Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost
Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost
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DOI：
10.1080/01446190802036128
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发表时间：
2008-07
影响因子：
3.4
通讯作者：
E. Eshtehardian;A. Afshar;R. Abbasnia
中科院分区：
文献类型：
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作者：
E. Eshtehardian;A. Afshar;R. Abbasnia
在任何时间成本权衡问题中，当最小化项目成本和工期时，都应该考虑不确定性，这导致了所谓的随机时间成本权衡问题。提出了一种利用模糊逻辑理论研究随机时间成本权衡问题的新方法。所提出的方法充分嵌入到模型中的总直接成本的不确定性的模糊结构。一个适当的遗传算法是用来开发一个解决方案的多目标模糊时间成本模型。项目经理的可接受风险水平是通过α切割方法定义的，该方法已经开发了一组非支配解的单独的帕累托前沿。为了比较任何假定项目工期的备选方案集，采用两种适当的模糊成本比较方法对不同α截值的相关模糊成本进行排序。所提出的模型被应用于解决两个基准测试问题。结果表明，该模型通过选择特定的风险水平和采用相关的帕累托前沿来促进决策过程。
Uncertainties should be considered in any time–cost trade‐off problems when minimizing project cost and duration, which leads to the so‐called stochastic time–cost trade‐off problem. A new approach to investigate stochastic time–cost trade‐off problems employing fuzzy logic theory is presented. The proposed approach fully embeds the fuzzy structure of the uncertainties in total direct cost into the model. An appropriate GA is used to develop a solution to the multi‐objective fuzzy time cost model. The accepted risk level of the project manager is defined through α cut approach for which a separate Pareto front with set of non‐dominated solutions has been developed. To compare the alternative set of options for any assumed project duration, associated fuzzy costs for different values of α cut are ranked employing two appropriate approaches for fuzzy costs comparison. The proposed models are applied to solve two benchmark test problems. It is shown that the models facilitate the decision‐making process by selecting specified risk levels and employing the associated Pareto front.