Project scheduling conflict identification and resolution using genetic algorithms (GA)

Project management is one very critical task of development activity. Project management is traditionally defined as the discipline of planning, organizing, and managing activities and resources for successful execution and completion of project goals and objectives. In this respect, project management holds a key position in satisfactory completion of projects. Project management for software holds the same importance in software development. That is the reason that we have a complete knowledge domain we know as software project management (SPM). Software project management aims to achieve all the project goals and objectives while working within the constraints posed by project environment and stakeholders. These constraints include (but not limited to) time, scope, resources, resource allocation and optimization etc. While managing software projects, it is natural to be confronted with various conflicts of different natures. A good project management activity is one which can effectively foresee these conflicts and resolve them in an optimal fashion. In this paper, a genetic algorithm based technique for conflict identification and resolution for project activities has been proposed. The effectiveness and utility of such a technique has also been discussed in this paper.

[1]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[2]  John C. Reynolds.  Some thoughts on teaching programming and programming languages , 2008, SIGP.

[3]  Michael R. Lyu,et al.  Achieving software quality with testing coverage measures , 1994, Computer.

[4]  Andrew Stellman,et al.  Applied software project management , 2005 .

[5]  Des Greer,et al.  Quantitative studies in software release planning under risk and resource constraints , 2003, 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings..

[6]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[7]  Peter W. G. Morris,et al.  A Brief History of Project Management , 2011 .

[8]  Rogério Eduardo Garcia,et al.  Genetic algorithms to support software engineering experimentation , 2005, 2005 International Symposium on Empirical Software Engineering, 2005..

[9]  B. Turchiano,et al.  Genetic identification of dynamical systems with static nonlinearities , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[10]  Shuang-Hua Yang,et al.  Genetic algorithm based software integration with minimum software risk , 2006, Inf. Softw. Technol..

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  David E. Goldberg,et al.  Simplex crossover and linkage identification: single-stage evolution vs. multi-stage evolution , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[14]  B. Turchiano,et al.  GARA: A genetic algorithm with resolution adaptation for solving system identification problems , 2001, European Control Conference.

[15]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[16]  Yong-Ji Wang,et al.  Software Development Effort Estimation Using Fuzzy Logic - A Survey , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[17]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[18]  Luigi Troiano,et al.  Supporting software evolution by using fuzzy logic , 2004 .

[19]  John Yen,et al.  A Fuzzy Logic-based Methodology for the Acquisition and Analysis of Imprecise Requirements , 1994 .