Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems.

Cochlear implant (CI) users have traditionally reported less enjoyment and have performed more poorly on tasks of music perception (timbre, melody and pitch) than their normal hearing (NH) counterparts. The enjoyment and perception of music can be affected by the MAP programmed into a user’s speech processor, the parameters of which can be altered to change the way that a CI recipient hears sound. However, finding the optimal MAP can prove challenging to clinicians because altering one parameter will affect others. Until recently the only way to find the optimal MAP has theoretically been to present each potential combination of parameters systematically, however this is impractical in a clinical setting due to the thousands of different potential combinations. Thus, in general, clinicians can find a good MAP, but not necessarily the best one. The goal of this study was to assess whether a Genetic Algorithm would assist clinicians to create a better MAP for music listening than current methods. Seven adult Nucleus Freedom CI users were assessed on tasks of timbre identification, melody identification and pitch-ranking using their original MAP. The participants then used the GA software to create an individualised MAP for music listening (referred to as their “GA MAP”). They then spent four weeks comparing their GA and original MAPs in their everyday life, and recording their listening experiences in a listening diary. At the end of this period participants were assessed on the same timbre, melody, and pitch tasks using their GA MAP. The results of the study showed that the GA process took an average of 35 minutes (range: 13-72 minutes) to create a MAP for music listening. As a group, participants reported the GA MAP to be slightly better than their original MAP for music listening, and preferred the GA MAP when at the cinema. Participants, on average, also performed significantly better on the melody identification task with their GA MAP; however they were significantly better on the half-octave interval pitch ranking task with their original MAP. The results also showed that participants were significantly more accurate on the single-instrument identification task than the ensemble instrument identification task regardless of which MAP they used. Overall, the results show that a GA can be used to successfully create a MAP for music listening, with two participants creating a MAP that they decided to keep at the conclusion of the study.

Active flow control is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. Current research has combined different existing GA techniques and motivation from the two-jet GA-CFD system previously developed at the University of Kentucky propose the applications of a real coded Continuous Genetic Algorithm (CGA) to optimize a four-jet and a synthetic jet control system on a NACA0012 airfoil. The control system is an array of jets on a NACA0012 airfoil and the critical parameters considered for optimization are the angle, the amplitude, the location, and the frequency of the jets. The design parameters of a steady four-jet and an unsteady synthetic jet system are proposed and optimized. The proposed algorithm is built on top of CFD code (GHOST), guiding the movement of jets along the airfoils upper surface. The near optimum control values are determined within the control parameter range. The current study of different Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application.

Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems.

Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.

The speed control of a Brushless DC Motor Drive in the presence of load disturbance is investigated. Firstly some practical results are presented where a simple proportional-integral speed controller is used in the presence of a large step input speed demand as well as load disturbance. The wind-up problem caused by the saturation of the controller is discussed. In order to improve the performance of the proportional-integral speed controller in the presence of load variation, a load estimator is used with torque feedforward control. The results presented show the speed holding capability in the presence of load variation is significantly improved. A genetic algorithm is used on line to optimise the controller for different conditions such as large and small step input speed demand and load disturbance. The results presented show that a genetic algorithm is capable of finding the tuning of the controller for optimal performance. Single-input single-output and two-input two-output fuzzy speed controllers are also used and the results compared to a proportional-integral controller. Results are presented showing that a single-input single-output fuzzy controller works as a proportional controller with variable gain whereas the two-input two-output fuzzy controller is capable of driving the motor at variable speed and load torque with excellent performance. The robustness of the fuzzy controllers is compared to the proportional-integral controller and the results presented show that the fuzzy one is more robust then the proportional-integral. A genetic algorithm is also used on line for the optimisation of the two-input twooutput fuzzy speed controller and the results show that despite the large number of parameters to be optimised, the tuning for optimal performance is also possible.

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. Several researchers have investigated approaches for modeling the capabilities of a technology with the aim of supporting the design process. In these works, the information about the physical form is typically abstracted away. However, the efficient generation of an accurate model of technical capabilities remains a challenge. Pareto frontier based methods are often used but yield results that are of limited use for subsequent decision making and analysis. Models based on parameterized Pareto frontiers—termed Technology Characterization Models (TCMs)—are much more reusable and composable. However, there exists no efficient technique for modeling the parameterized Pareto frontier. The contribution of this thesis is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The novelty of the algorithm lies in a new concept termed predicted dominance. The proposed algorithm uses fundamental concepts from multi-objective optimization and machine learning to generate a model of the technology frontier.

