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Title: Joint channel and data estimation: genetic algorithm based blind equalization.
Creator: Caimi, F. M., Wang, D., Harbor Branch Oceanographic Institute
Date Issued: 1999
PURL: http://purl.flvc.org/FCLA/DT/3183717
Subject Headings: Genetic algorithms
Format: Document (PDF)

Title: Performance analysis of the genetic algorithm and its applications.
Creator: Liu, Xinggang., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Research and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the...
Show moreResearch and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the modified genetic algorithm and hybridized genetic algorithm. A number of typical function optimization problems are solved by these genetic algorithms. Ample empirical data associated with various modifications to the simple genetic algorithm is also provided. Results from this research can be used to assist practitioners in their applications of genetic algorithms.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15210
Subject Headings: Genetic algorithms, Combinatorial optimization
Format: Document (PDF)

Title: Optimal planning of robot calibration experiments by genetic algorithms.
Creator: Huang, Weizhen., Florida Atlantic University, Wu, Jie
Abstract/Description: In this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration...
Show moreIn this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration selection problem in which the robot workspace constraints are defined in terms of robot joint limits. The genetic parameters are tuned in a systematic way to greatly enhance the performance of the algorithm. A recruit-oriented genetic algorithm is then proposed, together with new genetic operators. Examples are also given to illustrate the concepts of this new genetic algorithm. This new algorithm is aimed at solving another type of configuration selection problem, in which not all points in the robot workspace are measurable by an external measuring device. Extensive simulation studies are conducted for both classical and recruit-oriented genetic algorithms, to examine the effectiveness of these algorithms.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15186
Subject Headings: Genetic algorithms, Robots--Calibration, Combinatorial optimization
Format: Document (PDF)

Title: Information-theoretics based genetic algorithm: Application to Hopfield's associative memory model of neural networks.
Creator: Arredondo, Tomas Vidal., Florida Atlantic University, Neelakanta, Perambur S.
Abstract/Description: This thesis refers to a research addressing the use of information-theoretic techniques in optimizing an artificial neural network (ANN) via a genetic selection algorithm. Pertinent studies address emulating relevant experiments on a test ANN (based on Hopfield's associative memory model) wherein the said optimization is tried with different sets of control parameters. These parameters include a new entity based on the concept of entropy as conceived in the field of information theory. That...
Show moreThis thesis refers to a research addressing the use of information-theoretic techniques in optimizing an artificial neural network (ANN) via a genetic selection algorithm. Pertinent studies address emulating relevant experiments on a test ANN (based on Hopfield's associative memory model) wherein the said optimization is tried with different sets of control parameters. These parameters include a new entity based on the concept of entropy as conceived in the field of information theory. That is, the mutual entropy (Shannon entropy) or information-distance (Kullback-Leibler-Jensen distance) measure between a pair of candidates is considered in the reproduction process of the genetic algorithm (GA) and adopted as a selection-constraint parameter. The research envisaged further includes a comparative analysis of the test results which indicate the importance of proper parameter selection to realize an optimal network performance. It also demonstrates the ability of the concepts proposed here in developing a new neural network approach for pattern recognition problems.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15397
Subject Headings: Neuro network (Computer science), Genetic algorithms
Format: Document (PDF)

Title: Novel Techniques in Genetic Programming.
Creator: Fernandez, Thomas, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Three major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a...
Show moreThree major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a paper[36] for the Federal Reserve Bank, authors Neely and Weller state “a perennial problem with using flexible, powerful search procedures like Genetic Programming is overfitting, the finding of spurious patterns in the data. Given the well-documented tendency for the genetic program to overfit the data it is necessary to design procedures to mitigate this.” The third is the difficulty of determining optimal control parameters for the Genetic Programming process. Control parameters control the evolutionary process. They include settings such as, the size of the population and the number of generations to be run. In his book[45], Banzhaf describes this problem, “The bad news is that Genetic Programming is a young field and the effect of using various combinations of parameters is just beginning to be explored.” We address these problems by implementing and testing a number of novel techniques and improvements to the Genetic Programming process. We conduct experiments using data sets of various degrees of difficulty to demonstrate success with a high degree of statistical confidence.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012570
Subject Headings: Evolutionary programming (Computer science), Genetic algorithms, Genetic programming (Computer science)
Format: Document (PDF)

