Numerous advantages attained by integrating Distributed Generation (DG) in distribution systems. These advantages include decreasing power losses and improving voltage profiles. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. In this thesis, the optimal DG placement and sizing problem is investigated using two approaches. First, the optimization problem is treated as single-objective optimization problem, where the system’s active power losses are considered as the objective to be minimized. Secondly, the problem is tackled as a multi-objective one, focusing on DG installation costs. These problems are formulated as constrained nonlinear optimization problems using the Sequential Quadratic Programming method. A weighted sum method and a fuzzy decision-making method are presented to generate the Pareto optimal front and also to obtain the best compromise solution. Single and multiple DG installation cases are studied and compared to a case without DG, and a 15-bus radial distribution system and 33-bus meshed distribution system are used to demonstrate the effectiveness of the proposed methods.

Šioje disertacijoje nagrinėjamas sveikatos priežiūros įstaigos darbuotojų darbų grafikų optimizavimo uždavinys, kuris formuluojamas ir sprendžiamas, remiantis vienos didžiausių Lietuvos sveikatos priežiūros įstaigų, realiais duomenimis. Disertacijoje apžvelgiami darbų grafikų optimizavimo uždaviniai bei jų sprendimo metodai, atlikta naujausių šaltinių, tiriančių panašius uždavinius, analizė. Antrame skyriuje nagrinėjamasis darbų grafikų optimizavimo uždavinys suformuluotas matematiškai. Pateikiamos dvi formuluotės: vienakriterio ir daugiakriterio optimizavimo uždavinio. Aprašomos sąlygos, kurias turi tenkinti sudaromasis darbų grafikas. Trečiajame skyriuje nagrinėjami metodai, tiek vienakriteriams, tiek daugiakriteriams uždaviniams spręsti. Pasiūlytas naujas metodas, kuris efektyviau nei kiti nagrinėti metodai sprendžia šioje disertacijoje suformuluotą uždavinį. Ketvirtame skyriuje pateikiami pasiūlyto metodo eksperimentinio tyrimo rezultatai. Pirmoje skyriaus dalyje analizuojami rezultatai gauti, sprendžiant vienakriterį optimizavimo uždavinį, o antroje dalyje – daugiakriterį optimizavimo uždavinį. Disertacijos tyrimų rezultatai buvo pristatyti respublikinėje konferencijoje ir trijose tarptautinėse konferencijose bei publikuoti trijuose mokslo žurnaluose. / In this dissertation nurse rostering problem is investigated. The formulation of the problem is based on real-world data of one of the largest healthcare centers in Lithuania. Most recent publications that tackle the nurse rostering problem and the methods for solving the nurse rostering problem are reviewed in this dissertation. The mathematical formulation of the single objective and the multi-objective nurse rostering problem is presented and the requirements for the roster are described in the second chapter. In the third chapter, the methods for solving the single objective and the multi-objective nurse rostering problem are described. A new method for solving the single objective and the multi-objective nurse rostering problem is proposed in the third chapter. In the fourth chapter, the experimental results of our proposed method are introduced. In the first section of this chapter, the results gathered solving single-objective optimization problem are analyzed, and in the second section of this chapter, the results gathered solving multi-objective optimization problem are analyzed. Dissertation research results were presented at one national conference and three international conferences and published in three scientific journals.

In the dissertation the nurse rostering problem is investigated. The formulation of the problem is based on real-world data of one of the largest healthcare centers in Lithuania. Most recent publications that tackle the nurse rostering problem and the methods for solving the nurse rostering problem are reviewed, the mathematical formulation of the single objective and the multi-objective nurse rostering problem is presented, the requirements for the roster are described and a new method for solving the single objective and the multi-objective nurse rostering problem is proposed in this dissertation. / Disertacijoje nagrinėjamas sveikatos priežiūros įstaigos darbuotojų darbų grafikų optimizavimo uždavinys, kuris formuluojamas ir sprendžiamas, remiantis vienos didžiausių Lietuvos sveikatos priežiūros įstaigų, realiais duomenimis. Disertacijoje apžvelgiami darbų grafikų optimizavimo uždaviniai bei jų sprendimo metodai. Pateikiama nagrinėjamo darbų grafikų vienakriterio ir daugiakriterio optimizavimo uždavinių matematinės formuluotės. Aprašomos sąlygos, kurias turi tenkinti sudaromasis darbų grafikas. Nagrinėjami metodai, tiek vienakriteriams, tiek daugiakriteriams darbų grafikų optimizavimo uždaviniams spręsti. Pasiūlytas naujas metodas, kuris yra efektyvesnis nei kiti nagrinėti metodai sprendžiant disertacijoje suformuluotą uždavinį.

