya, West Bengal, India, Pin 700149
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| fuzzy_arindam@yahoo.com
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| Palash Mandal
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| Department of Mathematics, IIEST, Shibpur, Howrah, West Bengal, India, Pin 711103
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| palashmandalmbss@gmail.com
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| Tapan Kumar Roy
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| Department of Mathematics, IIEST, Shibpur, Howrah, West Bengal, India, Pin 711103
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| roy_t_k@yahoo.co.in
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| Published in:
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"Notes on IFS", Volume 21, 2015, Number 4, pages 104–123
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| Download:
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 PDF (146 Kb, File info)
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| Abstract:
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Technique to find Pareto-optimal solutions to multiple objective linear programming problems under imprecise environment is discussed in this paper. In 1997, Angelov formulated an optimization technique under intuitionistic fuzzy environment. Several other researchers have worked on it in recent years. In optimization technique under imprecise environment, it is observed that the prime intention to maximize up-gradation of most misfortunate is better served if some constraints present in existing, well established techniques are removed. In classical intuitionistic fuzzy optimization techniques, it is also observed that membership functions and non-membership functions are not utilised in the way they are defined; and in some cases, constraints in those existing techniques may make the problem infeasible. Hence in this paper, new functions: T(+)-characteristic functions and T(–)-characteristic functions, are introduced to supersede membership functions and non-membership functions respectively; and subsequently new set: Intuitionistic fuzzy T-set is introduced to supersede intuitionistic fuzzy set to represent impreciseness. Moreover in this paper, one general algorithm has been developed to find Pareto optimal solutions to multiple objective linear programming problems under imprecise environment. A real life industrial application model further illustrates the limitations of existing technique as well as advantages of using proposed technique. Finally conclusions are drawn.
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| Keywords:
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Intuitionistic fuzzy sets, Intuitionistic fuzzy optimization, T(+)-characteristic functions, T(–)-characteristic functions, Intuitionistic fuzzy T-sets.
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| AMS Classification:
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03E72, 90C70, 58E17, 90C05.
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| References:
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