Title: Applications of evolutionary algorithms in mechanical engineering.
Creator: Nelson, Kevin M., Florida Atlantic University, Huang, Ming Z., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Many complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve...
Show moreMany complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve complex engineering problems by optimizing the entire system, rather than sub-components of the system. Three standard forms of evolutionary algorithms have been developed: evolutionary programming, genetic algorithms and evolution strategies. Mathematical and algorithmic details are described for each of these methods. In this dissertation, four engineering problems are explored using evolutionary programming and genetic algorithms. Exploiting the inherently parallel nature of evolution, a parallel version of evolutionary programming is developed and implemented on the MasPar MP-1. This parallel version is compared to a serial version of the same algorithm in the solution of a trial set of unimodal and multi-modal functions. The parallel version had significantly improved performance over the serial version of evolutionary programming. An evolutionary programming algorithm is developed for the solution of electronic part placement problems with different assembly devices. The results are compared with previously published results for genetic algorithms and show that evolutionary programming can successfully solve this class of problem using fewer genetic operators. The finite element problem is cast into an optimization problem and an evolutionary programming algorithm is developed to solve 2-D truss problems. A comparison to LU-decomposition showed that evolutionary programming can solve these problems and that it has the capability to solve the more complex nonlinear problems. Finally, ordinary differential equations are discretized using finite difference representation and an objective function is formulated for the application of evolutionary programming and genetic algorithms. Evolutionary programming and genetic algorithms have the benefit of permitting over-constraining a problem to obtain a successful solution. In all of these engineering problems, evolutionary algorithms have been shown to offer a new solution method.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/12514
Subject Headings: Mechanical engineering, Genetic algorithms, Evolutionary programming (Computer science)
Format: Document (PDF)

Title: A method for the optimization of product development resource allocation.
Creator: Worp, Nicholas Jacob., Florida Atlantic University, Han, Chingping (Jim), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis presents a model designed to optimize the allocation of corporate resources required for the success of a product in the marketplace. The product development resources used in the model are: market research, applied research, product design, cost reduction and advertising. The key goals of this thesis are to provide industry with a usable tool: (1) Implement strategic plans through effective budgeting; (2) Optimize both short and long term profits; (3) Evaluate the impact of...
Show moreThis thesis presents a model designed to optimize the allocation of corporate resources required for the success of a product in the marketplace. The product development resources used in the model are: market research, applied research, product design, cost reduction and advertising. The key goals of this thesis are to provide industry with a usable tool: (1) Implement strategic plans through effective budgeting; (2) Optimize both short and long term profits; (3) Evaluate the impact of resource inter-dependencies; (4) Enable accountability that leads to goal achievement and checks unnecessary growth; (5) Remove much of the negative political and emotional variability; (6) Easily adapt to internal and external changes; (7) Output a specific allocation for each resource as a percentage of sales; (8) Output an estimate of future profitability. Genetic Algorithms are particularly well suited for this application because an exact optima is not required and the search space can be extremely large, complex, and non-linear.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15519
Subject Headings: Genetic algorithms, Resource allocation, Strategic planning, Business planning
Format: Document (PDF)

Title: Software reliability engineering: An evolutionary neural network approach.
Creator: Hochman, Robert., Florida Atlantic University, Khoshgoftaar, Taghi M.
Abstract/Description: This thesis presents the results of an empirical investigation of the applicability of genetic algorithms to a real-world problem in software reliability--the fault-prone module identification problem. The solution developed is an effective hybrid of genetic algorithms and neural networks. This approach (ENNs) was found to be superior, in terms of time, effort, and confidence in the optimality of results, to the common practice of searching manually for the best-performing net. Comparisons...
Show moreThis thesis presents the results of an empirical investigation of the applicability of genetic algorithms to a real-world problem in software reliability--the fault-prone module identification problem. The solution developed is an effective hybrid of genetic algorithms and neural networks. This approach (ENNs) was found to be superior, in terms of time, effort, and confidence in the optimality of results, to the common practice of searching manually for the best-performing net. Comparisons were made to discriminant analysis. On fault-prone, not-fault-prone, and overall classification, the lower error proportions for ENNs were found to be statistically significant. The robustness of ENNs follows from their superior performance over many data configurations. Given these encouraging results, it is suggested that ENNs have potential value in other software reliability problem domains, where genetic algorithms have been largely ignored. For future research, several plans are outlined for enhancing ENNs with respect to accuracy and applicability.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15474
Subject Headings: Neural networks (Computer science), Software engineering, Genetic algorithms
Format: Document (PDF)

