Endüstri Mühendisliği Bölümü Tez Koleksiyonuhttp://hdl.handle.net/11655/4282020-10-21T20:23:12Z2020-10-21T20:23:12ZDETERMINING THE BEST SETTINGS FOR THE OPERATORS AND PARAMETERS OF GENETIC ALGORITHMS: A METHODOLOGY AND ITS APPLICATION TO TRAVELING SALESPERSON PROBLEMAKDURAN, YAVUZHANhttp://hdl.handle.net/11655/228072020-09-17T12:52:03Z2020-01-01T00:00:00ZDETERMINING THE BEST SETTINGS FOR THE OPERATORS AND PARAMETERS OF GENETIC ALGORITHMS: A METHODOLOGY AND ITS APPLICATION TO TRAVELING SALESPERSON PROBLEM
AKDURAN, YAVUZHAN
Genetic Algorithms (GAs) are heuristic algorithms that are used to approximate the optimal solutions of optimization problems. They are inspired by the theory of natural evolution, where a population of solutions evolves through generations and only the fittest individuals survive at the end. GAs perform very well in many optimization problems in terms of approximation quality and run time. However, a typical GA has several operators such as mutation and crossover, and parameters such as population size and generation number that affect the performance of the GA significantly. In the literature, the operators and parameters of GAs are set based on either the previous experiences of users or trial-error experiments since finding optimum settings of GAs is quite difficult.
In this thesis, a methodology is developed for effectively setting the operators and parameters of GAs. Hence, the best settings that will exploit the potential of the used genetic algorithm can be determined. Typically, performance of a GA is evaluated based on two criteria: (1) approximation quality and (2) run time. Approximation quality of an algorithm is determined based on the closeness of the solution found by the algorithm to the optimal solution. Run time is measured by the computational time the algorithm consumes until termination. In general, there are trade-offs between these two criteria, i.e. higher approximation quality requires more run time, and a GA is expected to find a solution with high approximation quality in a short time. Settings of the operators and parameters affect both criteria, and different settings can be advantageous in terms of different criteria. Therefore, we model the problem of effectively setting the parameters of a GA as a multi-objective optimization problem, using approximation quality and run time as the objectives.
In the thesis, we employ a multi-objective evolutionary algorithm (MOEA) to solve the problem and discover the trade-offs between approximation quality and run time of GAs. MOEAs are population-based heuristics that mimic natural evolution process and find a well-converged and well-diversified set of nondominated solutions. In our approach, each solution of MOEA represents a setting for operators and parameters of the GA considered. To evaluate a solution in the population, the GA is run using the settings defined in the solution. The fittest settings in terms of approximation quality and run time survive through generations. At the end, a set of settings, each of which is better on another criterion, is found.
The developed methodology is demonstrated on travelling salesperson problems (TSP). A GA that is used to solve TSP is selected. Several alternatives for operators and parameters are considered for the GA, and the best settings are investigated by experimenting on 31 TSP instances selected from the literature. The set of best settings is searched using NSGA-II, a well-known MOEA. Then a greedy heuristic is developed to help decision makers to reduce the size of the set of final solutions based on their preferences.
2020-01-01T00:00:00ZA PROBABILISTIC PROJECT CONTROL TOOL FOR PROJECTS WITH HIGH RISKS AND UNCERTAINTYSü, Yaseminhttp://hdl.handle.net/11655/227752020-09-17T11:54:49Z2020-01-01T00:00:00ZA PROBABILISTIC PROJECT CONTROL TOOL FOR PROJECTS WITH HIGH RISKS AND UNCERTAINTY
Sü, Yasemin
Project monitoring and control are essential for project success. One of the most commonly used project control methods is Earned Value Management (EVM). EVM ensures that the projects are controlled in terms of cost, time and scope of the work and can make estimates about the completion time and cost according to the progress of the project. However, the common feature of the projects is that they contain risk and uncertainty and since EVM does not take into account uncertainty and risk factors, it is not effective in projects with high risk and uncertainty.
This study aims to develop a project control tool that is capable of effective project control under uncertainty and risk. The tool can control the project in multiple dimensions in terms of cost, time and scope, and it can calculate the uncertainty and causal risk factors related to these parameters. The tool uses Bayesian Networks (BNs) to model uncertainty and risk factors in the project parameters and to make statistical calculations related to them. BNs offer a powerful modeling technique for modeling and calculating probabilistic relationships, allowing expert knowledge and data to be combined.
In order to examine the applicability of the developed tool to different project areas, case studies will be examined in three different areas. Two of these case studies were based on real project data from different sectors. The positive and negative sides of the developed tool will be evaluated.
