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Selection of the Optimal Orientation of Parts in Rapid Prototyping Processes
, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Additive manufacturing (AM), also known as Rapid prototyping or D printing, is a new technology for the manufacturing of the physical parts through an additive manner. In the AM process, the orientation pattern of the part is one of the most important factors that significantly affects the product properties such as the build time, the surface roughness, the mechanical strength, the wrinkling, and the amount of support material. The build time and the surface roughness are the more imperative criteria than others that can be considered to find the optimum orientation of parts. In this research, Two method is used to optimize part build orientation (PBO). In the first method a new combined...
Monitoring of Precipitation By Merging Surface Gauge Measurements and PERSIANN Satellite Precipitation Product (Case Study: Lake Urmia Basin)
, M.Sc. Thesis Sharif University of Technology ; Abrishamchi, Ahmad (Supervisor)
Abstract
Worldwide coverage, online accessibility and unique accuracy of spatial and temporal satellite data such as rainfall, has encouraged researchers to apply this information in studies such as water resources engineering, hydrologic modeling, drought studies and flood forecast. This research aims to introduce a method that reduces the estimation error of rainfall data derived from a satellite product. “Satellites rainfall products” such as PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), which has been used in this research, has significant error in comparison with geo-prediction precipitation, due to stochastic and systematic errors. In...
Development of Non-deterministic Methods in Metamodel-based Simulation Optimization
, Ph.D. Dissertation Sharif University of Technology ; Mahlooji, Hashem (Supervisor) ; Eshghi, Kourosh (Co-Advisor)
Abstract
In recent years, simulation optimization methods have been developed to solve complicated problems that cannot be solved by mathematical programming methods. In simulation optimization methods, first the problem is modeled by simulation tools and then by applying optimization tools the optimal combination of input variables that optimizes the simulation output is determined. Although simulation optimization has attracted researchers’ attention in recent years, most of the works presented do not consider uncertainty in simulation models. This becomes our motivation in this study to develop uncertain methods in metamodel-based simulation optimization based on minimax methods that are...
Developin A New Metamodel-Based Simulation Optimisaztion Algorithm
, M.Sc. Thesis Sharif University of Technology ; Ghasemi Tari, Farhad (Supervisor)
Abstract
Digital computer simulation and employing the concepts of the experimental design, and other analytical tools for evaluating its output have attracted many of the scientists and researchers interests in the recent decades. The importance of this topic has been increasing and more related analytical tools have been introduced to the scientific literature. One of the powerful tools for simplifying accelerating the optimization process of simulation results, are the use of the metamodels. Use of these powerful tools becomes more eminent when the simulation runs are expensive. By the use of the metamodels the needs of conducting sampling for obtaining some more new points from direct simulating...
Robust Optimization for Simulated Systems Using Risk Management and Kriging
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
Many simulation optimization problems are defined in random settings and their inputs have uncertainty. Therefore, in defining an optimal solution for these problems, uncertainties should be taken into account. The primary way of dealing with this , is Robust Optimization which finds solution immune to these changing settings. Aiming at finding a new approach for simulation optimization problems, this study investigates these uncertainties and robust methods. In the optimization problem, the goal and constraints are considered with separate risk measures and a related problem is defined as follows: Minimizing the weighted sum of all risks subject to the problem constraints. To solve the...
Development of an Algorithm for Optimizing the Digital Computer Simulation Experiments
, M.Sc. Thesis Sharif University of Technology ; Ghasemi Tari, Farhad (Supervisor)
Abstract
simulation models are free of any restricting assumptions which may normally be considered in a stochastic system, so simulation is considered as one of the most popular tools that can be applied toward analysis of behavior of stochastic systems which are complex.
In order to analyze such problems and determine the best combination of input variables to optimize the system performance criterion, simulation optimization methods were introduced. The most important issue in these problems is that simulation models are usually considered as the black-box models in which, the output function is not usually expressed explicitly.
This work reviews different methods which developed...
In order to analyze such problems and determine the best combination of input variables to optimize the system performance criterion, simulation optimization methods were introduced. The most important issue in these problems is that simulation models are usually considered as the black-box models in which, the output function is not usually expressed explicitly.
This work reviews different methods which developed...
