近些年通过对液压元件污染状况的测量分析，有研究针对典型的单回路液压系统成功建立了不确定条件下的污染控制数学模型。本文在此基础上补充考虑了实际系统中部分参数间的相互关联以及泵的污染磨损机理，拟在不损坏关键液压元件的前提下，通过安装合适的过滤器及规划合理的更换周期，以降低系统运行的费用。该模型利用一致逼近的数学方法对大量的数据进行描述，极大精简了模型。同时，全文引入区间规划的优化理论，对实验算例进行分析并证实了该模型的有效性。

针对目前在图形用户界面（graphic user interface, GUI）自动化测试方法中存在的手工依赖性和测试缺乏准确性等问题，提出了一种改进的GUI自动化测试算法。该算法包括两种基于事件流图的GUI自动化测试用例生成策略：基于蚁群算法的日常冒烟测试用例生成策略和基于宽度优先搜索生成树的深度回归测试用例生成策略。将这两种策略应用于没有考虑分层的GUI 事件流图模型中，得到标准GUI 的测试用例，然后再进行GUI测试。结合Microsoft UI Automation 框架和Visual Studio 2005 开发平台，对该算法进行了实验研究。研究表明：该算法可以提高GUI测试的自动化程度和准确性。

A more highly automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, an automation tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then an improved ant colony optimization algorithm and a goal-directed searching approach are adopted to create GUI test sample cases. Moreover, a corresponding prototype system based on Microsoft UI automation framework is developed.

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-flow-allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.

In this study, an integrated two-stage optimization model (ITOM) is developed for the planning of municipal solid waste (MSW) management in the City of Regina, Canada. The ITOM improves upon the existing optimization approaches with advantages in uncertainty reflection, dynamic analysis, policy investigation, and risk assessment. It can help analyze various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated, and address issues concerning planning for a cost-effective diversion program that targets on the prolongation of the existing landfill. Moreover, violations for capacity and diversion constraints are allowed under a range of significance levels, which reflect the tradeoffs between system-cost and constraint-violation risk. The modeling results are useful for generating a range of decision alternatives under various environmental, socio-economic, and system-reliability conditions. They are valuable for supporting the adjustment (or justification) of the existing waste-management practices, the long-term capacity planning for the city's waste-management system, and the identification of desired policies regarding waste generation and management.