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simulation-and-optimization
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Elitist continuous ant colony optimization algorithm for optimal management of coastal aquifers
, Article Water Resources Management ; Volume 25, Issue 1 , 2010 , Pages 165-190 ; 09204741 (ISSN) ; Ketabchi, H ; Sharif University of Technology
2010
Abstract
This paper presents an evolutionary based approach to achieve optimal management of a coastal aquifer to control saltwater intrusion. An improved Elitist Continuous Ant Colony Optimization (ECACO) algorithm is employed for optimal control variables setting of coastal aquifer management problem. The objectives of the optimal management are; maximizing the total water-pumping rate, while controlling the drawdown limits and protecting the wells from saltwater intrusion. Since present work is one of the first efforts towards the application of an ECACO algorithm, sharp interface solution for steady state problem is first exploited. The performance of the developed optimization model is evaluated...
A time warping speech recognition system based on particle swarm optimization
, Article 2nd Asia International Conference on Modelling and Simulation, AMS 2008, Kuala Lumpur, 13 May 2008 through 15 May 2008 ; 2008 , Pages 585-590 ; 9780769531366 (ISBN) ; Razzazi, F ; Rahmani, A. M ; Gharan, S. O ; Sharif University of Technology
2008
Abstract
In this paper, dynamic programming alignment is replaced by a particle swarm optimization (PSO) procedure in time warping problem. The basic PSO is a very slow process to be applied to speech recognition application. In order to achieve a higher performance, by inspiring of PSO optimization methodology, we introduced a PSO Inspired Time warping Algorithm (PTW) that significantly increase the computational performance of time warping in alignments of long length massive data sets. As a main enhancement, a pruning strategy with an add-in controlling threshold is defined in PTW that causes a considerable reduction in recognition time, while maintaining the system accuracy comparing to DTW. ©...
Analysis of axial turbines behavior by means of comparing experimental and theoretical results
, Article 44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Hartford, CT, 21 July 2008 through 23 July 2008 ; June , 2008 ; 9781563479434 (ISBN) ; Hajilouy Benisi, A ; Sharif University of Technology
2008
Abstract
Estimation of efficiency of axial flow gas turbines under variety of conditions i.e. different speed and pressure ratio has been hampered by lack of reliable experimental data and experiments cost. Because the flow in an axial turbine is complex and many mechanisms of the flow losses in turbine have not been known well, loss models are necessary not only in the preliminary process of mean line prediction, but also in the further process of through flow calculation in the simulation and optimization of turbines. Present study has been carried out using 1-D modeling. Simulation computer code is prepared for one-stage axial turbine based on Ainley&Mathieson method with some modifications in the...
Efficiency assessment of job-level dynamic scheduling algorithms on identical multiprocessors
, Article WSEAS Transactions on Computers ; Volume 5, Issue 12 , 2006 , Pages 2948-2955 ; 11092750 (ISSN) ; Naghibzadeh, M ; Taherinia, A. H ; Bahekmat, M ; Khajouie Nejad, S ; Sharif University of Technology
2006
Abstract
This paper presents a comprehensive comparison between job-level dynamic scheduling algorithms on real-time multiprocessor environments using simulation. Earliest Deadline First (EDF) and Least Laxity First (LLF) are two well-known and extensively applied dynamic scheduling algorithms which have been proved to be optimal on uniprocessor systems. However, neither is shown to be optimal on multiprocessors. Many researches have already been done on aforementioned algorithms, but to the best of our knowledge, none of which has compared the efficiency of the two algorithms under similar conditions. Perhaps the main reason is that LLF algorithm is fully dynamic and impractical to implement. In...
