This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results. / Board of Trustees, Endowment Fund Project, KPK University of Engineering and Technology (UET), Peshawar and Higher Education Commission (HEC), Pakistan

No / The current global challenge of climate change has made renewable energy usage very important. There is an ongoing drive for the deployment of renewable energy resource at the domestic level through feed-in tariff, etc. However the intermittent nature of renewable energy has made storage a key priority. In this work, a community having a solar farm with energy storage embedded in the house of the energy consumers is considered. Consumers within the community are aggregated in to a local virtual power plant. Genetic algorithm was used to develop an optimized energy transaction for the virtual power plant. The results shows that it is feasible to have a virtual power plant setup in a local community that involve the use of renewable generation and embedded storage. The result also show that when maximization of battery state of charge is considered as part of an optimization problem in a day ahead market, certain trade-off would have to be made on the profit of the virtual power plant, the incentive of the prosumer, as well as the provision of peak service to the grid.

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Yes / Technological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers. / Mr. Oghenovo Okpako is grateful to the Niger Delta Development Commission of Nigeria for funding the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project.

Yes / Recently, significant advances in renewable energy generation have made it possible to consider consumers as prosumers. However, with increase in embedded generation, storage of electrical energy in batteries, flywheels and supercapacitors has become important so as to better utilize the existing grid by helping smooth the peaks and troughs of renewable electricity generation, and also of demand. This has led to the possibility of controlling the times when stored energy from these storage units is fed back to the grid. In this paper we look at how energy resource sharing is achieved if these storage units are part of a virtual power plant. In a virtual power plant, these storage units become energy resources that need to be optimally scheduled over time so as to benefit both prosumer and the grid supplier. In this paper, a smart energy resources allocation algorithm is presented for a virtual power plants using genetic algorithms. It is also proposed that the cause of battery depreciation be accounted for in the allocation of discharge rates. The algorithm was tested under various pricing scenarios, depreciation cost, as well as constraint. The results are presented and discussed. Conclusions were drawn, and suggestion for further work was made. / Mr. Oghenovo Okpako is grateful for the support of the Niger Delta Development Commission of Nigeria for supporting the work. The work has been also supported by the British Council and the UK Department of Business innovations and Skills under the GII funding of the SITARA project.

Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system. / EU Asia-Link project TH/Asia Link/004(91712) East-West and CAMT

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Change from a regulated to deregulated structure means that, the centralized maintenance system is not valid any more. In the surveyed published literature, there is not a single model which incorporates all maintenance cost components to analyze the effect of different maintenance strategies for generator companies (GENCOs). The work enclosed in this thesis demonstrates that there is a considerable requirement for accurately modelling cost components of the maintenance model, to be used in maintenance scheduling for deregulated power system, in order to attain a superior schedule with major financial and operational impact. This research investigates and models most cost factors that affect the maintenance activities of the deregulated GENCOs, and demonstrates the utilization of the developed cost models in maintenance scheduling. It also presents the data gathering process for the developed maintenance cost model. A generator maintenance scheduling model that considers direct and indirect maintenance costs, opportunity costs (i.e. loss of customer goodwill), effective maintenance strategies, failures, and interruptions is developed. A Genetic Algorithm (GA) based approach is employed to achieve maintenance schedules to various generators maintenance scenarios. An Analytical Hierarchy Process (AHP) approach is proposed for modelling customer goodwill. The maintenance model was redeveloped under the Reliability Centred Maintenance (RCM) strategy to analyze the effect of a maintenance strategy on maintenance costs. Case studies are presented to demonstrate the utilisation of the developed models.The investigation shows that the market prices, opportunity costs and maintenance strategy have an effect on the final maintenance schedule. The research demonstrates that the cost components are critical factors to achieve an effective maintenance schedule, and they must be considered and carefully modelled in order to reflect more realistic situation for maintenance scheduling of generator units in deregulation environment.

Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system.

With the advances in wireless communication systems, antennas with different shapes and design have achieved great demand and are desirable for many uses such as personal communication systems, and other applications involving wireless communication. This has resulted in different shapes and types of antenna design in order to achieve different antenna characteristic. One attractive approach to the design of antennas is to suppress or attenuate harmonic contents due to the non-linear operation of the Radio Frequency (RF) front end. The objectives of this work were to investigate, design and implement antennas for harmonic suppression with the aid of a genetic algorithm (GA). Several microstrip patch antennas were designed to operate at frequencies 1.0, 1.8 and 2.4 GHz respectively. The microstrip patch antenna with stub tuned microstrip lines was also employed at 1.0 and 1.8 GHz to meet the design objectives. A new sensing patch technique is introduced and applied in order to find the accepted power at harmonic frequencies. The evaluation of the measured power accepted at the antenna feed port was done using an electromagnetic (EM) simulator, Ansoft Designer, in terms of current distribution. A two sensors method is presented on one antenna prototype to estimate the accepted power at three frequencies. The computational method is based on an integral equation solver using adaptive surface meshing driven by a genetic algorithm. Several examples are demonstrated, including design of coaxially-fed, air-dielectric patch antennas implanted with shorting and folded walls. The characteristics of the antennas in terms of the impedance responses and far field radiation patterns are discussed. The results in terms of the radiation performance are addressed, and compared to measurements. The presented results of these antennas show a good impedance matching at the fundamental frequency with good suppression achieved at the second and third harmonic frequencies.