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An analytical approach to estimate the expected duration and variance for iterative product development projects

机译：一种估计迭代产品开发项目的预期持续时间和差异的分析方法

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The paper presents an analytical method for finding the expected duration and variance of a product development (PD) project network. A PD project network is a stochastic activity network (such as a PERT network) which allows for probabilistic repetition of activities (i.e., activity rework). When rework is allowed, estimating the process duration and variance becomes difficult. Most existing literature refers to the use of simulation in such scenarios; however, few analytical methods exist to solve this problem. One such method is called the reward Markov chain (RMC) which only considers sequential activity networks and which we use as a starting point in our proposed method. In this paper, we extend the RMC method to solve mixed networks (i.e., a combination of parallel and sequential activities) and more complicated practical issues that may arise in PD environments, specifically coupled activities and parallel rework.
机译：本文提出了一种分析方法，用于查找产品开发（PD）项目网络的预期持续时间和差异。 PD项目网络是一种随机活动网络（例如PERT网络），它允许活动的概率重复（即活动返工）。当允许返工时，估计过程持续时间和差异变得困难。现有的大多数文献都提到在这种情况下使用仿真。但是，很少有分析方法可以解决此问题。一种这样的方法称为奖励马尔可夫链（RMC），它仅考虑顺序活动网络，在我们提出的方法中将其用作起点。在本文中，我们将RMC方法扩展为解决混合网络（即并行活动和顺序活动的组合）以及PD环境中可能出现的更复杂的实际问题，特别是耦合活动和并行返工。

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