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:: Doctorate Defense ::

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      Resource Allocation in a Multi-Project System under Stochastic and Dynamic Conditions

  Advisor: Dr. Noori

  Co- Advisors: Dr. Mahdavi- Dr. Tavakouli Moghadam

  Internal Committee Members: Dr.Bagherpour, Dr.Jalali

  External Committee Members: Dr. Tahsiri, Dr. Rabbani

 

  Abstract:

 

  In this thesis, the resource allocation problem in dynamic PERT networks is modeled in various cases: dynamic PERT networks with finite capacity of concurrent projects (C o nstant Number of Projects In Process (CONPIP)), multi-class dynamic PERT networks with finite capacity, multi-server dynamic PERT networks with infinite capacity, multi-class dynamic PERT networks with infinite capacity. Moreover, the reactive resource allocation problem in dynamic PERT networks is also studied. In this research, it is assumed that the new projects are generated according to a Poisson process and activity durations are independent random variables with exponential distributions. Such system can be represented as a queuing network, where each activity of a project is performed at a devoted service station with only one server located in a node of the network.

  For modeling dynamic PERT networks in all cases, we first convert the network of queues into a stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to solve and find the completion time distribution for any particular project. Then, we propose a multi-objective model with conflict objectives to optimally control the resources allocated to the servers in all cases. It is impossible to solve these multi-objective continuous-time models optimally and consequently we apply metaheuristic models such as: particle swarm optimization (PSO) approach and simulated annealing (SA) algorithm, and also discrete-time approximation method to solve these, using a goal attainment technique.

 

  Keywords: Multi-project System, Dynamic PERT network; Queueing network; Markov processes; Multiple objective programming

 

 

 

 

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