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

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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 and Prachi Jadhav. Article: Survey on Project Management System using Event based Scheduler and Ant Colony Optimization. International Journal of Computer Applications 133(17):32-35, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {R. S. Vairagade and Rohan Arora and Vinita Gaikwad and Divyansh Singh and Prachi Jadhav},
	title = {Article: Survey on Project Management System using Event based Scheduler and Ant Colony Optimization},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {17},
	pages = {32-35},
	month = {January},
	note = {Published by 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.

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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