Browsing by Subject "Genetic Algorithms"
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Item Concurrent design of facility layout and flow-based department formation(Texas A&M University, 2005-02-17) Chae, JunjaeThe design of facility layout takes into account a number of issues including the formation of departments, the layout of these, the determination of the material handling methods to be used, etc. To achieve an efficient layout, these issues should be examined simultaneously. However, in practice, these problems are generally formulated and solved sequentially due to the complicated nature of the integrated problem. Specifically, there is close interaction between the formation of departments and layout of these departments. These problems are treated as separate problems that are solved sequentially. This procedure is mainly due to the complexity of each problem and the interrelationships between them. In this research, we take a first step toward integrating the flow-based department formation and departmental layout into comprehensive mathematical models and develop appropriate solution procedures. It is expected that these mathematical models and the solution procedures developed will generate more efficient manufacturing system designs, insights into the nature of the concurrent facility layout problem, and new research directions.Item Improving Network Reliability: Analysis, Methodology, and Algorithms(2010-07-14) Booker, Graham B.The reliability of networking and communication systems is vital for the nation's economy and security. Optical and cellular networks have become a critical infrastructure and are indispensable in emergency situations. This dissertation outlines methods for analyzing such infrastructures in the presence of catastrophic failures, such as a hurricane, as well as accidental failures of one or more components. Additionally, it presents a method for protecting against the loss of a single link in a multicast network along with a technique that enables wireless clients to efficiently recover lost data sent by their source through collaborative information exchange. Analysis of a network's reliability during a natural disaster can be assessed by simulating the conditions in which it is expected to perform. This dissertation conducts the analysis of a cellular infrastructure in the aftermath of a hurricane through Monte-Carlo sampling and presents alternative topologies which reduce resulting loss of calls. While previous research on restoration mechanisms for large-scale networks has mostly focused on handling the failures of single network elements, this dissertation examines the sampling methods used for simulating multiple failures. We present a quick method of nding a lower bound on a network's data loss through enumeration of possible cuts as well as an efficient method of nding a tighter lower bound through genetic algorithms leveraging the niching technique. Mitigation of data losses in a multicast network can be achieved by adding redundancy and employing advanced coding techniques. By using Maximum Rank Distance (MRD) codes at the source, a provider can create a parity packet which is e ectively linearly independent from the source packets such that all packets may be transmitted through the network using the network coding technique. This allows all sinks to recover all of the original data even with the failure of an edge within the network. Furthermore, this dissertation presents a method that allows a group of wireless clients to cooperatively recover from erasures (e.g., due to failures) by using the index coding techniques.Item Orbit design and estimation for surveillance missions using genetic algorithms(Texas A&M University, 2006-04-12) Abdelkhalik, Osama Mohamed OmarThe problem of observing a given set of Earth target sites within an assigned time frame is examined. Attention is given mainly to visiting these sites as sub-satellite nadir points. Solutions to this problem in the literature require thrusters to continuously maneuver the satellite from one site to another. A natural solution is proposed. A natural solution is a gravitational orbit that enables the spacecraft to satisfy the mission requirements without maneuvering. Optimization of a penalty function is performed to find natural solutions for satellite orbit configurations. This penalty function depends on the mission objectives. Two mission objectives are considered: maximum observation time and maximum resolution. The penalty function poses multi minima and a genetic algorithm technique is used to solve this problem. In the case that there is no one orbit satisfying the mission requirements, a multi-orbit solution is proposed. In a multi-orbit solution, the set of target sites is split into two groups. Then the developed algorithm is used to search for a natural solution for each group. The satellite has to be maneuvered between the two solution orbits. Genetic algorithms are used to find the optimal orbit transfer between the two orbits using impulsive thrusters. A new formulation for solving the orbit maneuver problem using genetic algorithms is developed. The developed formulation searches for a mini mum fuel consumption maneuver and guarantees that the satellite will be transferred exactly to the final orbit even if the solution is non-optimal. The results obtained demonstrate the feasibility of finding natural solutions for many case studies. The problem of the design of suitable satellite constellation for Earth observing applications is addressed. Two cases are considered. The first is the remote sensing missions for a particular region with high frequency and small swath width. The second is the interferometry radar Earth observation missions. In satellite constellations orbit's design, a new set of compatible orbits, called the "Two-way orbits",whose ground track path is a closed-loop trajectory that intersects itself, in some points, with tangent intersections is introduced. Conditions are derived on the orbital elements such that these Two-way Orbits exist and satellites flying in these orbits pass the tangent intersection points at the same time. Finally, the recently proposed concept of observing a space object from onboard a spacecraft using a star tracker is considered. The measurements of the star tracker provide directions to the target in space and do not provide range measurements. Estimation for the orbit of the target space object using the measurements of the star tracker is developed. An observability analysis is performed to derive conditions on the observability of the system states. The Gaussian Least Squares Differential Correction Technique is implemented. The results obtained demonstrate the feasibility of using the measurements of the star tracker to get a good estimate for the target orbit within a period of measurements ranging from about 20 percent to 50 percent of the orbital period depending on the two orbits.Item Reliability assessment of electrical power systems using genetic algorithms(Texas A&M University, 2004-11-15) Samaan, Nader Amin AzizThe first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most probable failure states. GA stores system states, in which there is generation deficiency to supply system maximum load, in a state array. The given load pattern is then convoluted with the state array to obtain adequacy indices. In the second part of the dissertation, a GA based method for state sampling of composite generation-transmission power systems is introduced. Binary encoded GA is used as a state sampling tool for the composite power system network states. A linearized optimization load flow model is used for evaluation of sampled states. The developed approach has been extended to evaluate adequacy indices of composite power systems while considering chronological load at buses. Hourly load is represented by cluster load vectors using the k-means clustering technique. Two different approaches have been developed which are GA parallel sampling and GA sampling for maximum cluster load vector with series state revaluation. The developed GA based method is used for the assessment of annual frequency and duration indices of composite system. The conditional probability based method is used to calculate the contribution of sampled failure states to system failure frequency using different component transition rates. The developed GA based method is also used for evaluating reliability worth indices of composite power systems. The developed GA approach has been generalized to recognize multi-state components such as generation units with derated states. It also considers common mode failure for transmission lines. Finally, a new method for composite system state evaluation using real numbers encoded GA is developed. The objective of GA is to minimize load curtailment for each sampled state. Minimization is based on the dc load flow model. System constraints are represented by fuzzy membership functions. The GA fitness function is a combination of these membership values. The proposed method has the advantage of allowing sophisticated load curtailment strategies, which lead to more realistic load point indices.