2020-01-01T00:00:00ZHeurıstıc Approaches for The Multı-Objectıve Routıng Problem for A Fleet of Unmanned Aerıal VehıclesBişkin, Büşrahttp://hdl.handle.net/11655/227472020-09-25T08:48:30Z2019-12-01T00:00:00ZHeurıstıc Approaches for The Multı-Objectıve Routıng Problem for A Fleet of Unmanned Aerıal Vehıcles
Bişkin, Büşra
Nowadays, Unmanned Aerial Vehicles (UAVs) are extensively employed for various missions with different purposes. In every mission, different goals and problem structures are considered. In this thesis, we study the routing problem of a fleet of identical UAVs under multiple objectives. UA Vs in the fleet, which have limited flight durations, take off from a base, visit a number of targets in a two-dimensional mission area, and return to the base. We assume that the targets have different priorities, and the UAVs try to visit as many targets as possible to collect maximum reward within flight limits. We consider the following three objectives: minimizing the total distance traveled by the fleet, maximizing the total reward collected from the targets, and minimizing the total radar threat. We address two versions of the problem: routing in a radar-free terrain (with distance and reward as objectives) and routing in a radar-monitored terrain (with all three objectives). We aim to find efficient routes for each UAV in the fleet and the trajectory between pairs of targets in each route.
We employ two solution approaches for each version of our problem. First, we model the problem as a Multi-Objective Team Orienteering Problem (MOTOP) and find exact solutions. In our second approach, we utilize an Evolutionary Algorithm, EA-fUAV (Evolutionary Algorithm for routing a fleet of UAVs), to approximate efficient solutions in reasonable time. We test both approaches on three different problem cases. The results show that EA-fUAV approximates the efficient set well in reasonable time.
2019-12-01T00:00:00ZDESIGN OF A FUZZY LOGIC CONTROLLER FOR FIGHTER AIRCRAFT FUEL TANK PRESSURIZATIONTİLKİOĞLU, TUNAhttp://hdl.handle.net/11655/227392020-09-17T13:03:15Z2020-01-01T00:00:00ZDESIGN OF A FUZZY LOGIC CONTROLLER FOR FIGHTER AIRCRAFT FUEL TANK PRESSURIZATION
TİLKİOĞLU, TUNA
Military fighter aircraft are designed to operate in difficult conditions, such as suddenly changing pressure and temperature environments. This brings complex requirements and interfaces between many systems on board. Fuel system is one of the critical systems with the function of pressurizing the fuel tanks it uses. The source of the air that pressurizes the fuel tanks is important for reducing fuel evaporation, especially at high altitudes where ambient air pressure is much lower than the sea level. This air comes from the environmental control system and is converted into nitrogen-enriched air thanks to the modules that separate oxygen and nitrogen molecules in the air in inerting system. The bleed air taken from the engine compressor causes a significant reduction in the power or thrust produced by the engine. Traditional mechanical valves, called climb and dive valves, are generally developed to maintain a certain pressure limit. In aircraft using a closed pressurization system, the climb valve allows air to come out as the atmospheric pressure decreases, while the dive valve allows air flow from the source during descending. On the other hand, while the transfer of fuel from the tank takes place, the empty space (ullage) in the tank increases, resulting in a pressure drop. Mechanical valves operate according to the binary logic. Predefined pressure limits are set and up to this level, the valve is closed and when it reaches the limit, it becomes open. This may be sufficient to maintain fuel tank pressurization but does not take into account engine compressor air consumption. In this thesis, the design and implementation of a fuzzy logic controller that can be applied to the electro-mechanical valve to regulate the flow of air coming into the fuel tanks for pressurization is discussed. Mamdani’s method is selected for the inference system. Fuzzy logic was preferred to design a controller due to the modeling complexity caused by the sudden change of air pressure in the fuel tanks. At the same time, fuzzy logic was found appropriate, as it reflects the experience and perspective of experts with linguistic terms, rather than with a high level of mathematical infrastructure and analytical models for designing a controller. The main purpose in this study is to reduce the waste of supply air while meeting the pressurization requirement. Since modern fighter aircraft often perform different types of missions, two flight profiles have been created to test the controller. Four experimental and four control groups/cases were analyzed by simulation to compare the effectiveness of the new type of electro-mechanical valve with traditional mechanical ones. The designed controller has kept the fuel tank pressure within predetermined limits, while providing significant savings in the amount of supply air. In the case of fuel transfer, it was observed that bleed air consumption was decreased by 90% while in the absence of fuel transfer, bleed air was consumed almost negligibly. The designed controller can be applied for a type of electro-mechanical valve and bleed air consumption can be saved. Therefore, saved bleed air can be distributed to other systems or used to generate more power.
2020-01-01T00:00:00Z