Sharif Digital Repository

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  

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... 

The Vehicle Routing Problem (VRP) is one of the most widely studied topics in the field of Operations Research and has received tremendous attention, especially in recent years. This attention is driven by today’s customer expectations with respect to fast and reliable service. In (classic) static VRPs, all information is known at the time of decision making. However, the world is more dynamic now – and (naturally) so are the optimization problems. the advance of information and communication technologies, Global interconnectedness, urbanization, and increased service orientation raise the need for anticipatory real-time decision making. A striking example is Logistic Service Providers... 

Today, machine learning has many applications in various industries, and healthcare is not an exception. Machine learning algorithms are used for medical diagnosis, make predictions about patients’ future health, newly-discovered treatment effect on patients prediction, drug recommendation system, build risk models and survival estimators and health risk prediction models. One of the topics that has received less attention in the world, especially in Iran, is the prediction of the surgery duration. This is very important because operating rooms in hospitals are the primary source of hospital revenue; We also need to predict the duration of surgery as accurately as possible in order to... 

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.

 

Control charts are the best tools for determining the deviations in the main parameters of a process. Exponentially weighted moving average, EWMA, control charts are the best type for determining small deviations. Determining the control charts parameters by means of minimizing a cost model is economic design. Economic-statistical design is achieved by adding statistical constraints to the economic model. Average run length when the process is in control, ARL0, and average run length when the process is out of control, ARL1, are limited in the economic-statistical model. In this thesis the economic-statistical design of a multivariate EWMA control chart is considered and this model is solved... 

The quality characteristics of both manufacturing and service industries include not only the variables but the attributes as well. While a substantial research have been performed on auto-correlated variables, no attempt has been fulfilled for auto-correlated attributes. Ignoring the imbedded autocorrelation structure in constructing control charts cause not only the in-control run length to decrease, but also the false alarms to increase. To overcome these shortcomings, in this research, an auto-regressive (AR) vector first models the autocorrelation structure of the process data. Then, a modified Elman neural network is developed to generate simulated data using the ARTA algorithm. Next,... 

In this thesis the simulation optimization problems are solved by using Artificial Bee Colony (ABC). The main objective is to improve and adapt the ABC algorithm for solving the optimization problems in deterministic and stochastic environments. For solving deterministic problems, directed search in neighborhood and Nelder-Mead algorithm are combined with ABC algorithm to improve the convergence rate and solutions. Moreover; in stochastic environment, hypothesis test and Kim-Nelson (KN) indifference zone ranking and selection procedure are helping bees to produce solutions with better confidence level on the quality of the solution. Results of optimizing an extensive complex benchmark... 

Hospitals increasingly use control charts to monitor clinical processes and their outcomes. In medical context, control charts should have stable performance when different patients with different levels of risk enter the hospital. In order to monitor multi-attribute medical processes, we propose a new control chart with entities having different levels of risk. First, risk-adjusted multivariate cumulative sum control chart (RA-MCUSUM) is developed. Then, simulation experiences are performed to demonstrate the application and to evaluate RA-MCUSUM performance in terms of in-control average run length (ARL0) stability with the one of a standard MCUSUM chart.The results show that while the... 

Forecasting of energy price and consumption is essential in making managerial decisions and plans effectively and efficiently. It is a valuable technique in economic and/or engineering decisions. While there are many sophisticated mathematical methods developed so far to forecast prices and consumption, nature-based intelligent algorithms have been developed recently. Generally, high accuracy, quick responding, and ability to solve complicated models that are some desired characteristics of artificial intelligence algorithms help decision-makers to come up with good solutions to their problems. The main objective of this research is short term forecasting the energy price and consumption in... 

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... 

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... 

One of the most critical problems in medical supply chain is tracking the pharmaceutical throughout the chain. The challenging solution is to use of barcodes which has numerous limits. Radio frequency identification (RFID) tools are the next generation of barcodes that provide more convenient and faster track of supplies. With automatic workplace, the precision of the system is increased and less personnel is needed. In this study, various medical supply chain management procedures were studied for a hospital and the most appropriate procedure was chosen. According to the hospital conditions, RFID over barcode was the procedure of choice. In order to validate this choice, the ARENA software... 

Nowadays, due to various reasons awareness of environment preservation has increased throughout the supply chain. So supply chain owners revise their carbon footprint. Among different methods of reducing, operational decisions (e.g. ordering quantity or transportation method) has less cost and time for observing outcomes. Our main focus is on inventory models that taking account environment considerations especially carbon emissions. Through these models, vendor-managed inventory model for reducing total cost of chain gets more attention than past. The purpose of this work is to develop a two echelon supply chain with multi-item for replacing inventory. This model includes warehouse capacity... 

In this thesis a model of mixed integer programming to find the optimal solution of bi-objective facility layout problem in flexible bay structure according to uncertainty in flow material among departments and closeness rating is represented. In a facility layout problem based on flexible bay structure, departments with unequal areas are allocated to parallel bays. Also each department can only be allocated in one bay. The Goals are to minimize material handling cost and to maximize closeness rating. Uncertainty in flow material and closeness rating are modeled by fuzzy numbers. Due to the high complexity of the presented model, exact methods are only able to respond to maximize of 9... 

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... 

There are a variety of multivariate industrial processes in real world problems. It seems to be necessary to control them through strong tools such as control charts. One of the state-of-the-art methods to monitor processes is neural network. Neural networks are data processing systems inspired by human brain and they are capable of processing data with a variety of small processors working in parallel forming an integrated network to solve a problem. Chaotic models, one of the states of being out of control, are deterministic non-linear models which have extremely complex behavior under determined assumption. Researches have shown neural networks have excellent performance in such systems.... 

Today, with the development of financial and economic sciences and the increasing volume of financial data, it is necessary to process and analyze this field more accurately with up-to-date tools. On the other hand, by the significant growth of the use of machines and computers for analysis and forecasting purposes, their importance and application have been well defined. Therefore, this research is considered to provide a more efficient method by processing historical data and analyzing them using data mining techniques. The results of this study can be provided to experts in this field as an effective method. Therefore, in this research, a new method based on the selection of required... 

Research in the field of financial market prediction has always been an intriguing subject for academic researchers and stock traders, despite its associated complexities and challenges. Accurately forecasting stock prices and market indices is considered a complex task due to their nonlinear and dynamic nature, requiring analysis of intricate time series data. Over time, various models such as regression models, classification methods, statistical techniques, and artificial intelligence algorithms have been used to predict these variables. With the advancement of technology and the development of AI-based models, particularly machine learning models, along with the availability of vast... 

Emergence of crypto-currency and blockchain technology revolutionize supply chain processes. In addition, customer needs for more information on products or services from origin to destination, highlights the necessity of transparency, originality and traceability in supply chains. This research is aimed to develop a blockchain based system ascertaining supply chain transparency and originality. To this aim, a joint pricing and closed-loop supply chain network design problem is selected as a good platform to implement it. Due to increasing concerns on environmental issues and maximizing job opportunities, sustainability is also considered in the proposed problem. To ascertain transparency...