A fast flux search controller for DTC based induction motor drives
, Article PESC Record - IEEE Annual Power Electronics Specialists Conference ; Volume 2005 , 2005 , Pages 739-744 ; 02759306 (ISSN); 0780390334 (ISBN); 9780780390331 (ISBN) ; Zolghadri, M. R ; Emadi, A ; Sharif University of Technology
2005
Abstract
In this paper, a flux search controller is proposed to increase the efficiency of a direct torque controlled induction motor with light load. The reference flux value is determined through a two stage minimization algorithm with the amplitude of the stator current as the objective function. In the transient state, a great flux step is used to speed up the convergence. In the steady state, a noise cancellation algorithm is used to let using small flux step and improves the steady state behavior of flux controller. Simulation and experimental confirm the fast dynamics of the proposed method and its superiority compared to the other optimal flux search controllers. © 2005 IEEE
A system dynamics approach to analyze water resources systems
, Article 31st IAHR Congress 2005: Water Engineering for the Future, Choices and Challenges, 11 September 2005 through 16 September 2005 ; 2005 , Pages 4991-5000 ; 8987898245 (ISBN); 9788987898247 (ISBN) ; Baradarannia, M.R ; Sarang, A ; Hjorth, P ; Byong-Ho J ; Sang I.L ; Won S.I ; Gye-Woon C ; Sharif University of Technology
Korea Water Resources Association
2005
Abstract
Several mathematical modeling approaches are used to model water resources systems such as deterministic and non-deterministic, lumped and distributed, steady and dynamic, simulation and optimization approaches. All these modeling paradigms - categorized as open systems - assume that the input conditions to the system will not change during their operation. What is happening in the real world is somewhat different. Due to their dynamic behaviors, real world events exert feedbacks from their outputs to their inputs which may cause the input conditions vary with time. This is the main focus of the system dynamics theory which has been introduced in this paper to be applied in water resources...
Study of association of 2-methoxyethanol in the aqueous phase
, Article Theoretical Chemistry Accounts ; Volume 106, Issue 3 , 2001 , Pages 194-198 ; 1432881X (ISSN) ; Jalili, S ; Sharif University of Technology
Springer New York
2001
Abstract
Monte Carlo simulations have been carried out for 2-methoxyethanol in an isothermal-isobaric ensemble (NPT) at 298.15 K and 1 atm pressure. The optimized potential for liquid simulation force field parameters has been used for modeling 2-methoxyethanol and the TIP4P model for water. Intramolecular rotations are described by an analytical potential function fitted to ab initio energies. It has been shown that the water molecules can form hydrogen bonds between adjacent O atoms of CH3OCH2CH2OH in aqueous media. The self-association of 2-methoxyethanol in aqueous media has been studied by statistical perturbation theory
Using Simulation-Optimization Approach for Fire Station Location and Vehicle Assignment Problem: a Case Study in Tehran, Iran
, M.Sc. Thesis Sharif University of Technology ; Amini, Zahra (Supervisor)
Abstract
In this research, the problem of locating fire stations and allocating equipment has been studied and a simulation-optimization approach has been presented to solve the problem. The mathematical models of this research were developed based on the idea of the randomness of the covered demand and the maximum expected coverage model. In these models, the issue of non-availability of equipment to cover accidents, the random nature of accidents, various fire incidents and the equipment needed to cover them are considered. Two mathematical models with deterministic and non-deterministic approach with different scenarios for demand are proposed. The non-deterministic model is developed with the aim...
Innovative Financing Scheme for Repair and Rehabilitation of Critical Infrastructures Systems
, M.Sc. Thesis Sharif University of Technology ; Haj Kazem Kashani, Hamed (Supervisor)
Abstract
The aim of this research is to examine and propose solutions for financing projects aimed at reducing vulnerability in the oil industry's infrastructure against earthquakes and enhancing their seismic resilience. Design and construction issues, as well as the gradual deterioration of oil infrastructure, increase their vulnerability to earthquakes. Earthquakes can disrupt the proper and continuous functioning of these infrastructures by causing damage. Such disruptions or interruptions in the performance of oil industry infrastructure can have significant economic and social consequences. To mitigate these damages, investment in vulnerability reduction measures is essential. Financing...
