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simulation-and-optimization
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Two New Meta-Model Based Artificial Neural Network Algorithms for Constrained Simulation Optimization Problems with Stochastic Constraints
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
Following the recent developments in the field of decision making, a considerable number of problems involved with stochastic systems can be thought of whose analysis depends on a set of intricate mathematical relations. In such cases, simulation is one of the most popular tools that can be applied toward analysis of behavior of such stochastic systems. Not only does not the simulation model rely on such intricate mathematical relations, it also enjoys the added advantage of being free of any restricting assumptions which may normally be considered in a stochastic system.To analyze such problem, one may aim at determining the best combination of input variables to optimize the system...
Application of Simulation Optimization in Buffer Allocation Problem Using Genetic Algorithm and Simulated Annealing
, M.Sc. Thesis Sharif University of Technology ; 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 ; 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...
Multi-Objective Simulation Optimization Within MCDM Framework: A Bi-Objective Inventory System
, M.Sc. Thesis Sharif University of Technology ; 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 ; 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...
Modeling and Simulation Optimization of Hotel Revenue Management with Customer Choice Behavior
, M.Sc. Thesis Sharif University of Technology ; Shavandi, Hassan (Supervisor)
Abstract
In this thesis, the problem of the hotel revenue management with the customer choice behaiver has been studied . In this model , each customer has a preference order among the set of products. He purchased or booked his products according to his ordered list of preferences. Because the high number of hotel products are assumed, each customer can only choices different fare classes and the list of the preference customers is made up of different fare classes. Two different approaches have been used to solve this problem . The first approach is The linear programming model in which α percent of the capacity is allocated to business customers and the remaining capacity to leisuer customers...
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...
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...
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...
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 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...
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...
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
Solve the Project Scheduling Problem Considering the Assumed Discount in Buying Cost Function for Nonrenewable and Perishable Resources
, M.Sc. Thesis Sharif University of Technology ; Shadrokh, Shahram (Supervisor)
Abstract
Project Scheduling is one of the most important branches of operations research and management science. In this paper, project scheduling problem with a deterministic duration of activities is studied. Resources are considered nonrenewable ones which are perishable. First of all, the project scheduling problem has been studied with a rate of corruption of inventory. Then, a rate of corruption with three parameters for Weibull is considered. Cost function for purchasing a renewable Perishable resource, has been assumed that has growing discounts for the purchase of certain health differently. Project costs are included purchase costs and maintenance costs. The objective function is the...
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...
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...
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...
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...
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...
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...