Measuring schedule uncertainty for a stochastic resource-constrained project using scenario-based approach with utility-entropy decision model

Abstract

The aim of this study is to propose a scenario-based approach with utility-entropy decision model to measure the uncertainty related to the evolution of a resource-constrained project scheduling problem with uncertain activity durations (a stochastic RCPSP). The approach consists of two stages. The first is to apply the proposal proposed by Tseng and Ko to convert a stochastic RCPSP into a full scenario tree. In stage two, we introduce the Expected Utility–Entropy (EU-E) decision model, a weighted linear average of expected utility and entropy, to establish an EU-E criterion. Then we apply the criterion to prune the worse branch(es) to lead a reduced scenario tree. Based on an illustrated example, it has been concluded that the reduced scenario tree by the EU-E criterion with larger trade-off coefficient λ has less number of possible paths, less uncertainty, and lengthier expected project duration than that with smaller trade-off coefficient λ. Thus, this has demonstrated that not only can the whole scenario during the course of a project be obtained, but also the uncertainty related to the evolution of a project can be measured.

Keywords:

• Resource-constrained project scheduling problem
• expected utility-entropy model
• scenario tree