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    Robust simulation optimization using φ-divergence

    , Article International Journal of Industrial Engineering Computations ; Volume 7, Issue 4 , 2016 , Pages 517-534 ; 19232926 (ISSN) Moghaddam, S ; 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... 

    Simulation, optimization & control of styrene bulk polymerization in a tubular reactor

    , Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 32, Issue 4 , 2013 , Pages 69-79 ; 10219986 (ISSN) Padideh, G. M ; Mohammad, S ; Hossein, A ; Sharif University of Technology
    Jihad Danishgahi  2013
    Abstract
    In this paper, optimization and control of a tubular reactor for thermal bulk post-polymerization of styrene have been investigated. By using the reactor mathematical model, static and dynamic simulations are carried out. Based on an objective function including polymer conversion and polydispersity, reactor optimal temperature profile has been obtained. In the absence of model mismatch, desired product characteristic can also be obtained by applying the corresponding reactor wall or jacket temperature profile. To achieve this temperature trajectory, reactor jacket is divided into three zones and jacket inlet temperatures are used as manipulated variables. Effectiveness of the proposed... 

    An artificial neural network meta-model for constrained simulation optimization

    , Article Journal of the Operational Research Society ; Vol. 65, issue. 8 , August , 2014 , pp. 1232-1244 ; ISSN: 01605682 Mohammad Nezhad, A ; Mahlooji, H ; Sharif University of Technology
    Abstract
    This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition... 

    A robust simulation optimization algorithm using kriging and particle swarm optimization: application to surgery room optimization

    , Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) Azizi, M. J ; Seifi, F ; Moghadam, S ; Sharif University of Technology
    Taylor and Francis Inc  2019
    Abstract
    Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

    A robust simulation optimization algorithm using kriging and particle swarm optimization: Application to surgery room optimization

    , Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) Azizi, M. J ; Seifi, F ; Moghadam, S ; Sharif University of Technology
    Taylor and Francis Inc  2019
    Abstract
    Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

    Multi-Objective Simulation Optimization Within MCDM Framework: A Bi-Objective Inventory System

    , M.Sc. Thesis Sharif University of Technology Ramezani, Iman (Author) ; Akhavan Niaki, Mohammad Taghi (Supervisor)
    Abstract
    System design, regardless of the type of system being considered, needs to determine parametersto maximize the system performance criteria.One of solutions of finding best system performance is using simulation optimization. In real world, end users have models with more than one objective and these objectives are conflicting objectives. There are a lot ofmeta-heuristic algorithms to solve multi-objective optimization problems. NSGA-II is one of the most popular proposed meta-heuristic algorithms to solve multi-objective problems. Because of using average to evaluate solutions, process of selecting new generations in this algorithm is such that in every generation some of suitable solutions... 

    Selection of Simulation-Optimization Meta-Modeling Approach in Manufacturing Supply Chains

    , M.Sc. Thesis Sharif University of Technology Khoddam, Mona (Author) ; 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... 

    A Robust Metamodel-based Simulation Optimization Approach for a Multi-Product Supply Chain Problem

    , M.Sc. Thesis Sharif University of Technology Sharifnia, Mohamad Ebrahim (Author) ; 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... 

    Multi-Objective Simulation Optimization and its Application in Buffer Allocation Problem

    , M.Sc. Thesis Sharif University of Technology Marani, Mohammad Reza (Author) ; 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... 

    Multiple Model Predictive Control of Methyl Methacrylate/Vinyl Acetate Synthesis Reactor

    , M.Sc. Thesis Sharif University of Technology Naderi Boldaji, Sara (Author) ; 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... 

    An Artificial Neural Network Meta-Model for Solving Semi Expensive Simulation Optimization Problems

    , M.Sc. Thesis Sharif University of Technology Behbahani, Mohammad (Author) ; 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 Stochastic Kriging Metamodel for Constrained Simulation Optimization Based on a k-Optimal Design

    , M.Sc. Thesis Sharif University of Technology Abbaszadeh Peivasti, Hadi (Author) ; 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... 