Critical-Item Supply-Chain Using Agent-Based Modelling
, M.Sc. Thesis Sharif University of Technology ; Haji, Alireza (Supervisor)
Abstract
One of the crucial matters in the area of Supply Chain Management is the ability of a supply chain to act and react under different circumstances. A helpful tool to understand the supply chain is simulation modeling. With the help of simulation modeling, we can provide the opportunity for the agents in a model to perform based on the defined environment.In the current research, a complete literature review is performed on the topics of supply chain planning and various distribution models and algorithms. With the focus on the vaccine as a critical item, we propose a model to distribute vaccines based on the degree of agents, and we realize that vaccine distribution, while facing huge demand...
Developing Optimization Models for Promotion Planning
, Ph.D. Dissertation Sharif University of Technology ; Modarres Yazdi, Mohammad (Supervisor) ; Aslani, Shirin (Co-Supervisor)
Abstract
Sales promotion plays an important role in increasing the profit, attraction, and retention of consumers. Temporary discounts are a popular promotional tactic that is applied in diverse situations. In this study, we examine some situations to determine optimal decisions. In the first scenario, promotions are implemented during predetermined periods, and the seller determines optimal pricing to achieve two separate objectives: maximizing profit and managing demand (clearance sales). We propose generalized inverse optimization models that determine discounted prices to make the given promotion timing as close to optimal as possible. The efficacy of our approach is demonstrated through...
A Robust Simulation Optimization Algorithm using Bayesian Method
, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
Huge availability of data in last decade has raised the opportunity to use data for decision making. The idea of using existing data to achieve more coherent reality solution has led to a branch of optimization called data-driven optimization. Presence of uncertain variables makes it crucial to design robust optimization methods for this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by this, we are using a Gaussian process in a Bayesian optimization framework to design a method that is consistent with the data in predefined confidence level. The goodness of the...
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...
Simulation-based Service Allocation in Cloud Manufacturing Environments for a Specific Product Type Considering Focusing on Uncertainty in Services' Supply Demand
, M.Sc. Thesis Sharif University of Technology ; Fatahi Valilai, Omid (Supervisor)
Abstract
Nowadays, both academic and industrial environment have come to the conclusion that recent manufacturing paradigms are probably no longer applicable to the ever-changing today’s business environments. That is why manufacturing is moving gradually from production-oriented to service-oriented approaches. Service-oriented manufacturing results in a variety of services through a product life cycle which create an abundance of high value-added markets promoting efficient collaboration and amazing innovation. Cloud Manufacturing as an intelligent newly developed service-oriented manufacturing paradigm provides platforms of shared and interconnected distributed manufacturing resources and...
Multiple Model Predictive Control of Methyl Methacrylate/Vinyl Acetate Synthesis Reactor
, M.Sc. Thesis Sharif University of Technology ; Pishvaie, Mahmoud Reza (Supervisor)
Abstract
The purpose of this study is the implementation of multi-model predictive control (MMPC) approach for the co-polymerization system of methyl methacrylate - vinyl acetate. Simpler development of local models and controllers and also convenience of understanding the model and controller structure are the main reasons for using this approach. In the first step, RGA analysis has been used for pairing input and output variables. Then the performance of PI controller on the system has been investigated. For designing model predictive controller (MPC) the nonlinear model has been linearized at operational point and the controller has been designed in MPC toolbox of MATLAB software R2013a. In the...
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
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...
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...
Multi-Objective Simulation Optimization and its Application in Buffer Allocation Problem
,
M.Sc. Thesis
Sharif University of Technology
;
Mahlooji, Hashem
(Supervisor)
Abstract
This work attempts to address the buffer allocation problem in an unreliable, linear production line. We try to determine the optimal sizes of buffers between adjacent work stations in such a way that a measure of costs is minimized and the production rate is simultaneously maximized. We resort to simulation optimization in order to determine the best combination of input parameters that leads to a near optimal performance for the system. To achieve this purpose, we employ a multi-objective genetic algorithm (NSGAII) in the optimization phase along with simulation as the tool for evaluating the objective function. To determine the merits of the proposed method, we compare the performance of...
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...