Purpose Life cycle assessment (LCA) is commonly used to analyze the environmental profile of a material and product. For example, the selection of building insulation is crucial to ensure that energy and financial conservation goals can be met. Most of the current methods used in material selection are deterministic in nature, which ignores or neglects the effects caused by uncertainties present in various factors, including how the thermal insulation properties of insulation may change with time. In addition, during the use phase, uncertainty in operating conditions may also affect the performance of the selected material, affecting the overall environmental and economic performance. In practice, when material usage is in large quantities, uncertainty may have significant impacts. This article proposes a novel optimization model-based approach in selecting suitable building insulations using LCA results and economic consideration together with its uncertainties. Methods This work presents an optimization-based approach to select building thermal insulation materials in the presence of data uncertainties. Firstly, we developed a deterministic model, incorporating life cycle assessment and costing to evaluate various environmental impacts and costs of the different materials. Next, by considering the different uncertainties in the data used for the environmental and cost assessments, a robust optimization approach is proposed to derive the second model. A novel solution algorithm is then developed to obtain model solutions efficiently. Results and discussion Computational studies based on a high-rise apartment in Shanghai were performed to test the applicability and performance of the proposed solution. The results demonstrate that the proposed robust optimization model is effective in mitigating the data uncertainties in the material selection optimization and outperforms the deterministic model significantly in terms of probability of achieving cost and environmental impact requirements under uncertainty. In addition, from our case studies, it was identified that parametric uncertainties and payback period exert great influence in decision-making. Conclusions The work which had expanded the basic deterministic model to include multiple aspects rather than a single objective, and taking into account of parametric uncertainties to mitigate the risks of uncertainties in real applications allows building engineers to select appropriate building material(s) based on both environmental and economic consideration. Furthermore, this work also proposes a novel solution approach to address a wide range of intractable nonlinear optimization problems.

中文翻译：

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使用稳健优化选择建筑保温材料

目的生命周期评估 (LCA) 通常用于分析材料和产品的环境状况。例如，建筑保温材料的选择对于确保实现能源和财务节约目标至关重要。当前用于材料选择的大多数方法本质上是确定性的，它忽略或忽略了各种因素中存在的不确定性引起的影响，包括绝缘材料的隔热性能如何随时间变化。此外，在使用阶段，运行条件​​的不确定性也可能影响所选材料的性能，影响整体环境和经济性能。在实践中，当材料使用量很大时，不确定性可能会产生重大影响。本文提出了一种新的基于优化模型的方法，使用 LCA 结果和经济考虑及其不确定性来选择合适的建筑绝缘材料。方法 这项工作提出了一种基于优化的方法，用于在存在数据不确定性的情况下选择建筑隔热材料。首先，我们开发了一个确定性模型，结合生命周期评估和成本计算来评估不同材料的各种环境影响和成本。接下来，通过考虑用于环境和成本评估的数据的不同不确定性，提出了一种稳健的优化方法来推导第二个模型。然后开发了一种新的解决方案算法来有效地获得模型解决方案。结果与讨论 基于上海一栋高层公寓的计算研究被用来测试所提出的解决方案的适用性和性能。结果表明，所提出的鲁棒优化模型在降低材料选择优化中的数据不确定性方面是有效的，并且在不确定性下实现成本和环境影响要求的概率方面显着优于确定性模型。此外，从我们的案例研究中发现，参数不确定性和投资回收期对决策有很大影响。结论 将基本确定性模型扩展到包括多个方面而不是单个目标的工作，考虑到参数不确定性以减轻实际应用中不确定性的风险，这使建筑工程师可以根据环境和经济因素选择合适的建筑材料。此外，这项工作还提出了一种新的解决方案来解决各种棘手的非线性优化问题。