Title: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
Creator: Thakkar, Jay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data...
Show moreDuring the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174078
Subject Headings: Neural networks (Computer science), Scientific applications, GMDH algorithms, Pattern recognition systems, Genetic algorithms, Fuzzy logic
Format: Document (PDF)

Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: A novel optimization algorithm and other techniques in medicinal chemistry.
Creator: Santos, Radleigh G., Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: In this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon...
Show moreIn this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon ARS. In Chapter 2, we use DARS and other methods to address issues arising from the use of mixture-based combinatorial libraries in drug discovery. In particular, we look at models associated with the biological activity of these mixtures and use them to answer questions about sensitivity and robustness, and also present a novel method for determining the integrity of the synthesis. Finally, in Chapter 3 we present an in-depth analysis of some statistical and mathematical techniques in combinatorial chemistry, including a novel probabilistic approach to using structural similarity to predict the activity landscape.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3352830
Subject Headings: Drugs, Design, Mathematical models, Combinatorial optimization, Combinatorial chemistry, Genetic algorithms, Mathematical optimization, Stochastic processes
Format: Document (PDF)

Title: Real-Time Localization of a Magnetic Anomaly: A Study of the Effectiveness of a Genetic Algorithm for Implementation on an Autonomous Underwater Vehicle.
Creator: Philippeaux, Harryel Arsene, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The primary objective of this research is to investigate the viability of magnetic anomaly localization with an autonomous underwater vehicle, using a genetic algorithm (GA). The localization method, first proposed by Sheinker. et al. 2008, is optimized here for the case of a moving platform. Extensive magnetic field modeling and algorithm simulation has been conducted and yields promising results. Field testing of the method is conducted with the use of the Ocean Floor Geophysics Self...
Show moreThe primary objective of this research is to investigate the viability of magnetic anomaly localization with an autonomous underwater vehicle, using a genetic algorithm (GA). The localization method, first proposed by Sheinker. et al. 2008, is optimized here for the case of a moving platform. Extensive magnetic field modeling and algorithm simulation has been conducted and yields promising results. Field testing of the method is conducted with the use of the Ocean Floor Geophysics Self-Compensating Magnetometer (SCM). Extensive out-of-water field testing is conducted to validate the ability to measure a target signal in a uniform NED frame as well as to validate the effectiveness of the GA. The outcome of the simulation closely matches the results of the conducted field tests. Additionally, the SCM is fully integrated with FAU’s Remus 100 AUV and preliminary in-water testing of the system has been conducted.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00005948
Subject Headings: Dissertations, Academic -- Florida Atlantic University, Autonomous underwater vehicles, Genetic algorithms., Geomagnetic field, Geomagnetism.
Format: Document (PDF)

Title: Object recognition by genetic algorithm.
Creator: Li, Jianhua., Florida Atlantic University, Han, Chingping (Jim), Zhuang, Hanqi, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Vision systems have been widely used for parts inspection in electronics assembly lines. In order to improve the overall performance of a visual inspection system, it is important to employ an efficient object recognition algorithm. In this thesis work, a genetic algorithm based correlation algorithm is designed for the task of visual electronic parts inspection. The proposed procedure is composed of two stages. In the first stage, a genetic algorithm is devised to find a sufficient number of...
Show moreVision systems have been widely used for parts inspection in electronics assembly lines. In order to improve the overall performance of a visual inspection system, it is important to employ an efficient object recognition algorithm. In this thesis work, a genetic algorithm based correlation algorithm is designed for the task of visual electronic parts inspection. The proposed procedure is composed of two stages. In the first stage, a genetic algorithm is devised to find a sufficient number of candidate image windows. For each candidate window, the correlation is performed between the sampled template and the image pattern inside the window. In the second stage, local searches are conducted in the neighborhood of these candidate windows. Among all the searched locations, the one that has a highest correlation value with the given template is selected as the best matched location. To apply the genetic algorithm technique, a number of important issues, such as selection of a fitness function, design of a coding scheme, and tuning of genetic parameters are addressed in the thesis. Experimental studies have confirmed that the proposed GA-based correlation method is much more effective in terms of accuracy and speed in locating the desired object, compared with the existing Monte-Carlo random search method.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15225
Subject Headings: Genetic algorithms, Robots--Control systems, Computer vision, Quality control--Optical methods
Format: Document (PDF)

