Sharif Digital Repository

One technique to reduce variance of output in comparing two or more systems via simulation is random number synchronization. the primary technique for achieving synchronization is to assign an independent seed to each random process and then use the same collection of seeds across all systems . Another way of synchronization in systems with limited customer population is to assign different seed to each customer (transaction) and then use the same collection of seeds across all systems. The main Question to be considered here is which of these methods of synchronization can better reduce variance of output in a queue with finite customer population  

Nowadays Human Resource Management (HRM) has a critical and fundamental importance in organization management. In the beginning and middle of 20th century and even in the last decades of that century financial investments were important in different economic sectors and industries, but in the beginning of 21th century human resource and training is one of the most fundamental issues in all of economic sectors and industries, especially in power industry. Following this point of view, people with appropriate education and qualifications should be employed in power industry. These employees should be skillfully able of working with complicated technology of power industry as well as being... 

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... 

This thesis presents a method to solve a redundancy allocation problem for the series-parallel system. The modeling formulation deals with the choice of the best redundancy strategy, component and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. Majority of the solution methods for the general redundancy allocation problems assume that the failure rate of components is determined. However, in practice variable failure rate may be used. The complexity of this problem is known as NP-hard and therefore the optimal solution is not accessible in the normal course of events. It is demonstrated in this thesis that genetic algorithm is... 

Sampling is the process of selecting and analyzing part of population and generalizing the results to estimate characteristics of the population. Time and cost considerations or the destructive nature of experiments are the main reasons why we study a sample instead of the whole population. In typical applications, sampling units are well-defined which means these units can be defined, selected and analyzed separately. This is the case in taking samples from a human population or products that consist of distinct units. If sampling units are not well-defined, as in the case of a bulk material (e.g., a pile of dust or other particulate materials, a tankful of liquid, the air or other gases)... 

In this thesis we will provide a comprehensive study of process capability indices, and we use multivariate g-and-h Tukey distribution for calculating process capability indices of multivariate non-normal processes. Univariate process capability indices have been used commonly in literature for measuring the capability of a process. However, there are several processes with more than one quality characteristic. When these characteristics are independent, we can use univariate process capability indices, but when the characteristics are dependent we should use multivariate methods for measuring process capability. There have been several studies about multivariate process capability indices,... 

This effort attempts to study the change point problem in the area of non-linear profiles. Two methods for estimating the time of a single step change is proposed. In the first method a model consisting of two networks which is based on artificial neural networks is proposed. These networks are different only in their training data. One network is trained for ascending segments of the profile and the other is trained for descending segments of the profile. In the second method the maximum likelihood estimator (MLE) of the single step change is analyzed. Due to the complexity of estimating the parameters of the non-linear model by MLE, this estimator is based on the difference between the... 

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... 

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... 

This work attempt to analyze the impact of changes in nurse scheduling on quality of services at emergency department of Emam Khomeini Hospital Complex. In nurse scheduling some constraints such as maximum working hours per week and unacceptable working sequences must be considered. A simulation model is developed to cover the complete flow for the patient through the emergency department. Emergency department of Emam Khomeini is simulated in order to obtain the patient average waiting time for each nurse roster. Then a genetic algorithm is integrated with the simulation model. In each iteration genetic algorithm produces many nurse rosters . For each roster the patient average waiting time... 

Statistical process control charts have been applied to the health care practices for quite same time. This work aims at investigating the merits to of applying process control chart to the survival time of the patients suffering from gastric cancer-who go though the surgical treatment. Based on accelerated failure time regression models we adopt risk adjusted control chart to monitor the surgical outcome. The monitoring process will we performed continuously based on a likelihood ratio test. The result indicate that this risk adjusted model can provide better estimation for the risk adjustment model parameters. As expected, the proposed risk-adjusted control charts can achieve a better... 

In many cases even high-quality products cannot satisfy the customer needs and this leads to having a lower share of the market. To achieve this purpose, customer satisfaction and robust design are regarded simultaneously to achieve a better quality. Balance Score Card (BSC) is proposed as a technique that could potentially has a great improvement effect on customer satisfaction. Moreover, with a non-linear programming (NLP), a novel method for integrating RSM and BSC have been proposed to accede robustness in design. The opinions of the customers are regarded in every system design, parameter design and tolerance design. To validate the applicability of the proposed approach, the approach... 

Before a construction project gets under way an attempt is made to examine different ways and scenarios for its implementation. Through such a process the more appropriate scenarios should be selected based on several criteria that are not necessarily at the same level in terms of importance. The purpose of this study is to examine different scenarios for implementing operations in the pre-construction phase of a project, based on several competing criteria with different importance levels in order to achieve a more efficient execution plan. This paper presents a new framework that integrates discrete event simulation (DES) and data envelopment analysis (DEA) to rank different scenarios for... 

This is the first attempt at process modeling in terms of predictive control using a hierachical method based on regression analysis and artificial neural networks(ANNs).This hierachical use leads to the reliability improvement of neural model of process in prediction (extrapolation and interpolation) of process output. such an outlook makes it possible to predict the proper input settings which achieved a desired process output by designing various senarios for process set up. This approach was applied in Tile industry for spray dring process and in order to indicate the achieved improvement,three models:(i) regression model of process using multiple linear regression,(ii)Neural model of... 

In some statistical process control applications, quality of a process or product 1s characterized by a relationship between a response variable and one or more explanatory variables which is referred to as profile by researchers. In some applications such as calibration, this relationship is characterized by a simple linear regression. However, in some situations, more complicated models are needed. It seems that there is a little attention to monitoring of profiles with binary response variables. Furthermore, the extensive applications of binary response variables in real industrial worlds make it necessary to concentrate on this kind of profiles. In this ... 

Control charts are one of the strongest optimization tools. Control charts issue warning due to out of control processby recorded data. As soon as charts warn, attempt start to find the changes reason.Finding out the issue on time save a lot of time and cost. By determining the time of this change, finding out the problem reason get faster. The real time of process change called change point. There exist many papers in change point field finding real time of change in literature. Each one considers the problem with specific assumptions. These assumptions consist of distribution function, change type, parameters and solution procedure. One kind of existing papers are about to determine normal... 

Nowadays, credit risk is recognized as the most significant factor of bankruptcy in banking industry. Incorrect selection and unsuitable appropriation of financial resources to customers cause this risk. On the other hand, the detection of company operating and financial difficulties is a subject which has been fundamentally discussed in financial ratio analysis. As a result, this study tries to analyze existence and relationship between financial ratios of recipients and their reimbursement ability by evaluation of their conditions in awarding them credit. Furthermore, according to the credit risk context, some of financial ratios are selected and their values are obtained as the input... 

This research studies multi-objective master production schedule (MPS) and advanced order commitment (AOC) in two-stage supply chains. Simulation-based experimental analysis evaluates the impact of environmental and MPS design factors on schedule cost and instability. The results provide insight into multi-objective MPS design considerations through rolling schedule policies. The study reveals that the manufacturer's production smoothness utility coefficient and its interaction with other experimental factors considerably impact the system's performance. In addition, it introduces a simulation framework with embedded mixed integer programming models that could be used as a framework for... 

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... 

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