# Dynamic Load Dispatching in Distributed Power Grids, Provost Research Project

1. Development of an adaptive simulations framework for the optimal and near-optimal load dispatching in distributed energy power systems,
2. Identification of the best possible routing plans for the flow of electricity given 1) the dynamic demand/supply environment of the grid network, and 2) capacity and location requirements of conventional and renewable energy generation (i.e., fossil fuel-based energy generators, hydropower plants, photovoltaic (PV)-based solar generators and wind generation turbines), and storage units of various kinds (i.e., compressed air energy storage, large-scale super-capacitors, and hydro-pump).
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# Electric Utility Resource Planning: A Continuous-Discrete Approach

1. Development of a comprehensive simulation based decision making framework to determine the best possible combination of resource investments for electric power generation and storage capacities,
2. Development of a combined continuous-discrete modeling framework for processes of different nature that exist within this complex system,
3. Generation of multi-objective optimization techniques to help the utility companies conduct resource planning in a realistic simulation environment,
4. Illustration of the proposed simulation framework for the electric utility resource planning in the state of Florida.
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# Dynamic Data Driven Application System (DDDAS) Simulation, National Science Foundation Project

1. Development of a distributed, multi-scale simulation model for a large scale, complex semiconductor manufacturing supply chain on a grid framework,
2. Selection of a proper fidelity of a complex simulation model against available computational resources by incorporating dynamic data into the executing model,
3. Analysis of how decisions and disturbances impact the global performance of a supply chain, using multi-resolution hybrid models,
4. Development of smart sampling algorithms to determine the best possible fidelity level of the executing application,
5. Demonstration of the effect of modeling detail onto the corresponding analysis. Intensive experiments were conducted to study the relationship between (1) the levels of modeling detail, (2) the system dynamics, and (3) the selection of control tasks to increase the system efficiency.
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# SOFTSIM, a Testbed for Process-Driven and Simulation-Based Knowledge Conglomeration in Enterprise Software Development, National Science Foundation Project

1. Development of a novel simulation modeling framework, which allows the stakeholders to perform what-if analyses before making their decisions during the enterprise software development process,
2. Development of a framework to help project managers devise optimal workforce assignments considering both the short-term as well as long-term aspects in the software development process,
3. Illustration of the proposed simulation framework with a software enhancement request process in Kuali, which is an open source project currently under development by a consortium of 12 major universities.
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# Simulation-based Forecasting and Demand-Shaping for Samsung Mobile Phones, Samsung Project

1. Identification of the significant (signal) factors affecting the sales volume of Samsung phones,
2. Based on the analysis, suggesting recommendations on future policies in data management.
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# Capacity Planning and Operational Decision Making in Integrated Photovoltaic Generation with Storage, APS (Utility company) and Arizona Research Institute for Solar Energy (AZRise) Project

1. Development of a flexible capacity planning tool to help obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions,
2. Development of a hybrid (system dynamics and agent-based models) simulation with embedded meta-heuristic optimization.
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# Development of Time Synchronization Methods for Distributed Simulations

1. Studying traditional and advanced time synchronization methods for distributed simulations,
2. Developing an application of an adaptive time synchronization mechanism for distributed simulation of manufacturing supply chain systems.
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# Automatic Partitioning of Large Scale Simulations in Grid Computing for Run Time Reduction

1. Partitioning large-scale monolithic simulation models into multiple pieces automatically and executing them in a distributed computing environment to reduce their execution time without sacrificing from their accuracy,
2. Studying tradeoff between reduced internal computations vs. increased time synchronization requirements in partitioned models,
3. Development of a partitioning algorithm and its experimental testing in an extensive case study.
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# A Tabu-based Genetic Algorithm Approach to Analyze the Impact of Information Sharing on Hierarchical Decision Making in Manufacturing Supply Chains

1. Development of a solid framework for the analysis of the impact of information sharing on hierarchical decision making in a flexible manufacturing supply chain,
2. Identification of the particular sources of the information which has significant effect on the scheduling decision within a supply chain system.
3. Development of a Tabu-based genetic algorithm in order to obtain the optimal scheduling solution for each level of a supply chain where the output of the algorithm that is built for an upper level becomes the input for an algorithm that is built for a lower level.
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