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首页> 外文会议>17th Anniversary of the International Simulatros Conference, Apr 16-20, 2000, Washington, D.C. >PROJECT RISK QUANTIFIER AND OPTIMAL PROJECT ACCELERATOR - USING MACHINE LEARNING AND STOCHASTIC SIMULATION TO OPTIMIZE PROJECT COMPLETION DATES AND COSTS
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PROJECT RISK QUANTIFIER AND OPTIMAL PROJECT ACCELERATOR - USING MACHINE LEARNING AND STOCHASTIC SIMULATION TO OPTIMIZE PROJECT COMPLETION DATES AND COSTS

机译：项目风险量化器和最佳项目加速器-使用机器学习和随机模拟来优化项目完成日期和成本

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Program managers routinely make decisions that affect projects in all phases. These decisions often control hundreds of thousands or millions of dollars of resources and personnel. A tremendous amount of energy has been applied to developing project management tools such as scheduling software and the like. These systems can sometimes be limited by two fundamental impediments; the project variables such as task duration and cost are typically assumed to be precisely known, and the project relationships are not always simple linear functions. These impediments can sometimes adversely affect the effectiveness of even the most expert project manager. This work describes a tool that combines risk assessment with optimization to optimize project management decisions. These tools consist of a project variable risk assessment by using stochastic inputs for project variables such as task duration and cost instead of single value point estimates. This analysis provides probability distributions for intermediate milestone or total project costs and completion dates. By employing a MonteCarlo simulation technique, the manager can focus project monitoring resources from output the details the percentage of the time a task will fall on the critical path. This can be done at the bidding stage to evaluate the project risk/reward level or at the project kick-off stages to optimize the project baseline schedule. If project slippage begins to occur, the manager can then implement a machine learning software algorithm known as genetic algorithm or genetic programming, that will optimize which project tasks to accelerate to recover schedule at the minimum cost under deterministic or uncertain (i. e. stochastic) conditions. These results are immediately discernable and readily explainable to management. A summary of the theoretical basis and applicability for the techniques are provided.
机译：计划经理通常会做出影响项目各个阶段的决策。这些决定通常控制着数十万或数百万美元的资源和人员。大量的精力已用于开发项目管理工具，例如计划软件等。这些系统有时会受到两个基本障碍的限制：通常假定项目变量（例如任务持续时间和成本）是精确已知的，并且项目关系并不总是简单的线性函数。这些障碍有时甚至会对最专业的项目经理产生负面影响。这项工作描述了将风险评估与优化相结合以优化项目管理决策的工具。这些工具由项目变量风险评估组成，该评估通过使用项目变量（例如任务持续时间和成本）的随机输入而不是单个价值点估计来进行。该分析提供了中间里程碑或项目总成本和完成日期的概率分布。通过采用MonteCarlo仿真技术，管理人员可以从输出细节集中项目监视资源，以了解任务落在关键路径上的时间百分比。这可以在招标阶段评估项目风险/回报水平，或者在项目启动阶段进行以优化项目基准时间表。如果项目开始出现滑移，则经理可以执行称为遗传算法或遗传编程的机器学习软件算法，该算法将在确定性或不确定性（即随机）条件下优化以最小成本加速恢复进度的项目任务。这些结果是立即可辨别的，并且易于管理层解释。总结了该技术的理论基础和适用性。

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