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A Comparative Study of Fuzzy Linear and Multi-Objective Optimization
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Author(s): Pinki Gulia (Lovely Professional University, India), Rakesh Kumar (Lovely Professional University, India), Amandeep Kaur (Chandigarh University, India)and Gaurav Dhiman (Department of Computer Science, Government Bikram College of Commerce, Patiala, India & University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India & Department of Computer Science and Engineering, Graphic Era University (Deemed), Dehradun, India)
Copyright: 2022
Pages: 20
Source title:
AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
Source Author(s)/Editor(s): Sandeep Kumar Kautish (Chandigarh University, Mohali, India)and Gaurav Dhiman (Government Bikram College of Commerce, India & Lebanese American University, Lebanon)
DOI: 10.4018/978-1-6684-4405-4.ch007
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Abstract
A new paradigm for the solution of problems involving single- and multi-objective fuzzy linear programming is presented in this chapter. As opposed to complex arithmetic and logic for intervals, the method offered uses basic fuzzy mathematical operations for fuzzy integers instead. Using fuzzy numbers to express variables and parameters in a fuzzy linear programming issue (FLPP) is common. However, the authors only talked about FLPP with fuzzy parameters here. Triangular fuzzy numbers are used as fuzzy parameters. Ranking functions are used to convert fuzzy problems into clear ones. Crisp optimization techniques have been used. The proposed solution is tested on a variety of real-world examples that address both of these concerns.
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