Sharif Digital Repository

Road accidents are unfortunate events that cause more thanl16000 deaths each year in Iran. Intercity accidents require a comprehensive plan to reduce casualties because the number of roads users are increasing and the accidents account for nearlyl65% of fatalities. In this study, we first tried to identify the status of Iran through a study of traffic accidents in the world, and then the research and activities carried out in Iran were analyzed to find new and effective solutions. Using the daily fatalities data froml2008 tol2014, and using the new methodology presented in this research based on the Discrete Fourier Transformation (DFT), the Box-Jenkins models and the Secant method, the... 

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... 

Poor decision making in health care has always had irreparable consequences for society. Also, expensive medical tests cause lots of problems for patients. A huge amount of data is produced daily by hospitals, which unfortunately are not used to improve decision making and predicting disease. Data mining can be an appropriate tool for extracting knowledge from a huge amount of data by using a variety of techniques such as prediction. The leading cause of death in the world is heart disease so this study has been designed to predict the incidence of that. Regarding the literature review, the Naïve Bayes method had predicted heart disease accurately. According to the specialist's opinion, some... 

Nowadays, data is the most important asset for health organizations in which the process of collecting, storing and analyzing of data leads to success of health organizations. Many companies have turned to data mining for the beneficial use of these data. The main purpose of data mining is to obtain useful knowledge from existing data. One of the diseases that is very significant for data miners is cardiovascular disease. Cardiovascular disease is the most important cause of death in the world. Therefore, it is necessary to improve the diagnostic and predictive measures of these patients. In this study, a database containing of characteristics of patients with chest pain who referred to... 

We consider finding some efficient control schemes for multivariate variability monitoring with capability of working with individual observations. To do this, the existing efficient control charts for multivariate variability monitoring are studied first and it is determined that the and control statistics, defined by individual observations, estimate the covariance matrix quite well. However, the control method that is based on monitoring the trace of these matrices is not necessarily the best. Thus, by applying the first and the second norm on these two statistics, four new control schemes, namely MEWMSL1, MEWMVL1, MEWMVL1 and MEWMVL2are proposed. Performance comparison results show... 

The endeavors towards achieving a sustainable management of supply chains have brought about new cardinal logistic aims in addition to the common cost minimization objective. Traditional and sometimes disoriented constraints and assumptions such as demand certainty, infinite shelf life of products and so forth have resulted in a noticeable number of improper decisions made by supply chains’ members. Furthermore, intensified environmental concerns, consumers’ awareness of health problems, growth of demand for high quality products, scarcity of natural resources, etc. have brought many challenges and complexities to inventory management and routing problems. Due to supply chain managers’ and... 

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... 

These days, most companies are creating Closed-loop Supply Chains or they are adding Reverse Supply Chain to their existing Forward Supply Chain. Reducing raw material consumption, profit, customer satisfaction and environmental laws are most important reasons of this phenomenon. In the Closed-loop Supply Chains, companies collect the end of life and the end of use products and then if the quality of the returned product is good enough, that product with be refurbished and will be sold again and if the quality of returned products is not good enough to be refurbished, companies will use its good parts in manufacturing new products and the rest of the parts would be sent to disposal centers.... 

Facility location decisions are critical elements in strategic planning processes for a wide range of private and public firms. High costs associated with facility location and construction make facility location or relocation projects long-term investments. One of the most popular models among facility location models is the covering problem. This is due to its application in real life, especially for service and emergency facilities. Set covering problem (SCP) and the maximal covering location problem (MCLP) are two categories of the covering models. The presented model in this study is based on the maximal covering location problemwhile considering some real¬ life constraintssuch as... 

In this study, a brief definition of data mining and its variants were mentioned. Then the methods and algorithms for clustering and their application in the field of healthcare is studied. Concidering the available data for anemia disease, including numeric and categorical attributes, the k-medoids clustering algorithm was selected. This algorithm is one of the simple, powerful and most widely used methods for clustering. The drawbacks of this algorithm are as follow: requires a user input on the number of clusters, depends on the initial data and traps in the local optima. In this thesis, an improved method of clustering-based on Random Forest and k-medoids algorithms has been developed.... 

