Abstract
In this paper, a learning curve effect on project duration is shown – calculating with working days only - and estimated with the help of simulated examples. Although learning is an essential part of our life, traditional scheduling techniques cannot efficiently handle the learning curve effect. It is assumed that the duration of impending repetitive activities are shorter due to the learning curve effect if the gap between consecutive activities is small enough. Taking into account the effects of the learning curve (or experience curve), it is possible to better predict project duration thus saving time and money. This effect normally results in shorter project duration. Although the effect is a “simple” calculation, it leads to an exponential time algorithm if the learning effect is applied to traditional project scheduling techniques like CPM, or Precedence Diagramming Method. In this paper, an integer programming model is developed, and an efficient algorithm is used to establish project duration. (This paper is an extended version of Levente Malyusz: Learning curve effect on Project Scheduling, Procedia 2016.)
Highlights
Project scheduling methods suffer from a lack of precision; it is a significant challenge to create a realistic and usable project schedule
The effect is a “simple” calculation, it leads to an exponential time algorithm if the learning effect is applied to traditional project scheduling techniques like CPM, or Precedence Diagramming Method
An integer programming model is developed, and an efficient algorithm is used to establish project duration. (This paper is an extended version of Levente Malyusz: Learning curve effect on Project Scheduling, Procedia 2016.)
Summary
Project scheduling methods suffer from a lack of precision; it is a significant challenge to create a realistic and usable project schedule. It is difficult and timeconsuming to estimate time, assign resources, determine interdependencies between tasks, and manage changes It is, important to identify and investigate the differences between the practice and theory of scheduling methods (Francis et al, 2013). Learning curve theory can be applied to predicting cost and time, generally in units of time, to complete repetitive activities (Malyusz and Pém, 2014; Hasanzadeh et al, 2016). Learning rate is the constant rate with which labour time/cost decreases when the production/ cycles doubles in a linear log x, log y model. Taking an example with 90% of the learning rate, in this case, if a job is 10 days, a repetition of that is 8 days if the working conditions are similar.
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