A Stochastic Kriging Metamodel for Constrained Simulation Optimization Based on a k-Optimal Design
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
In recent years, optimization via simulation for the systemswhose objective function has stochastic characteristic and doesn’t explicitly exist in closed form, has attracted considerable interest.Simulation of this kind of systems at times may be veryexpensive. In this research, the constraint simulation optimization problem is considered for solving problems with stochastic features based on metamodels. For this purpose, stochastic Kriging is used as a metamodel. In this method, first, a few feasible points in the solution space are identified by thek-optimal design of experiment and then the simulation runs are performed. In the next step, a metamodel is fitted to all the stochastic...
Detection of Change in Nonlinear Profiles using Kriging and Comparison with Self Organizing Clustering Method
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
Control Charts are the most popular monitoring tools for profiles. The time that a control chart gives an out-of-control signal is not the real time of change. The actual time of change is called the change point. This study suggests two new algorithms to find the change (shift) in parameters of nonlinear profiles. First, using ordinary Kriging method, new points are estimated. Then, with the help of Bernoulli hypothesis test, the probability of detecting the change for new points is tested. Nonlinear profiles in this study follow the exponential family of distributions; in particular, Exponential, Poison and Gaussian distribution structures are used as nonlinear profiles. The proposed...
A Robust Metamodel-based Simulation Optimization Approach for a Multi-Product Supply Chain Problem
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
One of the popular problems in the area of supply chain management is how to determine the critical decision variables in supply chain systems. This problem has been investigated by means of various methods one of which is simulation optimization. Due to the uncertain nature of real world systems, robustness of the resulting solutions is a worthy issue to be considered. In this effort, the problem of determining the safety stock levels in a multi-product supply chain system is addressed, a proper framework to define the decision and environmental variables is proposed, and their effects on the performance measures is investigated. A robust metamodel based simulation optimization approach...
Selection of Simulation-Optimization Meta-Modeling Approach in Manufacturing Supply Chains
, M.Sc. Thesis Sharif University of Technology ; Ghasemi Tari, Farhad (Supervisor)
Abstract
This research presents a modified algorithm for constrained optimization of random simulation models. One output is selected as objective to be minimized, while other must satisfy the given threshold value. Moreover, the simulation inputs must be integer and satisfy linear or nonlinear constraints. The research applies a sequentialized experimental design to specify the simulation input combinations, Kriging (or spatial correlation modeling) to analyze the global simulation input/output data resulting from these designs, and nonlinear programming to estimate the optimal solution from the Kriging metamodels. In addition, a simulation model is developed for different inventory planning...
Developing a Non-Arrhenius Model for Simulating Wellbore-Scale Carbonate Matrix Acidizing
, M.Sc. Thesis Sharif University of Technology ; Jamshidi, Saeed (Supervisor) ; Bazargan, Mohammad (Supervisor)
Abstract
In this work, a basic model for numerically simulating matrix acidizing in carbonate formations is developed. This model employs laboratory linear acid core-flooding data and avoids any fitting parameters. Wellbore-scale numerical simulation is based on finite-volume method with Cartesian coordination and cubic meshing. Mass transfer equations are based on fluid front tracking and the momentum transfer equation is the Navier-Stokes equation for flow in porous media. The effect of fine-scale dissolution phenomena of carbonate rocks are translated into the coarse-scale by homogenization. In this work, it is tried to achieve a simple but logical approach towards a correct simulation, avoiding...
Designing a Model-Based Process and Architecture for Partial Automation of Software Development
, M.Sc. Thesis Sharif University of Technology ; Habibi, Jafar (Supervisor)
Abstract
Automation of the software development process is the software engineers' final goal, but with the current facilities and knowledge in software engineering, it is not possible to automatically generate the whole software. Usually all the software in a specific domain contain common behaviors, which by careful exploration of these common behaviors and automation of code generation in these sections, the cost and time of projects' execution can be reduced. According to Model Driven Development (MDD), the first step in software development is creating appropriate models. For creating models, metamodel is required; therefore, we need to create a specific motamodel for the chosen domain or use...
An Artificial Neural Network Meta-Model for Solving Semi Expensive Simulation Optimization Problems
, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
Although a considerable number of problems whose analysis depends on a set of complex mathematical relations exist in the literature due to recent developments in the field of decision making, still very simplified and unrealistic assumptions are involved in many. Simulation is one of the most powerful tools to deal with this kind of problems and enjoys being free of any restricting assumptions which may generally be considered in a stochastic system. In addition, simulation optimization techniques are categorized into two broad classes of model-based and metamodel-based methods. In the first class, simulation and optimization component interact with each other causing an increase in...
