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Fuzzy efficient and pareto — Optimal solution for multiobjective linear plus linear fractional programming problem

机译:模糊有效和pareto —多目标线性加线性分数规划问题的最优解

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Many practical optimization problems usually have several conflicting objectives. In those multiobjective optimization, no solution optimizing all the objective functions simultaneously exists in general. Instead, pareto — optimal solutions, which are efficient in terms of all objective functions, are introduced. In general we have many optimal solutions. Therefore we need to decide a final solutions among pareto — optimal solutions taking in to account the balance among objective functions. In this paper we find fuzzy efficient and pareto — optimal solution to the multiobjective linear plus linear fractional programming problem and show that, in the case that, when any goal is fully achieved, then fuzzy efficient solution may or may not be pareto — optimal solution and therefore we propose a procedure to obtain fuzzy efficient solution which is pareto — optimal also and review some results. In the proposed approach each objective function is transformed into linear functions by using Taylor's theorem. Then the MOLLFP is changed into equivalent multiobjective linear programming problem (MOLP) and then find fuzzy efficient and pareto — optimal solution in finite number of steps. Efficiency of proposed method is verified by numerical examples. To explore the potential use of the proposed method, three numerical examples are solved. AMS 2000 Subject Classification: 90C29, 90C32
机译:许多实际的优化问题通常都有几个相互矛盾的目标。在那些多目标优化中,通常不存在同时优化所有目标函数的解决方案。相反,引入了pareto-在所有目标函数方面均有效的最佳解决方案。通常,我们有许多最佳解决方案。因此,我们需要在pareto中确定最终解决方案-考虑目标函数之间的平衡的最佳解决方案。在本文中,我们找到了多目标线性加线性分数规划问题的模糊有效和最优解,并表明,在完全实现任何目标的情况下,模糊有效解可能是也可能不是最优解因此,我们提出了一种获得模糊有效解的方法,该解也是pareto —最优的,并复习了一些结果。在提出的方法中,使用泰勒定理将每个目标函数转换为线性函数。然后将MOLLFP转换为等效的多目标线性规划问题(MOLP),然后在有限步数内找到模糊有效和pareto-最优解。数值算例验证了所提方法的有效性。为了探索该方法的潜在用途,求解了三个数值示例。 AMS 2000主题分类:90C29、90C32

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