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New Applications for Logic planning of traditional and agile projects

New Applications for Logic planning of traditional and agile projects. Judit Kiss PhD candidate. Content of the presentation. Matlab applications. genetic algorithm based on GAlib. Project management approaches *. Software development, product development projects.

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New Applications for Logic planning of traditional and agile projects

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  1. New ApplicationsforLogicplanning of traditional and agileprojects Judit Kiss PhD candidate

  2. Content of thepresentation Matlabapplications genetic algorithm based on GAlib

  3. Project management approaches* Software development, productdevelopmentprojects Constructionprojects 20% 70% 10% R&D projects * Wysocki, Robert K.: Effective Project Management: Traditional, Agile, Extreme, Wiley Publishing, Inc., Indianapolis, Indiana, 5th ed., 2009, ISBN 978-0-470-42367-7.

  4. Process of traditional project planning

  5. Traditional vs. agile project planning Traditional project planning Agile project planning Fixed Scope Time Budget Variable Scope Budget Time (Dalcher, 2009, PMUni)

  6. Specialities of IT projects • Atlogicplanning prior experiencecan be reused • Stochastictaskswithstochasticdurations • More possible project scenarios • Realizingtaskscan be rankedbytheirimportance • Less importanttasks/functionscan be left out fromthe project • Stochastic relations betweentasks • More possible project structures • Taskscan be repeatedortasksequencescanbereversed • Flexibleorder of tasksequences, • Severaltaskscan be realizedparallelly and alsosequentially

  7. Matrix-based project planningmethods SNPM - Relations betweentaskscan be: 0: independent/parallel relation 0-1: uncertain/possiblerelation 1: certain/sequentialrelation PEM - Uncertainty of taskcan be: 0: taskcan be omitted 0-1: uncertaintask 1: certaintask • DSM * • SNPM ** • PEM *** *** Project Expert Matrix (J. Kiss – Zs. Kosztyán, 2009, Confenis, AVA) * DependencyStructureMatrix (Steward, 1981; dsmweb.org) ** Stochastic Network PlanningMethod (Zs.Kosztyán-J.Fejes-J.Kiss, 2008, Szigma)

  8. Project scenarios - Selectingthetasks Step 1 Budget … Selectedtasks: A, C, E, B, D

  9. Project structures – different relations Step 2 CriticalPathMethod PrecedenceDiagrammingMethod Generatingallpossible project structuresbasedonthematrixvalues GraphicalEvaluation and ReviewTechnique ExtendedEvent-drivenProcessChain …

  10. Selectingtheoptimalsolution Reorderingthetasks

  11. Project scenario and structureGenerating&RankingAlgorithm Step 1 Step 2 MatlabapplicationbyJ.Kiss, basedon PSSM algorithm(Kosztyán – Kiss, 2010, DSM) Fullevaluatingalgorithm

  12. AgileProjectPlanning Algorithm Step 1 Step 2 What? Whichtasks? How? Inwhichorder? Howlong? Howmuch? Matlabapplicationby J. Kiss, basedonthe APS algorithm(Kosztyán-Kiss, 2010, Vezetéstudomány)

  13. Matrix-based Project Planning Genetic Algorithm Population Selection Population of thenewgeneration Crossover, mutation Selection GA applicationby I. Borbás

  14. Genetic operators– Crossover #1 Genetic algorithm

  15. Genetic operators - Crossover#2

  16. Mutation Selection Geneticoperators • Negatingoneor more elements • TournamentSelector

  17. Resultsof the algorithmswithout constraints

  18. Resultsof the algorithms with constraints

  19. ... ... ... ...

  20. Novelty of my research • PEM matrix • Supportingthelogicplanningbyhandlingthepossibletaskoccurrances and possible relations • The possiblesolutionscan be generated and ranked • Logicplanscan be restructured • Applyablefortraditional and agileprojects • Matrix-basedapplicationsareuseful and applicableat PEM matrixwithhigheruncertaintyaswell. • APPAgivestheoptimalsolutionbasedonthevaluesinthe PEM. • MPPGA is practicaltoget a goodsolutiontakingdifferentconstraints and multipleobjectivefunctioninto account.

  21. Thankyouforyourkindattention. kissjudit@gtk.uni-pannon.hu

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