Recent developments in materials and material joining [specifically Aluminum and Pulse Gas Metal Arc Welding (GMAW-P) technology] have increased the scope and extent of their areas of application. However, stern market demand for the improved weld quality necessitates the need for automation of the welding processes. As a result, improvements in the process parameter feedback, sensing and control, are necessary to successfully develop the automated control technology for the welding processes. Hence, several aspects of the GMAW-P process have been investigated in this study in order to improve its control techniques. Welding was conducted on 6XXX aluminium, using 1.2 mm diameter 4047 aluminum electrode and argon shielding gas. An extensive collection of high speed camera pictures were taken over a wide range of pulse parameters and wire feed rates using a xenon shadowgraph setup to improve understanding of the physics of GMAW-P process. Current and voltage signals were recorded concurrently too. This investigation explores the effects of different process parameters namely pulsing parameters (Peak current (IP), Base Current (IB), Peak time (TP), Base Time (TB)) and wire feed rate on metal transfer phenomena in GMAW-P. Number of drops per pulse, arc length and droplet diameter were measured for aluminium electrodes by high speed videography. The pulsing parameters and wire feed rate were varied to investigate their effect on the metal transfer behaviour. Analysis showed that transition between the different metal transfer modes is strongly influenced by the electrode extension. Lower electrode extension reduced the number of droplets detached per pulse, while at higher electrode extension, spray mode is observed due to increased influence of the resistance heating. Analysis of the current and voltage signals were correlated with the high speed films. A simple derivative filter was used to detect the sudden changes in voltage difference associated with metal transfer during GMAW-P. The chosen feature for detection is the mean value of the weld current and voltage. A new algorithm for the real time monitoring and classification of different metal transfer modes in GMAW-P has been developed using voltage and current signals. The performance of the algorithm is assessed using experimental data. The results obtained from the algorithm show that it is possible to detect changes in metal transfer modes automatically and on-line. Arc stability in the GMAW-P has a close relationship with the regularity of metal transfer, which depends on several physical quantities (like voltage, current, materials, etc.) related to the growth and transfer of the metal droplet. Arc state in GMAW-P can be assessed quantitatively in terms of number of drops per pulse, droplet diameter and arc length. In order to assess the arc state in GMAW-P quantitatively, statistical and neural network models for number of drops/pulse, droplet diameter and arc length were developed using different waveform factors extracted from the current waveform of GMAW-P. To validate the models, estimated results were compared to the actual values of the number of drops per pulse, droplet diameter and arc length, observed during several welding conditions. Determination of stable one drop per pulse (ODPP) parametric zone containing all the combinations of peak current (IP), base current (IB), peak time (TP), and base time (TB) that results in stable operation of GMAW-P, is one of the biggest challenges in GMAW-P. A new parametric model to identify the stable ODPP condition in aluminium which also considers the influence of the background conditions and wire feed has been proposed. Finally, a synergic control algorithm for GMAW-P process has been proposed. Synergic algorithm proposed in this work uses the sensing and prediction techniques to analyse state of the arc and correct the pulsing parameters for achieving the stable ODPP. First arc state is estimated using the signal processing techniques and statistical methods to detect the occurrence of short circuit, unstable ODPP or multiple drops per pulse (MDPP) in GMAW-P system. If the arc state is not stable ODPP, then parametric model and genetic algorithm (GA) is used to assess the deviation of the existing pulsing parameters from the stable operation of GMAW-P process and automatically adjust pulsing parameters to achieve stable ODPP.

A novel decomposition method that is referred to as Contribution-based Decomposition is presented in this thesis. The influence of variables on the values of objective functions and/ or constraints is interpreted as their contributions. Based on contributions of variables, a design problem is decomposed into a number of sub-problems so that variables have similar relative contributions within each sub-problem. The similarity in contributions among variables will lead to an even pressure on the variables when they are driven to better solutions during an optimisation process and, as a result, better solutions can be obtained. Due to nonlinearity of objectives and/ or constrains, variables??? contributions may vary significantly during the solution process. To cope with such variations, a Dynamic Contribution-based Decomposition (DCD) is proposed. By employing DCD, decomposition of system problems is carried out not only at the beginning, but also during the optimisation process, and as a result, the decomposition will always be consistent with the contributions of the current solutions. Further more, a random decomposition is also developed and presented to work in conjunction with the Dynamic Contribution-based Decomposition to introduce re-decompositions when it is required, aiming to increase the global exploring ability. To solve multidisciplinary engineering optimisation problems more efficiently, new solvers are also developed. These include a mixed discrete variable Pattern Search (MDVPS) algorithm and a mixed discrete variable Genetic Algorithm (MDVGA). Inside the MDVGA, new techniques including a flexible floating-point encoding method, a non-dominance ranking strategy and heuristic crossover and mutation operators are also developed to avoid premature convergence and enhance the GA???s search ability. Both MDVPS and MDVGA are able to handle optimisation problems having mixed discrete variables. The former algorithm is more capable of local searching and the latter has better global search ability. A hybrid solver is proposed, which incorporates the MDVPS and the MDVGA and takes advantage of both their strengths. Lastly, a Dynamic Sub-space Optimisation (DSO) method is developed by employing the proposed Dynamic Contribution-based Decomposition methods and the hybrid solver. By employing DSO, decomposed sub-problems can be solved without explicit coordination. To demonstrate the capability of the proposed methods and algorithms, a range of test problems have been exercised and the results are documented. Collectively the results show significant improvements over other published popular approaches.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Mavris, Dimitri; Committee Member: Beeson, Don; Committee Member: Duncan, Scott; Committee Member: German, Brian; Committee Member: Kumar, Viren. Part of the SMARTech Electronic Thesis and Dissertation Collection.