CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Survey on Project Management System using Event based Scheduler and Ant Colony Optimization

by R. S. Vairagade, Rohan Arora, Vinita Gaikwad, Divyansh Singh, Prachi Jadhav
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 17
Year of Publication: 2016
Authors: R. S. Vairagade, Rohan Arora, Vinita Gaikwad, Divyansh Singh, Prachi Jadhav
10.5120/ijca2016907948

R. S. Vairagade, Rohan Arora, Vinita Gaikwad, Divyansh Singh, Prachi Jadhav . Survey on Project Management System using Event based Scheduler and Ant Colony Optimization. International Journal of Computer Applications. 133, 17 ( January 2016), 32-35. DOI=10.5120/ijca2016907948

@article{ 10.5120/ijca2016907948,
author = { R. S. Vairagade, Rohan Arora, Vinita Gaikwad, Divyansh Singh, Prachi Jadhav },
title = { Survey on Project Management System using Event based Scheduler and Ant Colony Optimization },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 17 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number17/24008-2016907948/ },
doi = { 10.5120/ijca2016907948 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:31:32.146110+05:30
%A R. S. Vairagade
%A Rohan Arora
%A Vinita Gaikwad
%A Divyansh Singh
%A Prachi Jadhav
%T Survey on Project Management System using Event based Scheduler and Ant Colony Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 17
%P 32-35
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Resource allocation and tasks assignment to software development teams are very crucial and arduous activities that can affect a project's cost and completion time. Solution for such problem is NP-Hard and requires software managers to be supported with efficient tools that can perform such allocation and can resolve the software development project scheduling problem (SDPSP) more efficiently. Ant colony optimization (ACO) is a rapidly evolving meta-heuristic technique based on the real life behavior of ants and can be used to solve NP-Hard (SDPSP) problem. Different versions of ACO meta-heuristic have already been applied to the software project scheduling problem in the past that took various resources into account. We have applied elitist strategy of ACO (elitist ant system) for solving SDPSP in a parameter-constrained environment taking project's cost and duration into consideration. The objective of the ACO-SDPSP methodology allows software project managers and schedulers to assign most effective set of employees that can contribute in minimizing cost and duration of the software project. Experimental results show that the proposed ACO-SDPSP methodology is promising in achieving the desired results.

References
  1. Vahid Khodakarami, Norman Fenton, Martin Neil, “Project Scheduling:Imposed Approach to Incorporate Uncertainty using Bayesian Networks”(2007)”.
  2. Carl K.Chang,Hsin-Yi Jiang,Yu Di,Dan Zhu,Yujia Ge,“Time-line based model for software project scheduling with genetic lgorithms”(2008)
  3. C.K. Chang, H. Jiang, D. Zhu, Y. Di, and Y. Ge, “Time-line Based Model for Software Project Scheduling with Genetic Algorithms”, Information and Software Technology, vol. 50, pp. 1142-1154, 2008.
  4. C.K. Chang, C. Chao, M.J. Christensen, and T.T. Nguyen, “Software Project Management Net: A New Methodology on Software Management”, Proc. 22nd Ann. Int‟l Computer Software and Applications Conf., 1998.
  5. T. Stutzle and H. Hoos, “Max-Min Ant System”, Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.
  6. A. Barreto, C.M.L. Werner, “Staffing a Software Project: A Constraint Satisfation and Optimization-Based approach”, Computers and Operations Research, vol. 25, pp. 3073-3089, 2008.
  7. B. Boehm, Software Engineering Economics. Prentice-Hall, 1981.
  8. B. Boehm et al., Software Cost Estimation with COCOMO II. Prentice-Hall, 2000.
  9. A. Shtub, J.F. Bard, and S. Globerson, Project Management: Processes, Methodologies, and Economics, second ed. Prentice Hall, 2005.
  10. P. Brucker, A. Drexl, R. Mohring, K. Neumann, E. Pesch, “Resource-Constrained Project Scheduling: Notation, Classification, Models and Methods,” European J. Operational Research, vol. 112, pp. 3-41, 1999.
  11. C.K. Chang and M. Christensen, “A Net Practice for Software Project Management,” IEEE Software, vol. 16, no. 6, pp. 80-88, Nov./Dec. 1999.
  12. C.K. Chang, M.J. Christensen, C. Chao, and T.T. Nguyen, “Software Project Management Net: A New Methodology on Software Management,” Proc. 22nd Ann. Int’l Computer Software and Applications Conf., 1998.
  13. A. Kumar V.K. and L.S. Ganesh, “Use of Petri Nets for Resource Allocation in Projects,” IEEE Trans. Eng. Management, vol. 45, no. 1, pp. 49-56, Feb. 1998.
  14. L.C. Liu and E. horowitz, “A Formal Model for Software Project Management,” IEEE Trans. Software Eng., vol. 15, no. 10, pp. 1280- 1293, Oct. 1989.
  15. L. Ozdamar, “A Genetic Algorithm Approach to a General Category Project Scheduling Problem,” IEEE Trans. Systems, Man, and Cybernetics-Part C: Applications and Rev., vol. 29, no. 1, pp. 44-59, Feb. 1999.
  16. R. Bai, E.K. Burke, G. Kendall, J. Li, and B. McCollum, “A Hybrid Evolutionary Approach to the Nurse Rostering Problem,” IEEE Trans. Evolutionary Computation, vol. 14, no. 4, pp. 580-590, Aug. 2010.
  17. V. Nissen and M. Gu¨ nther, “Automatic Generation of Optimised Working Time Models in Personnel Planning,” Proc. Seventh Int’l Conf. Swarm Intelligence, pp. 384-391, 2010.
  18. M. Gu¨ nther and V. Nissen, “Particle Swarm Optimization and an Agent-Based Algorithm for a Problem of Staffing Scheduling,” Proc. Int’l Conf. Applications of Evolutionary Computation, pp. 451-461, 2010.
  19. C.K. Chang, M.J. Christensen, and T. Zhang, “Genetic Algorithms for Project Management,” Annals of Software Eng., vol. 11, pp. 107-139, 2001.
  20. G. Antoniol, M. Di Penta, and M. Harman, “Search-Based Techniques Applied to Optimization of Project Planning for a Massive Maintenance Project,” Proc. 21st IEEE Int’l Conf. Software Maintenance, 2005.
  21. G. Antoniol, M. Di Penta, and M. Harman, “Search-Based Techniques for Optimizing Software Project Resource Allocation,” Genetic and Evolutionary Computation, vol. 3103, pp. 1425-1436, 2004.
  22. Y. Ge, “Software Project Rescheduling with Genetic Algorithms,” Proc. Int’l Conf. Artificial Intelligence and Computational Intelligence, 2009.
  23. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Trans. Systems Man, and Cybernetics-Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, Feb. 1996
Index Terms

Computer Science
Information Sciences

Keywords

PERT: Program Evaluation and Review Technique CPM :Critical Path Method RCPSP: Resource Constrained Project Scheduling Problem  EBS: Event Based Scheduler ACO:Ant Colony Optimization

Powered by PhDFocusTM