Masters Thesis Proposal by Krishna Neelakanta University of Colorado Colorado Springs Fall 2009 Page 1 Introduction TimeCost Tradeoff in Project Management Crashing a Project Schedule ID: 469486
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A Simulation based Optimization Study of Dynamic Crashing in Collaborative Projects Masters Thesis Proposal by
Krishna NeelakantaUniversity of Colorado, Colorado SpringsFall 2009
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IntroductionTime-Cost Tradeoff in Project ManagementCrashing a Project Schedule
Deployment of additional Resources to activities or other means to minimize costBalance Crashing costs and Penalty CostsAim to minimize total cost while delivering the project on timeHow can we model collaborative projects as a project network
Introduce dynamic re-evaluation of optimal crashing configuration for the project network
Evaluate the model via simulation
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Krishna Neelakanta Thesis ProposalSlide3
Krishna Neelakanta Thesis Proposal
Page 3A Typical Project Network
A Project Network with task dependenciesSlide4
Project Time-Cost Tradeoff Page 4
10/08/2004Krishna Neelakanta Thesis ProposalSlide5
Static vs Dynamic CrashingStatic Crashing
Evaluate critical pathRun an LP optimizer on the critical path to identify tasks that need to be crashedLP optimizer tries to minimize Project Penalty + Crashing costDynamic CrashingThe initial step is similar to Static CrashingRe-evaluate project network at different points in project network, by accounting for task completed or underway (sunken costs at that point in time)
Optimize the reminder of the project via LP optimizer
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Krishna Neelakanta Thesis ProposalSlide6
Related WorkCritical Path Method (CPM) and Project Evaluation Review Technique (PERT) have been around since 1950sCPM – Repeatable activities from previous experience. Low variance
PERT – More focus on research type projects and activity times are uncertain.PERT/CPM is now one area – Hillier and Lieberman [4]Simulation is a tool used commonly for Project Mgmt. Williams [5]Activity times are not certain. CPM falls short hereProject managers want to determine the optimal crashing configurationIndustrial Strength COMPASS (ISC) Hong and Nelson 2006 [6] – convergent, discrete optimization via simulation
Dynamic Crashing approach in simulation based optimization outlined in
Kuhl
et.al (2008) [7]. Basis for extending the work.
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Krishna Neelakanta Thesis ProposalSlide7
Goal of the ThesisDesign a model/template Project Network for Collaborative efforts.Address Bottlenecks, context based handoff’s, reviews, addition of tasks in mid project in the model
Develop a simulation-based dynamic optimization method for the generalized stochastic time-cost tradeoff decision problemShould include the initial evaluation to determine optimal configurationShould include dynamic re-evaluation to determine optimal configuration as the project progressesImplement the simulation-based optimization tool in a Project Management tool like Microsoft Project
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Krishna Neelakanta Thesis ProposalSlide8
Design a model/template Project Network for Collaborative efforts.The model should address bottlenecks, context based handoff’s, review processes, addition of tasks in mid projectBuild a simulator to test the model
Hook up the simulator to a proven industrial strength Linear Programming Optimizer to evaluate crashing points, optimal crashing configuration. Use the Industrial Strength COMPASS – Nelson et.al [8] Perform dynamic re-evaluation and crashing of the project networkQuestions that we would like to answer.
Distribution of project completion times, Project Costs and Savings
How does a Collaborative Project compare with a Traditional Project (network on Slide 3)
Integrate the proposed model into an Common Project Management Tool like MS Project
How does the model perform for larger project networks.
Krishna Neelakanta Thesis Proposal
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Thesis ApproachSlide9
Initial ParametersInitial Project NetworkOptimistic, Pessimistic and mean times for each activity and dependenciesSimulator uses above and a Beta Distribution to generate activity times during each run.
Project Cost is a linear function of completion time, penalty costs and crashing costsHighly dynamic nature of the Collaborative Process and reduction in centralized planning Autonomy of Collaborators and its reflection in the modelSimulation comparison and conclusions for Collaborative Model
vs
Traditional Model that is in Literature
Integration of the Collaborative Model Simulation into MS Project
10/08/2004
Krishna Neelakanta Thesis Proposal
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Assumptions & ConsiderationsSlide10
A simulator is to be built and tested. Language : C++.Simulator to incorporate, initial evaluation of Project Network, dynamic re-evaluation based on the LP optimizer. Idea is to minimize the cost functionSimulator should support task addition and in mid projectAllows user input to select characteristics of desired
project NetworkKrishna Neelakanta Thesis Proposal Page
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Project Network
SimulatorSlide11
Performed a literature Study of Simulation based Optimization in Project management and Collaboration effortsStudied related work in the area of collaborative computingStudied the Industrial Strength COMPASS (ISC), Linear Programming OptimizerInstalled a version of ISC and ran some tests on the sameBrushed up on Simulation Concepts and ideas
Installed Microsoft Project 2007.Exploring ways to integrate simulator and the optimizer system into Microsoft Project, by studying the API’s available
Krishna Neelakanta Thesis Proposal
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Current StatusSlide12
The simulation tool, and the design and implementation documentation.Integrating the Simulator to MS Project to help the Project Manager try the systemA thesis report documenting the design and implementation of Simulator System, related algorithms, the analysis of findings, and the lessons learned in the thesis, and future work to be done.
Krishna Neelakanta Thesis Proposal Page 12
Deliverables Slide13
Committee Proposal Approval – Dec 2009Develop model and design and implement simulator – Dec-Jan 2009Document findings, thesis report – Jan-Feb 201010/08/2004
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High Level TimelinesSlide14
Krishna Neelakanta Thesis Proposal Page 14
References [1] Haga, W. A. 1998.
Crashing PERT networks. Ph.D. Dissertation, University of Northern Colorado, Colorado.
[2] Haga
, W. A., and K. A. Marold. 2004. A simulation approach to the PERT CPM time-cost trade-off problem. Project Management Journal, 35(2): 31-37.
[3]
Haga
, W. A., and K. A.
Marold
.
2005. Monitoring and control of PERT networks. The Business Review, 3(2): 240-245.
[4] Hillier, F. S., and G. J. Lieberman. 2008.
Introduction to Operations Research. 9th Ed., McGraw-Hill, New York.
[5] Williams, T. (2004). Why Monte Carlo Simulations Of Project Networks Can Mislead.
Project Management Journal
, 35(3), 53-61
[6]
Xu
, J., B. L. Nelson, and L. J. Hong.
2007. Industrial Strength COMPASS: A Comprehensive Algorithm and
Software for Optimization via Simulation.
Website <http://users.iems.northwestern.edu/~nelsonb/ISC/>.
[7] Michael E.
Kuhl
,
Radhamés
A.
Tolentino-Peña
A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT
USING SIMULATION-BASED OPTIMIZATION
Proceedings of the 2008 Winter Simulation Conference Pg : 2370-2376
[8] Nelson, L.J. and B. L. Nelson.
2006. Discrete optimization via simulation using COMPASS. Operations Research,
54:115-129.