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MAXIMIZING THE VALUE OF RESIDENTIAL PROJECTS USING FUZZY RULE BASED LINEAR PROGRAMMING

机译:使用基于模糊规则的线性规划最大化住宅项目的价值

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摘要

Optimizing the allocation of residence types has a great impact on efficient management of resources for construction projects. Hence, determining appropriate allocation of residence types provides effective financial activities, especially for large-scaled residential projects. Subjectivity and vagueness in the determination of price per m(2) for residences requires the consideration of fuzzy logic, since the modeling of imprecise and qualitative knowledge, as well as the transmission and handling of uncertainty at various stages are possible through the use of fuzzy sets. This study proposes an approach that integrates Fuzzy Rule Based System (FRBS) with mathematical programming. In the proposed hybrid approach, FRBS is used to set price per square meter and it provides input for Net Present Value (NPV) formulation. Mixed integer linear programming (MILP) model is developed for the maximization of (NPV) of the project. The proposed model is applied to a residential project in Istanbul to demonstrate its performance. The optimal NPVs and allocation results for four types of residences are analyzed depending on the different factor levels of total construction area, sales rate and bank loan interest rate. The results indicate that the optimal NPV and allocation of residences are significantly influenced by the factor of construction area.
机译:优化居住类型的分配对建设项目资源的有效管理有很大影响。因此,确定适当的居住类型分配可提供有效的财务活动,尤其是对于大型住宅项目。在确定住宅每平方米价格(2)时的主观性和模糊性需要考虑模糊逻辑,因为通过使用模糊可能对不精确和定性知识进行建模以及在各个阶段传递和处理不确定性套。这项研究提出了一种将基于模糊规则的系统(FRBS)与数学编程相集成的方法。在提出的混合方法中,FRBS用于设置每平方米的价格,并为净现值(NPV)公式提供输入。开发了混合整数线性规划(MILP)模型以最大化项目(NPV)。提议的模型应用于伊斯坦布尔的一个住宅项目,以证明其性能。根据总建筑面积,销售率和银行贷款利率的不同因素水平,分析了四种类型住宅的最优NPV和分配结果。结果表明,最优NPV和住宅分配受到建筑面积因素的显着影响。

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