Optimizing problems in the modern era, the single objective optimization problems are insufficient to hold the full data of the problem. Therefore, multi-objective optimization problems come to the rescue. Similarly, in daily life problems, the parameters used in the optimization problem are not always fixed but there may be some uncertainty and it can characterize by fuzzy number. This work underlines the genetic algorithm (GA) based solution of fuzzy transportation problem with more than one objective. With a view to providing the multifaceted choices to decision-maker (DM), the exponential membership function is used with the decision-makers desired number of cases which consisted of shape parameter and aspiration level. Here, we consider the objective functions which are non-commensurable and conflict with each other. To interpret, evaluate and exhibit the usefulness of the proposed method, a numerical example is given. KeywordsFuzzy optimization, GA, Exponential membership function, Decision-maker (DM).

中文翻译：

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基于遗传算法的模糊多目标运输问题求解

在现代的优化问题中，单目标优化问题不足以容纳问题的全部数据。因此，多目标优化问题得以解决。同样，在日常生活问题中，优化问题中使用的参数并不总是固定的，但可能存在一些不确定性，并且可以通过模糊数来表征。这项工作强调了具有多个目标的基于遗传算法的模糊运输问题解决方案。为了向决策者（DM）提供多方面的选择，指数隶属函数与决策者所需的病例数一起使用，包括形状参数和期望水平。在这里，我们考虑的目标功能是不可比拟的，并且彼此冲突。为了解释，评估并展示了所提方法的有效性，并给出了数值例子。关键字：模糊优化，遗传算法，指数隶属函数，决策者（DM）。