...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers & Industrial Engineering >A chance constrained optimization approach for resource unconstrained project scheduling with uncertainty in activity execution intensity
【24h】

A chance constrained optimization approach for resource unconstrained project scheduling with uncertainty in activity execution intensity

机译：活动执行强度不确定的资源不受约束的项目调度的机会约束优化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We study the problem of scheduling project activities with precedence constraints and unlimited resources. The latter problem, with the objective of minimizing the completion time of the project and deterministic activity durations, is known to be polynomially solvable. In the case of stochastic durations, the objective becomes to determine the project makespan distribution which is a #P complete problem. The most common technique used in this case is PERT. However, it is known that PERT tends to underestimate the expected makespan of the project. In our work, we try to overcome this shortcoming by considering a stochastic formulation of the problem, exploiting the activity execution intensity as a stochastic variable, and a chance constrained optimization approach. The main hypotheses under which our model works are essentially two: one is to have a sufficiently large time horizon for the project and the second, differently to what happens for the durations of the activities in the PERT model, is to assume a Beta probability density function for the activity execution intensity variables. The first hypothesis appears to be realistic since, when time horizon is large, stochastic factors tend to come into play in every decision problems; the second hypothesis, is realistic as well, since a minimum and a maximum value exist for the stochastic variables used in our model. Experimental results and a comparison with the PERT model and a Monte Carlo simulation are presented.
机译：我们研究了具有优先约束和无限资源的项目活动调度问题。以使项目的完成时间和确定的活动持续时间最小化为目标的后一个问题是可以解决多项式的。在随机时间的情况下，目标是确定项目的完成时间分布，这是一个#P完全问题。在这种情况下，最常用的技术是PERT。但是，众所周知，PERT往往低估了该项目的预期工期。在我们的工作中，我们尝试通过考虑问题的随机表述，利用活动执行强度作为随机变量和机会受限的优化方法来克服此缺点。我们的模型工作的主要假设基本上是两个：一个是为项目拥有足够大的时间范围，第二个与PERT模型中活动持续时间所发生的情况不同，是假设Beta概率密度活动执行强度变量的函数。第一个假设似乎是现实的，因为当时间跨度很大时，随机因素往往会在每个决策问题中发挥作用；第二个假设也是现实的，因为模型中使用的随机变量存在最小值和最大值。给出了实验结果，并与PERT模型和蒙特卡洛模拟进行了比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号