Sharif Digital Repository

Project Portfolio Selection as a part of Project Portfolio Management, is a very important issue for the organizations and is a key activity. In this thesis at the first with studding stock portfolios, it has been shown that because of mathematical features, Conditional Value at Risk is a coherent risk criteria and used efficiently in stock portfolio management. So in Project Portfolio Selection we use Conditional value at Risk as coherent risk criteria and based on that we will present a model that not only reach to the maximum utility but also but also reach to the minimum feasible Conditional Value at Risk. After that with the use of this model and with the use of mathematical features... 

This thesis focuses on the network capacity control problem while, risk-averse criteria is considered for decision making. Network revenue management models have wide applications in various industries such as airlines, hotels, railways, rental halls due to handling multiple resources. All models in the related literature are developed based on risk-neutral criteria. However, in this thesis we consider risk-averse policies for many reasons such as risk reduction, covering fixed costs rather than maximizing revenue, and earning some specific revenue within a specific time period. We apply dynamic programming by adopting two types of utility functions: 1) additive and time-separable utility... 

In this project railway industry is introduced as an optimal alternative for transportation in this country. To select the best choice, four railway technologies that have commercial capacity are presented and the most appropriate one is selected with respect to several criteria, including fuel consumption. The purpose of this thesis is to present the best technology in order to improve Iranian railway transportation by considering the shortage of fossil resources as well as pollution.In this project,the copmparison of alternatives and assessment criteria for best technology selection is done by Analytical Hierarchy Process (AHP) and the questionaries are filled out by industry and... 

Markowitz’s asset allocation model with mean-variance approach has well-known issues, amongst them are too much sensitivity to input parameters (especially asset returns) and portfolios that are not diversified well. In order to cope with these issues, risk parity approach has been suggested, which by omitting return parameter from the model, tries to balance portfolio’s risk among different assets. However, risk parity has not been appreciated by researchers due to ambiguity in the investor’s utility function, lack of a solid mathematical framework and its heuristic nature. This study, first tries to develop the mathematical theory of risk parity at the asset level and at the risk factors... 

This paper investigates the interaction between the beneficiaries of an employee stock option plan within a Stackelberg game framework. The beneficiaries are shareholders and employees. In the proposed model, shareholders, as the leaders of the Stackelberg game, determine the optimal features of employee stock option grants. In response, employees, the followers of the proposed Stackelberg game, maximize their own profits by determining their own effort level by considering that every effort level of employees has an associated cost and expected income for employees. It is assumed that the stock price follow Geometric Brownian Motion process with a known drift rate and volatility. Also, it... 

Nowadays, stock index forecasting has become an important issue for decision makers as well as investors, and such a forcasting is increasingly difficult thanks to multifarious impact of factors. In this regard, machine learning approaches can be used to analyze and predict the stock index and its influencing factors. Such an approach not only provides decision makers to make more becoming decision associated with the monetary and financial sectors of the country, but also allows investors to chose more appropriate portfolio (lower risk) by forecast long-term stock.In this study, we intend to better predict the stock index based on the necessity by examining the economic factors affecting... 

In this paper, a new method for handling power plants preventive maintenance scheduling problem is proposed in which a decentralized framework is applied in order to help the electricity markets to prevent data sharing. The new approach is the extension of alternating direction method of multipliers (ADMM) along with several heuristics and refinements to omit the effect of non-convexity of the problem. Since, the new methodmerely needs minimal information exchange, it is completely practical. In order to evaluate the performance of the proposed method, a case study based on generating system of Iran is presented. It is shown that the proposed model obtains acceptable results  

In this study, the problem of assortment optimization considering a non-parametric choice model will be introduced. The present study is generally divided into two main parts. The first part deals with modeling customer behavior using the definition of tree models of customer preference behavior. The second part is related to the main problem solving framework in order to get the best product combination in the product classification basket. This research is related to the online seller who intends to sell a certain number of types of products without inventory limit. Customers entering the system, according to their specifications, are provided with the appropriate product classification... 

Predicting the price that has the least error and can provide the best and highest accuracy has been one of the most challenging issues and one of the most critical concerns among capital market activists and researchers. Therefore, a model that can solve problems and provide results with high accuracy is one of the topics of interest among researchers. In this project, using time series prediction models such as ARIMA to estimate the price, variables, and indicators related to technical analysis show the behavior of traders involved in involving psychological factors for the model. By linking all of these variables to stepwise regression, we identify the best variables influencing the... 

In this thesis, we study the problems of staffing and controlling queueing systems with an uncertain number of servers and space constraints. We assume that agents are allowed to set their schedules. Furthermore, in the condition that there are space constraints in each shift, a model is presented to obtain the number of agents we need to hire from the beginning by minimizing the daily cost incurred by the manager. Due to the uncertainties in the system, the model solves the problem by considering the different modes that may occur in each shift. Because randomness in the number of agents creates congestion in the system, and also special health conditions that may arise in a call center... 

Human blood is a valuable and scarce product which is only produced by human body. There is no replaceable material for our blood and there has always been a constant need to donors and blood products in societies. Like every other product, blood has to be transfused to patients after it is donated and this process requires a complete and effective blood supply chain. Considering all properties, complexities and uncertainties of a blood supply chain, we need to develop stable methodologies in order to simplify decision making in blood supply chain. In this research, we develop a novel blood distribution system from a regional blood center (RBC) to its nearby hospitals. It helps RBCs plan the... 

Prescribing and consuming drugs more than necessary is considered polypharmacy, which is both wasteful and harmful. In this study, an innovative data mining framework is developed for analyzing prescriptions regarding polypharmacy. The approach consists of three main steps: pre-modeling, modeling, and post-modeling. In the first step, after collecting and cleaning the raw data, several novel features are extracted for physicians and patients. In the modeling step, decision trees are applied to generate a set of If-Then rules to detect and describe physicians’ features or patients’ features associated with polypharmacy. A novel approach based on the response surface methodology (RSM) is... 

Nowadays, considering intense competition and the need for continuous improvement in organizations, research and development projects have attracted the attention of many organizations. Due to the intense competition and turbulence in the competitive market, there is a need for flexibility in the strategic decisions. In this thesis, we introduce a systematic framework for executive decision making within a strategic planning framework using the real options valuation method. This two-stage model first takes value of strategic projects in different periods considering the market effects of using the real options opportunity approach. Then the effect of this selection on all the options is... 

In this thesis, the Conditional Value-at-Risk measure (CVaR) is used in two basic decision making problems under uncertainty. This measure is used previously in the field of risk management. The first problem is decision tree problem which is a multi-stage stochastic decision problem. In this problem, the risk of a decision maker is estimated by CVaR measure and a linear programming model is presented by using disjunctive programming theory. The importance of the developed model is in its generality for modeling all problems with decision tree structure. Then, its computational performance is compared to intuitive nonlinear programming models for the problem. Moreover, the presented model... 

Sales promotion plays an important role in increasing the profit, attraction, and retention of consumers. Temporary discounts are a popular promotional tactic that is applied in diverse situations. In this study, we examine some situations to determine optimal decisions. In the first scenario, promotions are implemented during predetermined periods, and the seller determines optimal pricing to achieve two separate objectives: maximizing profit and managing demand (clearance sales). We propose generalized inverse optimization models that determine discounted prices to make the given promotion timing as close to optimal as possible. The efficacy of our approach is demonstrated through...