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An Annealed Genetic Algorithm for Multi Mode Resource Constrained Project Scheduling Problem
by
Vijay S Bilolikar,
Karuna Jain,
Mahesha R Sharma
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 1 |
| Year of Publication: 2012 |
| Authors: Vijay S Bilolikar, Karuna Jain, Mahesha R Sharma |
10.5120/9659-4080
|
Vijay S Bilolikar, Karuna Jain, Mahesha R Sharma . An Annealed Genetic Algorithm for Multi Mode Resource Constrained Project Scheduling Problem. International Journal of Computer Applications. 60, 1 ( December 2012), 36-42. DOI=10.5120/9659-4080
@article{
10.5120/9659-4080,
author = {
Vijay S Bilolikar,
Karuna Jain,
Mahesha R Sharma
},
title = { An Annealed Genetic Algorithm for Multi Mode Resource Constrained Project Scheduling Problem },
journal = {
International Journal of Computer Applications
},
issue_date = { December 2012 },
volume = { 60 },
number = { 1 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = {
36-42
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume60/number1/9659-4080/
},
doi = { 10.5120/9659-4080 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:31.664361+05:30
%A Vijay S Bilolikar
%A Karuna Jain
%A Mahesha R Sharma
%T An Annealed Genetic Algorithm for Multi Mode Resource Constrained Project Scheduling Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 1
%P 36-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a genetic algorithm (SA) for the classical multimode resource-constrained project scheduling problem (MRCPSP). The objective is makespan minimization of the project. A new precedence feasible multi point forward backward crossover operator is presented. SA and GA are used in tandem to ensure balance in exploration and exploitation of search space. SA is employed as local search procedure, due to its stochastic neighborhood selection strategy to escape local optima hence a good exploitation strategy; and GA as exploration strategy due to its large number of population. Computational investigations are carried out on standard data set problems from PSPLIB and it is proved that hybridization of GA with SA gives competitive performance when compared with currently available algorithms
References
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Index Terms
Computer Science
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