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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