There is a significant amount of data in the healthcare domain and it is unfeasible to process such volume of data manually in order to diagnose the diseases and develop a treatment method in the short term. Diabetes mellitus has attracted the attention of data miners for a couple of reasons among which significant effects on the health and well-being of the contracted people and the economic burdens on the health care system are of prime importance. Researchers are trying to find a statistical correlation between the causes of this disease and factors like patient's lifestyle, hereditary information, etc. The purpose of data mining is to discover rules that facilitate the early diagnosis... 

Functional data and profiles are characterized by complex relationships between a response and several predictor variables. Fortunately, statistical process control methods provide a solid ground for monitoring the stability of these relationships over time. This study focuses on the monitoring of geometric specifications modeled by roundness profiles. Although the existing approaches deploy regression models with spatial auto-regressive error terms combined with control charts to monitor the parameters, they are designed based on some idealistic assumptions that can be easily violated in practice. In this study, the independent component analysis (ICA) is used in combination with a change... 

Today we live in an era of continuous technology improvement which results in huge changes in different areas of diverse industries. Among the most recent systems for monitoring and quality control which benefits from high speed, are machine vision systems. The output of these systems, are digital images that can be used for monitoring instead of the original products. Unfortunately due to the computational complexity of data extracted from the digital images, traditional methods lose their efficiency. Therefore, in this thesis, a method is proposed to design a model for the monitoring and control of image-based processes, which uses classification methods, that are capable of classifying... 

Today with the development of technology in medical science, there is a need to develop new methods to analyze and process the medical images. Furthermore, increasing use of machines and computers to accomplish prediction goals delineates that these tools had promising results. Because of all the above, this research focuses on processing and analyzing medical images with using data mining tools in order to identify MS and tumor disease which have been ubiquitous in last decades, fast and meticulous. To do so, we introduce a new clustering algorithm based on the modularity measure of graph networks as well as a new machine learning algorithm based on Kalman filter for Tensor-based data.... 

Nowadays due to the advancement in technology, most of the production processes consist of several dependent stages and the quality characteristics of products at each stage depends not only on the operation at the current stage but also to the quality characteristics at the upstream stages. In other words, the disturbance in the quality characteristics of each stage would propagate to the downstream stages and affects the quality of the products at downstream stages. This property is referred to as the cascade property of multistage processes. However, the most of the conventional SPC tools were developed based on the assumption of processes with single stage or processes with multiple... 

This project tries to select the inputs which really affect the change in the direction of S&P500. For this purpose, design of experiments and analysis of variance are used. T tests are carried out to calculate the statistical significance of mean differences. Experiment results indicate that the designed neural networks with the selected inputs significantly outperform the traditional logit model with respect of the number of correct predictions. Moreover, real trades are simulated using the neural network predictions in the test period and the results show that using the designed neural network can significantly increase the income.

 

In a production process, when the quality of a product depends on more than one characteristic, and there is correlation between them, using univariate control charts increases type І and type ΙΙ errors. So for monitoring these processes, multivariate quality control charts are used. Multivariate statistical process control is receiving increased attention in the literature,but little work has been done to deal with multi-attribute processes and just in recent years some techniques are developed in this field. In this thesis, based on the power transformation concept, two new techniques have been developed to monitor multi-attribute processes, in which the defect counts are important. In the... 

In this research, we estimate the change point in multistage processes using maximum likelihood estimation approach. We first model a multistage process with one quality characteristic in each stage, with both AR(1) and ARMA(1,1) time series model and then a maximum likelihood estimator for linear trend change point is developed. Also, a multivariate multistage process is modeled with VAR(1) time series model and the step change point is estimated using maximum likelihood estimator for multivariate multistage process  

Control charts are considered as one of the most important tools of statistical process control in detection of assignable causes of variation in the processes. One of the main criticisms of these charts is their inability in discovering the out-of-control state in real time. To eliminate the main sources of error, indicating the actual time of deviation in processes which is called change point is very important. Diagnosing of real time of changes limits the range of search for the causes of deviations and maximizes the chance of finding the main sources of deviation resulting in time saving and reducing expenses. There are different types of change points. One of change point types which... 

This effort attempts to study the multiple change-point problem in the area of non-linear profiles. Two methods for estimating the times of multiple change-points is proposed. In the first method, a model consisting of two networks, which is based on artificial neural networks, is proposed. These networks are distinctive only in their training data. One network is trained for ascending segment of the profile and the other is trained for descending segments of the profile. In the second method, Bayesian approach is proposed for estimating multiple change-point. While using Bayesian approach the parameters of the Non-linear model must be estimated. However, this issue is complicated or...