Global optimization problems are relevant in various fields of research and industry, such as chemistry, biology, biomedicine, operational research, etc. Normally it is easier to solve optimization problems having some specific properties of objective function such as linearity, convexity, differentiability, etc. However, there are a lot of practical problems that do not satisfy such properties or even cannot be expressed in an adequate mathematical form. Therefore, it is popular to use random search optimization methods in solving such optimization problems. The dissertation deals with investigation of random search global optimization algorithms, their parallelization and application to solve practical problems. The work is focused on modification and parallelization of particle swarm optimization and genetic algorithms. The modification of particle swarm optimization algorithm, based on reduction of the search area is proposed, and several strategies to parallelize the algorithm are investigated. The algorithm is applied to solve Multiple Gravity Assist problem using parallel computing system. A hybrid global multi-objective optimization algorithm is developed by modifying single agent stochastic search strategy, and incorporating it into multi-objective optimization genetic algorithm. Several strategies to parallelize multi-objective optimization genetic algorithm is proposed. Parallel algorithms are experimentally investigated by solving competitive facility location... [to full text] / Optimizavimo uždaviniai sutinkami įvairiose mokslo ir pramonės srityse, tokiose kaip chemija, biologija, biomedicina, operacijų tyrimai ir pan. Paprastai efektyviausiai sprendžiami uždaviniai, turintys tam tikras savybes, tokias kaip tikslo funkcijų tiesiškumas, iškilumas, diferencijuojamumas ir pan. Tačiau ne visi praktikoje pasitaikantys optimizavimo uždaviniai tenkina šias savybes, o kartais iš vis negali būti išreiškiami adekvačia matematine išraiška. Tokiems uždaviniam spręsti yra populiarūs atsitiktinės paieškos optimizavimo metodai. Disertacijoje yra tiriami atsitiktinės paieškos optimizavimo metodai, jų lygiagretinimo galimybės ir taikymas praktikoje pasitaikantiems uždaviniams spręsti. Pagrindinis dėmesys skiriamas dalelių spiečiaus optimizavimo ir genetinių algoritmų modifikavimui ir lygiagretinimui. Disertacijoje yra siūloma dalelių spiečiaus optimizavimo algoritmo modifikacija, grįsta pieškos srities siaurinimu, ir tiriamos kelios algoritmo lygiagretinimo strategijos. Algoritmas yra taikomas erdvėlaivių skrydžių trajektorijų optimizavimo uždaviniui spręsti lygiagrečiųjų skaičiavimų sistemose. Taip pat yra siūlomas hibridinis globaliojo daugiakriterio optimizavimo algoritmas, gautas modifikuojant vieno agento stochastinės paieškos algoritmą ir įkomponuojant į daugiakriterio optimizavimo genetinį algoritmą. Siūlomos kelios daugiakriterio genetinio algoritmo lygiagretinimo strategijos. Jų pagrindu gauti lygiagretieji algoritmai eksperimentiškai tiriami sprendžiant... [toliau žr. visą tekstą]

