On the project risk baseline: integrating aleatory uncertainty into project scheduling

TL;DR

This paper introduces a novel method that incorporates aleatory uncertainty into project scheduling to help managers select schedules with the highest probability of meeting deadlines among alternatives of equal duration.

Contribution

It proposes a new approach for integrating risk quantification into schedule selection, addressing a gap in existing project management tools.

Findings

Method successfully tested on benchmark repository

Enables risk-based schedule comparison

Helps select schedules with lowest risk of deadline breach

Abstract

Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a well-known repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.