A Novel Metamodel-based Simulation Optimization Algorithm using a Hybrid Sequential Experimental Design
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
In this work, we propose a metamodel-based simulation optimization algorithm using a novel hybrid sequential experimental design. The algorithm starts with a metamodel construction phase in which at each stage, a sequential experimental design is used to select a new sample point from the search space using a hybrid exploration-exploitation search strategy. Based on the available design points at each stage, a metamodel is constructed using Artificial Neural Network (ANN) and Kriging interpolation techniques. The resulting metamodel is then used in the optimization process to evaluate new solutions. We use Imperialist Competitive Algorithm (ICA) which is a powerful population-based...
Meta-model Based Simulation Optimization under Uncertainty
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
In this research we will develop an algorithm to find the optimal robust solution via simulation optimization by using an artificial neural network metamodel. Following Taghuchi, in design phase of the algorithm, we will discriminate between decision or control variables and environmental or noise variables. To arrive of the best new solution in every iteration, the algorithm will use a symmetrical probabilistic distribution about the optimum point of the previous iteration. In comparison with the existing methods, our algorithm displays an improvement in results when applied to such problems as single channel queueing system problem and economic order quantity problem
Robust Design Optimization for Fatigue Life with Geometric and Material Uncertainties of Mechanical Parts Under Random Loading Based on Maximizing Fatigue Life and Minimizing Uncertainty in Fatigue Llife Prediction
, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Fatigue life prediction of a mechanical part is one of issues which a group of engineers are engaged with it and always they try to design the parts with the maximum of lifetime. Although many researches have been done in this field but yet we can see that predicted life are different from that happens in the reality because there are some uncertainties in the phenomena. Our effort in this project is creating an algorithm design so that the parts are designed by it, have the maximum fatigue life and the minimum uncertainty in prediction. In this project we have considered geometrical, material and random loading uncertainties as error resources. Older methods those are presented in this...
Simulation Optimization Using Hybrid and Adaptive Metamodels
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
In this thesis we propose a new metamodel based simulation optimization algorithm using sequential design of experiments. The main objective is to have a new method which can be used without deep knowledge of different kinds of metamodels, optimization techniques and design of experiments. The method uses three metamodels simulataneously and gradually adapts to the best metamodel. In each iteration, some points are chosen as candidates for future simulation. These points are ranked based on the quality of metamodel prediction and their placement among simulated points, the best point will be chosen for simulation. Comparing the proposed algorithm with some of the popular simulation...
Efficient back analysis of multiphysics processes of gas hydrate production through artificial intelligence
, Article Fuel ; Volume 323 , 2022 ; 00162361 (ISSN) ; Shadabfar, M ; Huang, H ; Leung, Y. F ; Uchida, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Natural gas hydrate, a crystalline solid existing under high-pressure and low-temperature conditions, has been regarded as a potential alternative energy resource. It is globally widespread and occurs mainly inside the pores of deepwater sediments and sediments under permafrost area. Hydrate production via well depressurization is deemed well-suited to existing technology, in which the pore pressure is lowered, the natural gas hydrate is dissociated into water and gas, and the water and gas are produced from well. This method triggers multiphysics processes such as fluid flow, heat transfer, energy adsorption, chemical reaction and sediment deformation, all of which are dependent on the...
Towards tool support for situational engineering of agile methodologies
, Article Proceedings - Asia-Pacific Software Engineering Conference, APSEC, 30 November 2010 through 3 December 2010, Sydney, NSW ; 2010 , Pages 326-335 ; 15301362 (ISSN) ; 9780769542669 (ISBN) ; Hasani Sadi, M ; Ramsin, R ; Sharif University of Technology
2010
Abstract
Various agile software development methodologies, practices, and techniques have been proposed in the last decade; some present novel ideas, while many are simply made up of tasks and techniques borrowed from prominent agile methodologies. Each of these methodologies prescribes a set of practices and techniques which are deemed appropriate for application in a specific context. However, there exists no single method which fits all project situations. This has resulted in the advent of Situational Method Engineering (SME) approaches, which are used for developing software methodologies that are tailored to fit the specific circumstances of the project situation at hand. Since tool support has...
Robust simulation optimization using φ-divergence
, Article International Journal of Industrial Engineering Computations ; Volume 7, Issue 4 , 2016 , Pages 517-534 ; 19232926 (ISSN) ; Mahlooji, H ; Sharif University of Technology
Growing Science
Abstract
We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains...