Dear scheduling researcher,
We are delighted to announce the talk given by Benjamin Moseley
<http://www.andrew.cmu.edu/user/moseleyb/> (Carnegie Mellon).
The title is "Machine Learning for Scheduling".
The seminar will take place on Zoom on Wednesday, October 27 at 13:00 UTC.
Join Zoom Meeting
https://cesnet.zoom.us/j/96553570741?pwd=ZkcwUk1IZjM1R2xDSkNQUWtYOHNVdz09
<https://cesnet.zoom.us/j/96553570741?pwd=ZkcwUk1IZjM1R2xDSkNQUWtYOHNVdz09>
Meeting ID: 965 5357 0741
Passcode: 705618
You can follow the seminar online or offline on our Youtube channel as well:
https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A
The abstract follows.
This talk will discuss a model for augmenting algorithms with useful
predictions that go beyond worst-case bounds on the algorithm
performance. The model ensures predictions are formally learnable and
instance robust. Learnability guarantees that predictions can be
efficiently constructed from past data. Instance robustness formally
ensures a prediction is robust to modest changes in the problem input.
This talk will discuss predictions that satisfy these properties for
scheduling and resource augmentation. Algorithms developed break through
worst-case barriers with accurate predictions and have a graceful
degradation in performance when the error in the predictions grows.
The next talk in our series will be given by
Carlo Mannino (SINTEF & Oslo Uni.) | November 10 | Train Scheduling:
Models, decomposition methods and practice.
For more details, please visit https://schedulingseminar.com/
With kind regards
Zdenek, Mike and Guohua
--
Zdenek Hanzalek
Industrial Informatics Department,
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague,
Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
https://rtime.ciirc.cvut.cz/~hanzalek/
Dear scheduling researcher,
We are delighted to announce the talk given by Federico Della Croce
(DIGEP - Polito.it).
The title is "The Longest Processing Time Rule for Identical Parallel
Machines Revisited".
The seminar will take place on Zoom on Wednesday, October 13 at 13:00 UTC.
Join Zoom Meeting
https://cesnet.zoom.us/j/95567469894?pwd=cDcyZGRWaTVRTVptOEUvNlIrTUpOZz09
<https://cesnet.zoom.us/j/95567469894?pwd=cDcyZGRWaTVRTVptOEUvNlIrTUpOZz09>
Meeting ID: 955 6746 9894
Passcode: 138314
You can follow the seminar online or offline on our Youtube channel as well:
https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A
The abstract follows.
We consider the P||Cmax scheduling problem where the goal is to schedule
n jobs on m identical parallel machines to minimize makespan. We revisit
the famous Longest Processing Time (LPT) rule proposed by Graham in
1969. LPT requires sorting jobs in non-ascending order of processing
times and then assigning one job at a time to the machine whose load is
smallest so far. We provide new insights into LPT and discuss the
approximation ratio of a modification of LPT that improves Graham’s
bound. We use linear programming to analyze the approximation ratio of
our approach. This performance analysis can be seen as a valid
alternative to formal proofs based on analytical derivation. Also, we
derive from the proposed approach an O(n log n) time complexity
heuristic called SLACK. The heuristic splits the sorted job set in
tuples of m consecutive jobs (1,...,m; m+1,...,2m; etc.) and sorts the
tuples in non-increasing order of the difference (SLACK) between the
largest and smallest job in the tuple. Given this new ordering of the
job set, list scheduling is applied. This approach strongly outperforms
LPT on benchmark literature instances and is competitive with more
involved approaches such as COMBINE and LDM.
The next talk in our series will be given by
Benjamin Moseley <http://www.andrew.cmu.edu/user/moseleyb/> (Carnegie
Mellon) | October 27 | Machine Learning for Scheduling.
For more details, please visit https://schedulingseminar.com/
With kind regards
Zdenek, Mike and Guohua
--
Zdenek Hanzalek
Industrial Informatics Department,
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague,
Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
https://rtime.ciirc.cvut.cz/~hanzalek/