{"user_id":"477","citation":{"apa":"Mäcker, A., Malatyali, M., Meyer auf der Heide, F., &#38; Riechers, S. (2017). Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times. In <i>Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)</i> (Vol. 10787, pp. 207–222). Springer. <a href=\"https://doi.org/10.1007/978-3-319-89441-6\">https://doi.org/10.1007/978-3-319-89441-6</a>","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 <i>Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)</i>, 2017, vol. 10787, pp. 207–222.","mla":"Mäcker, Alexander, et al. “Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times.” <i>Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)</i>, vol. 10787, Springer, 2017, pp. 207–22, doi:<a href=\"https://doi.org/10.1007/978-3-319-89441-6\">10.1007/978-3-319-89441-6</a>.","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: <i>Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)</i>. Vol 10787. Lecture Notes in Computer Science. Springer; 2017:207-222. doi:<a href=\"https://doi.org/10.1007/978-3-319-89441-6\">10.1007/978-3-319-89441-6</a>","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={<a href=\"https://doi.org/10.1007/978-3-319-89441-6\">10.1007/978-3-319-89441-6</a>}, 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} }","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 <i>Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)</i>, 10787:207–22. Lecture Notes in Computer Science. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-89441-6\">https://doi.org/10.1007/978-3-319-89441-6</a>.","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."},"publisher":"Springer","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subprojekt C4","_id":"16"},{"_id":"4","name":"SFB 901 - Project Area C"}],"department":[{"_id":"63"}],"series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"date_updated":"2022-01-06T07:03:47Z","has_accepted_license":"1","title":"Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times","file":[{"creator":"ups","file_name":"Non-clairvoyantSchedulingToMin.pdf","file_size":380629,"date_updated":"2018-11-02T14:59:22Z","file_id":"5289","access_level":"closed","success":1,"date_created":"2018-11-02T14:59:22Z","content_type":"application/pdf","relation":"main_file"}],"doi":"10.1007/978-3-319-89441-6","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."}],"volume":10787,"ddc":["000"],"year":"2017","_id":"79","intvolume":"     10787","status":"public","author":[{"full_name":"Mäcker, Alexander","last_name":"Mäcker","id":"13536","first_name":"Alexander"},{"full_name":"Malatyali, Manuel","last_name":"Malatyali","first_name":"Manuel"},{"first_name":"Friedhelm","id":"15523","full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide"},{"last_name":"Riechers","full_name":"Riechers, Sören","first_name":"Sören"}],"date_created":"2017-10-17T12:41:06Z","publication":"Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)","file_date_updated":"2018-11-02T14:59:22Z","type":"conference","page":"207-222"}