Trisna, Trisna and Marimin, Marimin and Arkeman, Yandra (2016) Solving Fuzzy Multi-objective Optimization Using Nondominated Sorting Genetic Algorithm II. In: The 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang.
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IEEE 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) - Malang, Indonesia (201.pdf Download (5MB) | Preview |
Abstract
This paper presents the stages for solving fuzzy multi-objective optimization problems using genetic algorithm approach. Before applying non-dominated sorting genetic algorithm II (NSGA II) techniques to obtain optimal solution, first multi-objective possibilistic (fuzzy) programmmg was converted into an equivalent auxiliary crisp model to form deterministic progl'amming model. To detel'mine the best solution from Pareto set, we implied feasibility degree of decision variable and satisfaction degree of decision maker. The best optimal solution is the intersection between a-feasibility degree and satisfaction degree of the decision makers that has the highest fuzzy membership degl·ee. For numerical experiment, we used simple formulation in multi-objective fuzzy linear programming model with three maximum objective functions, three decision variables, and six constraints. The comparison of the results shows that our results are better for two objectives than that of compromising programming.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | T Technology & Engineering > TB Industrial Engineering > Operation Research T Technology & Engineering > TB Industrial Engineering > Supply Chain |
| Divisions: | Faculty of Engineering > Department of Industrial Engineering |
| Depositing User: | Dr. Trisna ST, M.Eng |
| Date Deposited: | 01 Dec 2019 14:03 |
| Last Modified: | 01 Dec 2019 14:03 |
| URI: | http://repository.unimal.ac.id/id/eprint/5026 |
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