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models, an easy way to solve them is to use the genetic algorithms
(GA). GA can be viewed as a family of computational models that are
inspired by evolution. To illustrate the effectiveness of the model
obtained in this paper, GA is used to solve the multi-objective opti-
misation model. Note that other types of popular algorithms, such
as particle swarm optimisation, simulated annealing, ant colony,
and so forth, may also be applied to solve the obtained model.
The paper is organised as follows: in Section 2, an opti-
misation model for investment decision making in buildings
energy-efficiency projects is formulated. In Section 3 the optimi-
sation model is applied to a case study. The results and simulations 一个建筑节能投资决策的多目标优化模型
摘要：一个多目标优化模型是当投资于节能改造时制定来帮助决策者做出最佳决策的。对于一个给定的固定的初始投资项目进行最大限度地节约能源和减少投资回收期。该模型是制定为一个有净现值（NPV），能源和投资回收期为约束条件的多目标优化问题的初始投资，它也采用遗传算法求解。最优决策是通过选择最佳的决策达到建筑节能改造。该模型应用在一个拥有25种可以改装的设施的建筑，说明能源节约的多和回收期价值的少。它也通过分析设施影响审核的错误，制定错误的节约能源，初始投资，利率的变化和电力价格对投资回收期的变化，最大的能量保存和NPV投资进行敏感度分析。分析结果表明，该模型是稳定的。