Generating Pareto Optimal Solutions of Multi-Objective LFPP With Interval Coefficients Using E-Constraint Method

Nayak, Suvasis; Ojha, Akshay K.

doi:10.3846/13926292.2015.1048757

Cited by 7 publications

(1 citation statement)

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“…In general, the objective functions are conflicting, and it is not possible to obtain a unique solution that optimizes simultaneously these objective functions. Many solutions can be found by using the concept of Pareto optimality [7,23]. For this set of solutions, we cannot choose a better one without adding some criteria of preference.…”

Section: Introductionmentioning

confidence: 99%

New Approach to Solving Fuzzy Multiobjective Linear Fractional Optimization Problems

Sama,

Traore,

Some

2024

Int. J. Anal. Appl.

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In this paper, an iterative approach based on the use of fuzzy parametric functions is proposed to find the best preferred optimal solution to a fuzzy multiobjective linear fractional optimization problem. From this approach, the decision-maker imposes tolerance values or termination conditions for each parametric objective function. Indeed, the fuzzy parametric values are computed iteratively, and each fuzzy fractional objective is transformed into a fuzzy non-fractional parametric function using these values of parameters. The core value of fuzzy numbers is used to transform the fuzzy multiobjective non-fractional problem into a deterministic multiobjective non-fractional problem, and the ε-constraint approach is employed to obtain a linear single objective optimization problem. Finally, by setting the value of parameter ε, the Dangtzig simplex method is used to obtain an optimal solution. Therefore, the number of solutions is equal to the number of used values, and the optimal solution is chosen according to the preference of the decision-maker. We have provided a didactic example to highlight the step of our approach and its numerical performances.

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Section: Introductionmentioning

confidence: 99%