Simulation-based optimization methodologies are widely applied in real world optimization problems. In developing these methodologies, beside simulation models, algorithms play a critical role. One example is an evolutionary multi objective optimization algorithm known as Reference point-based Non-dominated Sorting Genetic Algorithm-II (R-NSGA-II), which has shown to have some promising results in this regard. Its successor, R-NSGA-II-adaptive diversity control (hereafter Adaptive Epsilon-R-NSGA-II (AE-R-NSGA-II) algorithm) is one of the latest proposed extensions of the R-NSGA-II algorithm and in the early stages of its development. So far, little research exists on its applicability and usefulness, especially in real world optimization problems. This thesis evaluates behavior and performance of AE-R-NSGA-II, and to the best of our knowledge is one of its kind. To this aim, we have investigated the algorithm in two experiments, using two benchmark functions, 10 performance measures, and a behavioral characteristics analysis method. The experiments are designed to (i) assess behavior and performance of AE-R-NSGA-II, (ii) and facilitate efficient use of the algorithm in real world optimization problems. This is achieved through the algorithm parameter configuration (parametric study) according to the problem characteristics. The behavior and performance of the algorithm in terms of diversity of the solutions obtained, and their convergence to the optimal Pareto front is studied in the first experiment through manipulating a parameter of the algorithm referred to as Adaptive epsilon coefficient value (C), and in the second experiment through manipulating the Reference point (R) according to the distance between the reference point and the global Pareto front. Therefore, as one contribution of this study two new diversity performance measures (called Modified spread, and Population diversity), and the behavioral characteristics analysis method called R-NSGA-II adaptive epsilon value have been introduced and applied. They can be modified and applied for the evaluation of any reference point based algorithm such as the AE-R-NSGA-II. Additionally, this project contributed to improving the Benchmark software, for instance by identifying new features that can facilitate future research in this area. Some of the findings of the study are as follows: (i) systematic changes of C and R parameters influence the diversity and convergence of the obtained solutions (to the optimal Pareto front and to the reference point), (ii) there is a tradeoff between the diversity and convergence speed, according to the systematic changes in the settings, (iii) the proposed diversity measures and the method are applicable and useful in combination with other performance measures. Moreover, we realized that because of the unexpected abnormal behaviors of the algorithm, in some cases the results are conflicting, therefore, impossible to interpret. This shows that still further research is required to verify the applicability and usefulness of AE-R-NSGA-II in practice. The knowledge gained in this study helps improving the algorithm.

Optimizavimo uždaviniai sutinkami įvairiose mokslo ir pramonės srityse, tokiose kaip chemija, biologija, biomedicina, operacijų tyrimai ir pan. Paprastai efektyviausiai sprendžiami uždaviniai, turintys tam tikras savybes, tokias kaip tikslo funkcijų tiesiškumas, iškilumas, diferencijuojamumas ir pan. Tačiau ne visi praktikoje pasitaikantys optimizavimo uždaviniai tenkina šias savybes, o kartais iš vis negali būti išreiškiami adekvačia matematine išraiška. Tokiems uždaviniam spręsti yra populiarūs atsitiktinės paieškos optimizavimo metodai. Disertacijoje yra tiriami atsitiktinės paieškos optimizavimo metodai, jų lygiagretinimo galimybės ir taikymas praktikoje pasitaikantiems uždaviniams spręsti. Pagrindinis dėmesys skiriamas dalelių spiečiaus optimizavimo ir genetinių algoritmų modifikavimui ir lygiagretinimui. Disertacijoje yra siūloma dalelių spiečiaus optimizavimo algoritmo modifikacija, grįsta pieškos srities siaurinimu, ir tiriamos kelios algoritmo lygiagretinimo strategijos. Algoritmas yra taikomas erdvėlaivių skrydžių trajektorijų optimizavimo uždaviniui spręsti lygiagrečiųjų skaičiavimų sistemose. Taip pat yra siūlomas hibridinis globaliojo daugiakriterio optimizavimo algoritmas, gautas modifikuojant vieno agento stochastinės paieškos algoritmą ir įkomponuojant į daugiakriterio optimizavimo genetinį algoritmą. Siūlomos kelios daugiakriterio genetinio algoritmo lygiagretinimo strategijos. Jų pagrindu gauti lygiagretieji algoritmai eksperimentiškai tiriami sprendžiant... [toliau žr. visą tekstą] / Global optimization problems are relevant in various fields of research and industry, such as chemistry, biology, biomedicine, operational research, etc. Normally it is easier to solve optimization problems having some specific properties of objective function such as linearity, convexity, differentiability, etc. However, there are a lot of practical problems that do not satisfy such properties or even cannot be expressed in an adequate mathematical form. Therefore, it is popular to use random search optimization methods in solving such optimization problems. The dissertation deals with investigation of random search global optimization algorithms, their parallelization and application to solve practical problems. The work is focused on modification and parallelization of particle swarm optimization and genetic algorithms. The modification of particle swarm optimization algorithm, based on reduction of the search area is proposed, and several strategies to parallelize the algorithm are investigated. The algorithm is applied to solve Multiple Gravity Assist problem using parallel computing system. A hybrid global multi-objective optimization algorithm is developed by modifying single agent stochastic search strategy, and incorporating it into multi-objective optimization genetic algorithm. Several strategies to parallelize multi-objective optimization genetic algorithm is proposed. Parallel algorithms are experimentally investigated by solving competitive facility location... [to full text]

