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Reseach Article

Stochastic Scheduling with Unknown Precedence Using Genetic Algorithm: A Modified Pittsburg Approach

Published on None 2011 by Madhu Kumari, Kamal K. Bharadwaj
journal_cover_thumbnail
International Symposium on Devices MEMS, Intelligent Systems & Communication
Foundation of Computer Science USA
ISDMISC - Number 6
None 2011
Authors: Madhu Kumari, Kamal K. Bharadwaj
0fdb154a-80d8-4c60-92fc-69959a9a9a36

Madhu Kumari, Kamal K. Bharadwaj . Stochastic Scheduling with Unknown Precedence Using Genetic Algorithm: A Modified Pittsburg Approach. International Symposium on Devices MEMS, Intelligent Systems & Communication. ISDMISC, 6 (None 2011), 36-42.

@article{
author = { Madhu Kumari, Kamal K. Bharadwaj },
title = { Stochastic Scheduling with Unknown Precedence Using Genetic Algorithm: A Modified Pittsburg Approach },
journal = { International Symposium on Devices MEMS, Intelligent Systems & Communication },
issue_date = { None 2011 },
volume = { ISDMISC },
number = { 6 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 36-42 },
numpages = 7,
url = { /proceedings/isdmisc/number6/3483-isdm143/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Symposium on Devices MEMS, Intelligent Systems & Communication
%A Madhu Kumari
%A Kamal K. Bharadwaj
%T Stochastic Scheduling with Unknown Precedence Using Genetic Algorithm: A Modified Pittsburg Approach
%J International Symposium on Devices MEMS, Intelligent Systems & Communication
%@ 0975-8887
%V ISDMISC
%N 6
%P 36-42
%D 2011
%I International Journal of Computer Applications
Abstract

Genetic Algorithm's meta heuristics and inherent parallel approach for exploration makes it a prominent candidate solution scheme for stochastic Scheduling. Their appropriateness to combat uncertainty in stochastic attributes of the problem domain and their robustness towards combinatorial optimization, especially for the problems with conflicting goals encourages researchers to apply these methods to NP hard class of resource constrained stochastic scheduling. Motivated by resource constrained stochastic scheduling problem we have considered an extended version of this research problem with unknown precedence among the entities to be scheduled and perishable resources. In this work, we have proposed a modified pittsburg approach of genetic algorithm for optimization and to learn precedence order among the entities involved .Efficiency and merits of the proposed scheme are evident from the results.

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Index Terms

Computer Science
Information Sciences

Keywords

Pittsburg Approach of Genetic Algorithm Greedy Scheduling Resource Constraint Scheduling