Abstract
We study a fundamental model of resource allocation in which a finite amount of service capacity must be allocated to a stream of jobs of different priorities arriving randomly over time. Jobs incur costs and may also cancel while waiting for service. To increase the rate of service, overtime capacity can be used at a cost. This model has application in healthcare scheduling, server applications, make-to-order manufacturing systems, general service systems, and green computing. We present an online algorithm that minimizes the total cost due to waiting, cancellations and overtime capacity usage. We prove that our scheduling algorithm has cost at most twice of an optimal offline algorithm. This competitive ratio is the best possible for this class of problems. We also provide extensive numerical experiments to test the performance of our algorithm and its variants.
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.
