" Energy efficiency-based course timetabling for university buildings,"Įnergy, Elsevier, vol. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017." A hyper-heuristic approach to sequencing by hybridization of DNA sequences,"Īnnals of Operations Research, Springer, vol. Jacek Blazewicz & Edmund Burke & Graham Kendall & Wojciech Mruczkiewicz & Ceyda Oguz & Aleksandra Swiercz, 2013.PLOS ONE, Public Library of Science, vol. " Automatically Generated Algorithms for the Vertex Coloring Problem," Carlos Contreras Bolton & Gustavo Gatica & Víctor Parada, 2013.Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kielĥ39, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
#Ischedule.. professional#
" Professional course scheduling in airline transport pilot training: A case from Lufthansa flight training," Schirmer, Andreas & Potzhar, Kathrin, 2001." Distribution requirements and compactness constraints in school timetabling,"Įuropean Journal of Operational Research, Elsevier, vol. Drexl, Andreas & Salewski, Frank, 1997." A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems,"Įuropean Journal of Operational Research, Elsevier, vol. Zhang, Defu & Liu, Yongkai & M'Hallah, Rym & Leung, Stephen C.H., 2010.275(1), pages 145-160, April.Įuropean Journal of Operational Research, Elsevier, vol. " Practices in timetabling in higher education institutions: a systematic review,"Īnnals of Operations Research, Springer, vol. " A survey of school timetabling research,"Īnnals of Operations Research, Springer, vol. These are the items that most often cite the same works as this one and are cited by the same works as this one. It also reduced the time required to generate a complete schedule to about two weeks. Our tool, iSchedule, generated a far richer set of feasible schedules, allowing administrators to focus on satisfying individual student needs. Prior to our engagement, school administrators spent approximately eight weeks each year manually generating schedules, a time commitment that was likely to grow as school enrollment increased. Educators have multiple competing objectives with trade-offs that they cannot quantify precisely therefore, our solution generates a diverse set of schedules that they can compare, combine, and change, before ultimately choosing one schedule to use.
#Ischedule.. series#
We took a graph-theoretic approach to this problem by developing an algorithm that breaks it into a series of subproblems and applies randomized direct heuristics. In particular, the educators in these schools wanted to create master schedules that could accommodate each student's specific needs while satisfying teacher and other resource constraints. These schools focus on individualized instruction, making the scheduling problem challenging and novel.
The New York City public school system needed a new tool that would generate schedules for a set of experimental schools.
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