Abstract
Many real-world scheduling problems are characterized by uncertain parameters. In this paper, we
study a classical parallel machine scheduling problem where the processing time of jobs is given by
a normal distribution. The objective is to maximize
the probability that jobs are completed before a
given common due date. This study focuses on the
computational aspect of this problem, and it proposes a Branch-and-Price approach for solving it.
The advantage of our method is that it scales very
well with the increasing number of machines and
is easy to implement. Furthermore, we propose an
efficient lower bound heuristics. The experimental
results show that our method outperforms the existing approaches