Abstract
This paper considers a stochastic online scheduling problem in which a set of independent jobs are to be processed on a single machine. Each job has a processing time, which is a random variable with normal distribution. All the jobs arrive overtime, which means that the existence and the parameters of each job including its processing time specifications and weight are unknown until its release date. Moreover, the actual processing time of each job is unknown until its completion. During the processing, jobs are allowed to be preempted and restarted later. So, the processing time devoted to the job before the preemption is lost and considered as preemption penalty. The objective is to minimize the expected value of the total weighted completion time. Since the problem is strongly NP-hard, a heuristic algorithm is proposed in this paper and is validated using numerical examples. The proposed method utilizes the properties of the normal distribution but it can be used as a heuristic for other distributions, as long as their means and variances are available.
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.
