Sharif Digital Repository

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... 

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, 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 research, among the levels of the blood supply chain, hospital is chosen for study because of its importance. A model of inventory management of hospital blood bank is presented. In this case, we consider some assumptions which are more realistic. These assumptions include two types of emergency and ordinary demands, young blood demand, and finally, a cross-match process. In the existing articles, although blood groups considered in mathematical models, the priority of allocation of blood groups is not considered in these articles. In this study, three mathematical models are developed with deterministic parameters. In the first model, the assumption of ordinary and emergency demand,... 

The present study investigates dynamic pricing in case of urban transportation operators competing on an identical network. Game theory has been adopted as an approach to the modeling and solutions.Game theory is a mathematical theory widely applied in competitive situations.The proposed model takes the players’ interdependency problems into account to predict their decisions as well as the displacement on the network about equilibrium point. The problem is modeled in no-cooperation state using the dynamic Nash equilibrium; Gauss-Seidel algorithm is used for the solutions considering the discontinuity of the decision space. A comprehensive sensitivity analysis has also been conducted on the... 

As the number of patients waiting for receiving kidney transplantation increases all over the world, the average waiting time for a patient in queue rises too, and consequently, the rate of death goes beyond the accepted level. Also, unbalanced numbers of donors and patients with same blood type make the problem bolder. The aim of this research is to design a queuing model in which kidney could be transplanted from deceased donors to patients with different but compatible blood type (Cross-Transplantation) in order to make the average waiting time for patients with different blood type closer to each other. In other words, the transplant system is supposed to make a decision whether to... 

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... 

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 research, we consider a made-to-order production system which receives orders for a single product. The demand is stochastic and price-dependent. We also consider a sale index that links the demand of each period to the demands as well as to the quality of the previous periods. The variety of quality levels and its effect on the amount of orders make this revenue management complicated. Furthermore, the price of each product is related to its quality and sales index. Furthermore, reference price causes the model to be significantly more complicated. We model the problem within the framework of dynamic programming approach to maximize the discounted profit of the firm during a... 

In this study we are looking for an optimum way to select a multi-asset portfolio. Usually portfolios only contain stocks and bonds. Here we examine if adding other sort of assets like gold, currencies, bank deposit and gilt-edged securities are optimum or not. We are doing a comprehensive research of adding other kinds of assets to the portfolio. With modeling and solving the real situation, data and constraints of Iranian financial market we decide to whether add other assets to our portfolio or not. The results show in recent years low risk assets are optimum to add to the portfolio as it is was anticipated to the major economic of Iran  

In this study vendor-managed inventory policy in supply chains is investigated and a formulation is developed to optimize replenishments from vendor to retailer and also price for negotiator customers. As a result, we consider a two echelon supply chain containing a vendor and a retailer managed according to VMI policy. The goal is to find the optimal replenishment from vendor to retailer at the beginning of the month and by using dynamic programming approach to maximize the supply chain profit. Demand is nondeterministic and it is supposed Poisson distribution with unknown parameter. We will consider Gamma distribution for this parameter which its parameters are learning in dynamic... 

The Operating Room (OR) is one of the most expensive resources of any hospital. Surgical costs typically account for approximately 40% of the total hospital resource costs, while surgeries typically generate around 67% of hospital revenues. Therefore, it is important to control the surgery costs while maintaining a high quality of care. In this study, all the decisions related to the Operating Theatre are being hierarchically considered in strategic, tactical and operational levels. The Operating Theatre consists of operation rooms, a recovery unit, an intensive care unit (ICU), a critical care unit (CCU), and ward units. In this model there exist many uncertainties, which the robust... 

In this research, we consider an assemble-to-order production system which accepts order for multi-products. Demand is stochastic and price-dependent. We also consider a sale index that links demand of each period to the previous periods demand. The variety of products which use common parts makes this revenue management system complicated. Furthermore, since the price of each product can have an effect on other products demand, it causes price dependency among them. We model the problem within the framework of dynamic programming approach to maximize discounted profit of the firm during a finite and multi-period horizon. We develop an integrated system of pricing and production decisions... 

In this thesis, we address the Revenue Management problem, when there is cancellation, no-shows, overbooking, and refund constraints. We model this problem using Dynamic Programming. Given the fact that this problem is intractable, we develop a linear programming model that is equivalent to our problem. We first prove that the optimal value of linear programming objective function serves as an upper bound for the Dynamic Program. In the next part we develop a bid-pricing technique, using the shadow prices of the aforementioned linear program, to decide upon rejecting, or accepting the seats, in the network. Using the Monte-Carlo Simulation we show that there is a small distance between the... 

In this study, the problem of pricing and inventory control for non-instantaneous deteriorating items with two separate warehouses is considered. One warehouse is owned and the other one is rented. The rented one has a higher holding cost due to better preserving facilities. In the literature, a common assumption is that the deterioration of items starts at the instant of their arrival, while in fact the majority of goods have a short time span for maintaining quality or original condition, during which no deterioration occurs. For non-instantaneous deteriorating items, the common assumptions may lead to the overvaluation of the total annual relevant inventory cost. In the present study a... 

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...