In this thesis, optimal placement of adhesively bonded piezoelectric patches on laminated plates and the determination of geometry of the bonding area to maximize actuation effect are studied. A new finite element model, in which each layer is considered to be a separate plate, is developed. The adhesive layer is modeled as a distributed spring system. In this way, relative transverse normal and shear motion of the layers are allowed. Effect of delamination on the adhesive layer stresses is also studied and investigated through several case studies. Optimization problems, having single and multiple objectives, are investigated for both actuator placement and selective bonding examples. In these case studies, 2D and 3D Pareto fronts are also obtained. &amp / #8216 / Hide and Seek Simulated Annealing&amp / #8217 / method is adapted for discrete problems and used as the optimization technique for single-objective problems. Finally, Multiple Cooling Multi Objective Simulated Annealing optimization algorithm is adapted and used in multi-objective optimization case studies.

Multiple Cooling Multi Objective Simulated Annealing algorithm has been combined with a conceptual design code written by the author to carry out a multi objective design optimization of an Agricultural Aerial Robot. Both the single and the multi objective optimization problems are solved. The performance figures of merits for different aircraft configurations are compared. In this thesis the potential of optimization as a powerful design tool to the aerospace problems is demonstrated.

Esta tesis explora el problema de la integración de procesos, dentro de la logística y también con otras áreas de la empresa, desde el punto de vista de la planificación estratégica.El primer estudio, explora tres estrategias de distribución: el problema clásico de rutas de vehículos; la estrategia de "master routing", y una estrategia multi-objetivo: minimización de costes y mejoría del servicio al cliente. Una meta-heurística, Búsqueda Local Iterativa, se usa para resolver los modelos y comparar estrategias. El modelo multi-objetivo ofrece varias soluciones no-dominadas que pueden verse como un equilibrio bueno entre perfeccionar el servicio al cliente y reducir costes de transporte. En el segundo estudio, se desarrollan dos modelos para un sistema de inventario en el que el inventario es manejado por el distribuidor. La conclusión principal es que los modelos de decisión integrados producirán un proceso de decisión global bueno. El tercer estudio, explora la coordinación de funciones de la logística: Transporte e Inventario. La conclusión principal es de qué podrían obtenerse reducciones del coste cuando se consideran el inventario y el transporte de una manera integrada. / This thesis explores the key issue integrating logistic processes both inside the logistics field and with other areas of the firm, from a strategic planning point of view.The first study, explores three different distribution strategies: the classical vehicle routing problem; the master route strategy, and the third is a multi-objective strategy: minimizing cost and improving customer service. A meta-heuristic based on Iterated Local Search is used to solve the models and compare the strategies. The main conclusion is that the multi-objective model gives several non-dominated solutions that can be seen as a good balance between optimizing the transportation cost and the customer service.In the second study, two models for an inventory system in which the distributor manages the inventory at the retailers' location, were developed. The main conclusion of this chapter is that developing integrated decision models will result in a better global decision process.In the third study, explores the coordination of two logistics planning functions: Transportation and Inventory. The main conclusion was that cost reductions could be obtained when considering inventory and routing in an integrated manner.

Changes in the electric power industry have brought great challenges and uncertainties in transmission planning area. More effective planning of transmission grids with the appropriate development of advanced planning technologies is badly-needed. The aim of this research is to develop an advanced probabilistic transmission expansion planning (TEP) methodology in a continually changing market environment. The methodology should be able to strengthen and increase the robustness of existing transmission network. By using the proposed probabilistic TEP methodology, it can reduce the risks of major outages and identify weak buses in the system. The significance of this research is shown by its comprehensiveness and powerful practicability. Results from this research are able to improve the planning efficiency and reliability with consideration of financial risks in an electricity market. In order to achieve the target, this research methodologies focused on two main important issues, (1) probability based technical assessment and (2) financial investment evaluation. During the first stage study, probabilistic congestion management, probabilistic reliability evaluation and probabilistic load flow for TEP under uncertainties have been investigated and improved. The developed methodologies and indices, which truly represent the composite impact from both critical state and probability, have linked with financial terms. At financial investment evaluation part, Monte Carlo market simulation is performed to assist economic analysis. The overall planning process has been treated as a constrained multi-objective optimisation task. Comprehensive investigations are conducted on several test systems and testified by real power systems using the available reliability data and economic information from the Australian National Electricity Market (NEM). Overall, this research developed probabilistic transmission planning methodologies that can reflect modern market structures more accurately and it enable a greater utilization of current generation and transmission resources to increase potential operation efficiencies.