Title: A simplistic approach to reactive multi-robot navigation in unknown environments.
Creator: MacKunis, William Thomas., Florida Atlantic University, Raviv, Daniel, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Multi-agent control is a very promising area of robotics. In applications for which it is difficult or impossible for humans to intervene, the utilization of multi-agent, autonomous robot groups is indispensable. This thesis presents a novel approach to reactive multi-agent control that is practical and elegant in its simplicity. The basic idea upon which this approach is based is that a group of robots can cooperate to determine the shortest path through a previously unmapped environment by...
Show moreMulti-agent control is a very promising area of robotics. In applications for which it is difficult or impossible for humans to intervene, the utilization of multi-agent, autonomous robot groups is indispensable. This thesis presents a novel approach to reactive multi-agent control that is practical and elegant in its simplicity. The basic idea upon which this approach is based is that a group of robots can cooperate to determine the shortest path through a previously unmapped environment by virtue of redundant sharing of simple data between multiple agents. The idea was implemented with two robots. In simulation, it was tested with over sixty agents. The results clearly show that the shortest path through various environments emerges as a result of redundant sharing of information between agents. In addition, this approach exhibits safeguarding techniques that reduce the risk to robot agents working in unknown and possibly hazardous environments. Further, the simplicity of this approach makes implementation very practical and easily expandable to reliably control a group comprised of many agents.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13013
Subject Headings: Robots--Control systems, Intelligent control systems, Genetic algorithms, Parallel processing (Electronic computers)
Format: Document (PDF)

Title: Evolutionary algorithms for design and control of material handling and manufacturing systems.
Creator: Kanwar, Pankaj., Florida Atlantic University, Han, Chingping (Jim), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The crucial goal of enhancing industrial productivity has led researchers to look for robust and efficient solutions to problems in production systems. Evolving technologies has also, led to an immediate demand for algorithms which can exploit these developments. During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution...
Show moreThe crucial goal of enhancing industrial productivity has led researchers to look for robust and efficient solutions to problems in production systems. Evolving technologies has also, led to an immediate demand for algorithms which can exploit these developments. During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies and neural networks. The emergence of massively parallel systems has made these inherently parallel algorithms of high practical interest. The advantages offered by these algorithms over other classical techniques has resulted in their wide acceptance. These algorithms have been applied for solving a large class of interesting problems, for which no efficient or reasonably fast algorithm exists. This thesis extends their usage to the domain of production research. Problems of high practical interest in the domain of production research are solved using a subclass of these algorithms i.e. those based on the principle of evolution. The problems include: the flowpath design of AGV systems and vehicle routing in a transportation system. Furthermore, a Genetic Based Machine Learning (GBML) system has been developed for optimal scheduling and control of a job shop.
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Date Issued: 1994
PURL: http://purl.flvc.org/fcla/dt/15025
Subject Headings: Industrial productivity--Data processing, Algorithms, Genetic algorithms, Motor vehicles--Automatic location systems, Materials handling--Computer simulation, Manufacturing processes--Computer simulation
Format: Document (PDF)