    Robust Optimization for Simulated Systems Using Risk Management and Kriging

    , M.Sc. Thesis Sharif University of Technology Mohseni, Ali (Author) ; 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... 

    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 Rezghi, Atieh (Author) ; 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... 

    Design, modeling and optimization of a novel two DOF polymeric electro-thermal micro-actuator

    , Article Applied Mechanics and Materials ; Vol. 307 , 2013 , pp. 112-116 ; ISSN: 16609336 ; ISBN: 9783037856598 Sheikhbahaie, R ; Alasty, A ; Salarieh, H ; Sharif University of Technology
    Abstract
    In this paper, design, simulation and optimization of a novel electrothermally-activated polymeric microactuator capable of generating combination of bidirectional lateral and rotational motions are presented. The composite structure of this actuator is consisted of a symmetric meandered shape silicon skeleton, a SU8 thermal expandable polymer and a thin film chrome layer heater. This actuator is controlled by applying appropriate voltages on its four terminals. With the purpose of dimension optimization, a numerical parametric study is executed. The modeled actuator which is 1560 ?m long, 156 ?m wide and 30 ?m thick, demonstrates a remarkable lateral displacement of 23 ?m at power... 

    Simulation and optimization of a semi spherical air bearing

    , Article ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) ; Volume 7, Issue PARTS A, B, C, D , 2012 , Pages 153-159 ; 9780791845233 (ISBN) Saidimanesh, M ; Shahiri, A ; Nikparto, A ; Sharif University of Technology
    2012
    Abstract
    It is important to test the attitude control systems on satellites before they are launched in space. Traditionally this has been done by dropping the satellite, and firing the thrusters before the satellite makes a soft landing in a net. This method only allows a few seconds of testing and does not lend itself to the measurement of pointing accuracy. A better method is to mount the satellite on a spherical air bearing. In this paper behavior of a semi spherical air bearing is studied and analyzed in various conditions. These bearings are used in different applications such as simulation of approximately frictionless condition which is the satellite's situation in space. In this analysis... 

    Joint optimization of power allocation and relay selection in cooperative wireless sensor networks

    , Article 2009 IEEE 3rd International Symposium on Advanced Networks and Telecommunication Systems, ANTS 2009, 14 December 2009 through 16 December 2009 ; 2009 ; 9781424459896 (ISBN) Jamali Rad, H ; Abdizadeh, M ; Meshgi, H ; Abolhassani, B ; Sharif University of Technology
    Abstract
    We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). We propose that to achieve the minimum SEP at the destination, a joint optimization of power allocation and relay selection should be accomplished. To this aim, we reformulate the SEP performance of a simple cluster-based cooperative WSN in the general form and solve the joint optimization problem efficiently. Simulation results demonstrate that the proposed joint optimization can efficiently improve the SEP performance of the network in comparison with the previous disjoint optimal relay selection schemes  

    A robust simulation optimization algorithm using kriging and particle swarm optimization: application to surgery room optimization

    , Article Communications in Statistics: Simulation and Computation ; Volume 50, Issue 7 , 2021 , Pages 2025-2041 ; 03610918 (ISSN) Azizi, M. J ; Seifi, F ; Moghadam, S ; Sharif University of Technology
    Taylor and Francis Ltd  2021
    Abstract
    Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

    Application of Simulation Optimization in Buffer Allocation Problem Using Genetic Algorithm and Simulated Annealing

    , M.Sc. Thesis Sharif University of Technology Ramezani Ali Abadi, Majid (Author) ; Kianfar, Farhad (Supervisor)
    Abstract
    Simulation optimization has been one of the most important topics in recent years. It is especially useful when the objective function cannot be computed directly or some of the parameters are stochastic. Buffer allocation problem is one of the important problems related to production lines. In this problem the objective is to determine the buffers between workstations so that the throughput is maximized. Non-linear and combinatorial nature of the problem, stochastic factors like machine breakdown or processing times and most importantly lack of a distinct relation for the objective function are the reasons of using simulation ... 

    Developin A New Metamodel-Based Simulation Optimisaztion Algorithm

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Maryam (Author) ; 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...