Monday, October 11, 2021

[DMANET] [Scheduling seminar] Federico Della Croce (DIGEP - Polito.it) | October 13 | The Longest Processing Time Rule for Identical Parallel Machines Revisited

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/


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