[{"publication":"Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)","year":"2017","ddc":["000"],"project":[{"_id":"1","name":"SFB 901"},{"_id":"16","name":"SFB 901 - Subprojekt C4"},{"_id":"4","name":"SFB 901 - Project Area C"}],"status":"public","citation":{"bibtex":"@inproceedings{Mäcker_Malatyali_Meyer auf der Heide_Riechers_2017, series={Lecture Notes in Computer Science}, title={Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times}, volume={10787}, DOI={10.1007/978-3-319-89441-6}, booktitle={Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)}, publisher={Springer}, author={Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}, year={2017}, pages={207–222}, collection={Lecture Notes in Computer Science} }","apa":"Mäcker, A., Malatyali, M., Meyer auf der Heide, F., & Riechers, S. (2017). Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times. In *Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)* (Vol. 10787, pp. 207–222). Springer. https://doi.org/10.1007/978-3-319-89441-6","short":"A. Mäcker, M. Malatyali, F. Meyer auf der Heide, S. Riechers, in: Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA), Springer, 2017, pp. 207–222.","mla":"Mäcker, Alexander, et al. “Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times.” *Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)*, vol. 10787, Springer, 2017, pp. 207–22, doi:10.1007/978-3-319-89441-6.","chicago":"Mäcker, Alexander, Manuel Malatyali, Friedhelm Meyer auf der Heide, and Sören Riechers. “Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times.” In *Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)*, 10787:207–22. Lecture Notes in Computer Science. Springer, 2017. https://doi.org/10.1007/978-3-319-89441-6.","ieee":"A. Mäcker, M. Malatyali, F. Meyer auf der Heide, and S. Riechers, “Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times,” in *Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)*, 2017, vol. 10787, pp. 207–222.","ama":"Mäcker A, Malatyali M, Meyer auf der Heide F, Riechers S. Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times. In: *Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)*. Vol 10787. Lecture Notes in Computer Science. Springer; 2017:207-222. doi:10.1007/978-3-319-89441-6"},"page":"207-222","language":[{"iso":"eng"}],"accept":"1","author":[{"full_name":"Mäcker, Alexander","first_name":"Alexander","last_name":"Mäcker","id":"13536"},{"last_name":"Malatyali","first_name":"Manuel","full_name":"Malatyali, Manuel"},{"id":"15523","last_name":"Meyer auf der Heide","first_name":"Friedhelm","full_name":"Meyer auf der Heide, Friedhelm"},{"last_name":"Riechers","first_name":"Sören","full_name":"Riechers, Sören"}],"_id":"79","intvolume":" 10787","file":[{"file_id":"5289","success":1,"access_level":"closed","open_access":1,"content_type":"application/pdf","file_name":"Non-clairvoyantSchedulingToMin.pdf","date_created":"2018-11-02T14:59:22Z","date_updated":"2018-11-02T14:59:22Z","file_size":380629,"creator":"ups","relation":"main_file"}],"department":[{"_id":"63"}],"user_id":"477","abstract":[{"lang":"eng","text":"Consider a problem in which $n$ jobs that are classified into $k$ types arrive over time at their release times and are to be scheduled on a single machine so as to minimize the maximum flow time.The machine requires a setup taking $s$ time units whenever it switches from processing jobs of one type to jobs of a different type.We consider the problem as an online problem where each job is only known to the scheduler as soon as it arrives and where the processing time of a job only becomes known upon its completion (non-clairvoyance).We are interested in the potential of simple ``greedy-like'' algorithms.We analyze a modification of the FIFO strategy and show its competitiveness to be $\\Theta(\\sqrt{n})$, which is optimal for the considered class of algorithms.For $k=2$ types it achieves a constant competitiveness.Our main insight is obtained by an analysis of the smoothed competitiveness.If processing times $p_j$ are independently perturbed to $\\hat p_j = (1+X_j)p_j$, we obtain a competitiveness of $O(\\sigma^{-2} \\log^2 n)$ when $X_j$ is drawn from a uniform or a (truncated) normal distribution with standard deviation $\\sigma$.The result proves that bad instances are fragile and ``practically'' one might expect a much better performance than given by the $\\Omega(\\sqrt{n})$-bound."}],"file_date_updated":"2018-11-02T14:59:22Z","doi":"10.1007/978-3-319-89441-6","date_updated":"2019-01-03T13:18:36Z","publisher":"Springer","date_created":"2017-10-17T12:41:06Z","title":"Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times","volume":10787,"type":"conference","series_title":"Lecture Notes in Computer Science"}]