UNITED STATES
Multi Objective Genetic Algorithm for the optimized Resource usage and the Prioritization of the Constraints in the Software project planning
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10.5120/718-1010 |
D.Sundar, B.Umadevi and Dr.K.Alagarsamy. Article:Multi Objective Genetic Algorithm for the optimized Resource usage and the Prioritization of the Constraints in the Software project planning. International Journal of Computer Applications 3(3):1–4, June 2010. Published By Foundation of Computer Science. BibTeX
@article{key:article,
author = {D.Sundar and B.Umadevi and Dr.K.Alagarsamy},
title = {Article:Multi Objective Genetic Algorithm for the optimized Resource usage and the Prioritization of the Constraints in the Software project planning},
journal = {International Journal of Computer Applications},
year = {2010},
volume = {3},
number = {3},
pages = {1--4},
month = {June},
note = {Published By Foundation of Computer Science}
}
Abstract
Capability Maturity Model Integration (CMMI) helps organizations to improve software development processes. But it contains very little information on process dynamics. It doesn’t address the following issues like Specific tools, methods and technologies to be followed, Issues in Human resource management, People management methodology and cost attached to people manager. In this paper we focus on the real world problem in the systems development life cycle such as Organizations understaffed, Separation of duties. In order to overcome the problem we have suggested a Multi objective genetic algorithm (MOGA) for minimization of the human resources used. So inturn MOGA has been used to minimize the cost associated, minimize the time involved and to maximize the efficiency by proper usage .the Prioritization of the various constraints involved in the project is also done by the MOGA, which shows a good result over the manual allocation. The results compared with the manual assignment and the comparative results are reported and discussed, which shows the effectiveness of the proposed approach for the project planning.
Reference
-
H.Iima ,N.Sannomiya, “Proposition of Module Type Genetic Algorithm and Its Application to Modified Scheduling Problems with Worker Allocation” IEEE Japan ,Vol.122-C, pp.409-416,2002
A.Osawa , K. Ida ,“Scheduling Problem with Worker Allocation using Genetic Algorithm” , Japan-Australia workshop on intelligent and evolutionary systems, pp.1- 8 ,2005
H. Iima ,N.Sannomiya, “Module Type Genetic Algorithm for Modified Scheduling Problems with Worker Allocation” Proceedings of the American Control Conference Arlington, VA, 2001.
P. Brucker, A Drexl, R. Mohring, K. Neumann, E.Pesch, Resource-constrained project scheduling:Notation, classification, models and methods, European Journal of Operational Research, Vol.112 (1), 1999, pp. 3-41.
R. Kolisch and S. Hartmann, Experimental investigation of heuristics for resource-constrained project scheduling: an update, European Journal of Operational Research, Vol.174 (1), 2006, pp. 23-37.
D. Debels, B. De Reyck, R.Leus and M.Vanhoucke, A Hybrid Scatter Search/Electromagnetism Meta- Heuristic for Project Sheduling, European Journal of Operational Research, Vol. 169, 2006, pp. 638-653.
D. Debels and M. Vanhoucke. A Decomposition-BasedHeuristic for the Resource-Constrained Project Scheduling Problem. Working Paper 2005/293, Faculty of Economics and Business Administration, University of Ghent, Ghent, Belgium, 2005.
J.J.M. Mendes, J.F. Gonçalves and M.G.C. Resende, A random key based genetic algorithm for the resource constrained project scheduling problem, Computers & Operations Research, Vol. 36, 2009, pp. 92-109
K. Fleszar and K.S. Hindi, Solving the resource constrained project scheduling problem by a variable neighbourhood search, European Journal of Operational Research, Vol. 155, 2004, pp. 402-413.
J. Magalhaes-Mendes, “Project scheduling under multiple resources constraints using a genetic algorithm”, WSEAS TRANSACTIONS on BUSINESS and ECONOMICS, Issue 11, Volume 5, November 2008.
Turner J R. The hand book of project based management [M].London: Mc-Graw Hill,1993.
MaoYi Hua. Application for network optimization technique in construction claim management .
Garey M., Johnson D., Computers and Intractability: a Guide to the Theory of NP-Completeness. W.H. Freeman, 1979.
Milena Karova, Julka Petkova, Vassil Smarkov, “ A Genetic Algorithm for Project Planning Problem”, International Scientific Conference Computer Science’2008.
Holland J., Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, 1975.
D. A. Coley, “An Introduction to Genetic Algorithms for Scientists and Engineers”, New Jersey, 1999.
E. Zitzler, K. Deb, L. Thiele, Comparison of multi-objective evolutionary algorithms: empirical results, Evolutionary Computation 8 (2000) 125–148.
Ghosh, A. and S. Dehuri, 2004. Evolutionary algorithms for multi-criterion optimization: A survey. Intl. J. Comp. Inform. Sci., 2: 38–57.
