不确定条件下资源受限项目组合选择与调度问题研究

发布时间：2018-01-20 03:09

本文关键词： 不确定条件项目组合选择项目调度鲁棒性遗传算法 STC启发式算法蒙特卡洛模拟　出处：《浙江大学》2015年硕士论文　论文类型：学位论文

【摘要】：本文研究问题是不确定条件下,资源受限项目组合选择与调度问题。项目组合选择与项目调度作为项目管理的重要组成部分,正在受到越来越多的研究学者和项目经理的重视。全球化的市场经济竞争的激烈性和企业边界内部资源有限性,决定了企业必须选择有限数量的项目,并完成对项目整个进程计划的有效控制。同时,瞬息万变的经济状态和激烈竞争的市场环境,也暗示着企业或者组织面临着比以往更多的不确定性,这也对项目组合选择结果,进度计划的稳定性和有效性提出了更高的要求。现有的不确定条件下的项目组合选择问题研究多假设项目进度生成计划是确定的,是不考虑任务调度的项目组合问题的研究,割裂了项目组合选择与调度的内在联系；即使将项目组合选择与调度联系起来,也多是在确定性条件下进行分析,有一定的局限性。本研究在以往研究的基础上,着眼于不确定条件下,资源受限项目组合选择与调度问题的研究。本研究将项目组合选择和项目调度两个层面联系起来,在项目组合选择层面,以项目组合期望收益和风险的组合函数为目标,进行决策；在任务调度层面,对项目进行鲁棒调度；以期得到最优的项目组合并提供具有鲁棒性的多项目调度方案。不论是理论研究还是实践意义,都具有一定程度的开拓性。本研究设计双层决策方法对问题进行求解。其中,项目组合选择作为上层决策问题,采用遗传算法对其求解,作为算法设计的主程序；项目任务调度层面作为下层决策,采用STC启发式算法求解,作为算法设计的子程序,也是整个算法设计的内核,进行上层项目组合选择决策时,会被主程序反复调用；使用蒙特卡洛数字仿真的方法处理项目收益的不确定性,将项目任务调度执行层面的鲁棒性传递给项目组合选择决策层面。本研究不确定条件下的资源受限项目组合选择与调度问题,并设计了算例测试和大规模实验对算法进行分析。研究表明,本研究设计的模型在处理不确定条件下的资源受限项目组合选择问题具有良好的适用性,设计的算法具有良好的求解质量和求解效果。因此,本研究在一定程度上填补了不确定条件下,项目组合选择与项目调度领域的理论研究空白,所设计的数学模型也可应用于具体项目风险管理实践过程中,具有理论和实践意义。
[Abstract]:This paper studies the problem of resource constrained project portfolio selection and scheduling under uncertain conditions. Portfolio selection and project scheduling as an important part of project management. More and more researchers and project managers are paying more and more attention to it. The fierce competition in the global market economy and the limited resources within the enterprise boundary determine that enterprises must choose a limited number of projects. And complete the effective control of the whole process of the project. At the same time, the rapidly changing economic state and fierce competition market environment, also implies that enterprises or organizations are facing more uncertainty than ever before. This also puts forward higher requirements for the stability and effectiveness of the project portfolio selection results and schedule planning. The existing project portfolio selection problem under uncertain conditions multi-hypothesis project schedule generation plan is determined. It is the research of project portfolio problem that does not consider task scheduling, which separates the internal relation between project portfolio selection and scheduling. Even if portfolio selection is associated with scheduling, it is often analyzed under deterministic conditions, which has some limitations. On the basis of previous studies, this study focuses on the study of resource-constrained project portfolio selection and scheduling under uncertain conditions. This study links two levels of project portfolio selection and project scheduling. At the level of project portfolio selection, the goal is the combination function of the expected income and risk of the project portfolio, and the decision is made. At the task scheduling level, the project is scheduled robustly. In order to obtain the optimal project portfolio and provide a robust multi-project scheduling scheme, both theoretical research and practical significance have a certain degree of pioneering. In this study, a two-layer decision making method is designed to solve the problem, in which the project combination is chosen as the upper decision problem, and the genetic algorithm is used to solve the problem, which is the main program of the algorithm design. Project task scheduling level as the lower level decision, using STC heuristic algorithm to solve, as the algorithm design subroutine, but also the kernel of the whole algorithm design, when the upper project combination selection decision. Will be called repeatedly by the main program; Monte Carlo digital simulation is used to deal with the uncertainty of project income, and the robustness of project task scheduling execution level is transferred to the decision level of project portfolio selection. In this paper, the resource constrained project portfolio selection and scheduling problem under uncertain conditions is studied, and an example is designed to test and analyze the algorithm. The model designed in this paper has good applicability in dealing with the resource-constrained project portfolio selection problem under uncertain conditions, and the algorithm designed has good solution quality and solution effect. To a certain extent, this study fills the blank of theoretical research in the field of portfolio selection and project scheduling, and the mathematical model designed can also be applied to the practical process of specific project risk management. It has theoretical and practical significance.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F272

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