Title: Modeling strategic resource allocation in probabilistic global supply chain system with genetic algorithm.
Creator: Damrongwongsiri, Montri., Florida Atlantic University, Han, Chingping (Jim), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Effective and efficient supply chain management is essential for domestic and global organizations to compete successfully in the international market. Superior inventory control policies and product distribution strategies along with advanced information technology enable an organization to collaborate distribution and allocation of inventory to gain a competitive advantage in the world market. Our research establishes the strategic resource allocation model to capture and encapsulate the...
Show moreEffective and efficient supply chain management is essential for domestic and global organizations to compete successfully in the international market. Superior inventory control policies and product distribution strategies along with advanced information technology enable an organization to collaborate distribution and allocation of inventory to gain a competitive advantage in the world market. Our research establishes the strategic resource allocation model to capture and encapsulate the complexity of the modern global supply chain management problem. A mathematical model was constructed to depict the stochastic, multiple-period, two-echelon inventory with the many-to-many demand-supplier network problem. The model simultaneously constitutes the uncertainties of inventory control and transportation parameters as well as the varying price factors. A genetic algorithm (GA) was applied to derive optimal solutions through a two-stage optimization process. Practical examples and solutions from three sourcing strategies (single sourcing, multiple sourcing, and dedicated system) were included to illustrate the GA based solution procedure. Our model can be utilized as a collaborative supply chain strategic planning tool to efficiently determine the appropriate inventory allocation and a dynamic decision making process to effectively manage the distribution plan.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/12056
Subject Headings: Business logistics--Mathematical models, Physical distribution of goods--Management, Inventory control--Mathematical models, Genetic algorithms
Format: Document (PDF)

Title: A genetic algorithm for non-constrained process and economic process optimization.
Creator: Chirdchid, Sangthen., Florida Atlantic University, Masory, Oren, Mazouz, Abdel Kader, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Improving the quality of a product and manufacturing processes at a low cost is an economic and technological challenge which quality engineers and researches must contend with. In general, the quality of products and their cost are the main concerns for manufactures. This is because improving quality is very crucial for staying competitive and improving the organization's market position. However, some difficulty of finding where the standard of good quality is still remains. Customers'...
Show moreImproving the quality of a product and manufacturing processes at a low cost is an economic and technological challenge which quality engineers and researches must contend with. In general, the quality of products and their cost are the main concerns for manufactures. This is because improving quality is very crucial for staying competitive and improving the organization's market position. However, some difficulty of finding where the standard of good quality is still remains. Customers' satisfaction is a key for setting up the quality target. One possible solution is to develop control limits, which are the limits for indicating the area of nonconforming product on the basis of minimizing the total cost or loss to the customer as well as to the manufacturer. Therefore, the goal of this dissertation is to develop an effective tool to improve a high quality of product while maintaining a minimum cost.
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Date Issued: 2004
PURL: http://purl.flvc.org/fau/fd/FADT12081
Subject Headings: Genetic algorithms, Quality of products--Cost effectiveness--Econometric models, Multivariate analysis, Taguchi methods (Quality control)
Format: Document (PDF)

FAU Theses and Dissertations (16)	+ -
HBOI Faculty Contributions (1)	+ -

Genetic algorithms (16)	+ -
Combinatorial optimization (3)	+ -
Neural networks (Computer science) (3)	+ -
Evolutionary programming (Computer science) (2)	+ -
Fuzzy logic (2)	+ -

GMDH algorithms (2)	+ -
Intelligent control systems (2)	+ -
Pattern recognition systems (2)	+ -
Robots--Control systems (2)	+ -
Algorithms (1)	+ -
Autonomous underwater vehicles (1)	+ -
Business logistics--Mathematical models (1)	+ -
Business planning (1)	+ -
Combinatorial chemistry (1)	+ -
Computer vision (1)	+ -
Design (1)	+ -
Dissertations, Academic -- Florida Atlantic University (1)	+ -
Drugs (1)	+ -
Expert systems (Computer science) (1)	+ -
Genetic algorithms. (1)	+ -
Genetic programming (Computer science) (1)	+ -
Geomagnetic field (1)	+ -
Geomagnetism. (1)	+ -
Industrial productivity--Data processing (1)	+ -
Inventory control--Mathematical models (1)	+ -
Manufacturing processes--Computer simulation (1)	+ -
Materials handling--Computer simulation (1)	+ -
Mathematical models (1)	+ -
Mathematical optimization (1)	+ -
Mechanical engineering (1)	+ -

Electronic Thesis or Dissertation (16)	+ -
article (1)	+ -

Florida Atlantic University (13)	+ -
College of Engineering and Computer Science (12)	+ -
Department of Computer and Electrical Engineering and Computer Science (6)	+ -
Department of Ocean and Mechanical Engineering (6)	+ -
Han, Chingping (Jim) (4)	+ -

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