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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
A New Machine Scheduling Problem with Temperature Loss
Adelaide, Australia
January 23-January 24
ISBN: 0-7695-3090-7
ASCII Text
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Danyu Bai, Lixin Tang, Meng Su, "A New Machine Scheduling Problem with Temperature Loss," International Workshop on Knowledge Discovery and Data Mining, pp. 662-666, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.
 
 
BibTex
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@article{ 10.1109/WKDD.2008.36,
author = {Danyu Bai and Lixin Tang and Meng Su},
title = {A New Machine Scheduling Problem with Temperature Loss},
journal ={International Workshop on Knowledge Discovery and Data Mining},
volume = {0},
year = {2008},
isbn = {0-7695-3090-7},
pages = {662-666},
doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.36},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Workshop on Knowledge Discovery and Data Mining
TI - A New Machine Scheduling Problem with Temperature Loss
SN - 0-7695-3090-7
SP662
EP666
A1 - Danyu Bai,
A1 - Lixin Tang,
A1 - Meng Su,
PY - 2008
VL - 0
JA - International Workshop on Knowledge Discovery and Data Mining
ER -
 
This paper considers a new problem of scheduling hot jobs with nonlinear Temperature Drop Curve which is more approximate to the real situation than linear Temperature Drop Curve to minimize the total temperature drop loss. In the problem, all jobs have the same Temperature Drop Curve and different processing times. In this paper, two cases of problems are studied. 1) For the case of jobs without release dates, we prove that the Shortest Processing Time first rule is optimal to the single-machine problem. And we extend the result to the parallel-machine problem. 2) For the case of jobs with release dates, the singlemachine problem is strongly NP-hard. And a heuristic, Modified Shortest Processing Time first, is proposed to deal with the problem. In order to verify the performance of the heuristic, a lower bound based on release times delaying is presented. Computational results show the effectiveness of the heuristic on a set of random test problems. Key words: Scheduling, Temperature Drop Curve, NP-hard, Total Temperature Drop Loss
Citation:
Danyu Bai, Lixin Tang, Meng Su, "A New Machine Scheduling Problem with Temperature Loss," wkdd, pp.662-666, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008
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