Sharif Digital Repository

In this Thesis, a multi period portfolio optimization consisting stocks, gold and risk free asset is considered, in which periodical reinvestment and withdrawing is possible. Maximizing the net present value of investor’s cash flow is the objective. Due to the existence of uncertain parameters, two robust counterpart models are developed. In the first model, a conservative robust model is presented to generate feasible solution in all cases. In the second one, the conservative degree of investor is adjustable to control the risk of the model by investor appropriately. For evaluating the proposed models, the data of 5 well known stocks of Tehran market and gold prices are gathered. By using... 

Todays, one of the most important problems is optimal allocation of capital to different investment options that many researchers studied about this problem and presented a lot of solutions. In this thesis, we solve portfolio optimization problem to maximize the return of investment. The options of investment are different kinds of stocks, gold coins and we consider bank deposits as a risk free asset. We apply the problem in multi period and we can buy and sell stocks and gold coins in each period. To improve the efficiency of the proposed models, we use real data and consider transaction fees. The return of some options is uncertain, so we use robust optimization approach. At first we apply... 

In this thesis, we apply robust optimization to analyze the uncertainty of model parameters of a portfolio optimization which contains stock options. We also develop two robust counterpart models for single period and multiperiod problems. By assuming that the probability distribution of parameters is not known, their uncertainty is considered to lie within known linear intervals. Due to the existence of nonlinear relations (piecewise linear) between uncertain data (stock and option price), we present an over-conservative robust model to make the solution feasible for all parameters. However in the second model by adopting a different approach we develop a robust counterpart model with... 

In this research, the determination of inventory management policy is investigated in two-level supply chains. Two scenarios are considered to analyze this problem. In the first one, all parameters are assumed to be deterministic while in the other one, the retailer’s demand is assumed to be uncertain. We extend the previous studies by considering the real world conditions; and introduce a model in which the costs of lost-sale and backorder are considered simultaneously. A genetic algorithm is developed to solve the proposed model. To evaluate the efficiency of the model, the results are compared over five different examples when only complete backorder is considered. In case of uncertain... 

In this research, an integrated location-inventory-transportation problem with uncertainty in the parameters is analyzed using Robust Optimization techniques. Unknown demand distribution is the underlying assumption, which relates better to the reality. The model’s main purpose is to determine the number and locations of the Distribution Centers (DC) that connect the supplier to the retailers, and then to assign retailers to them. The problem’s mathematical model is developed on the basis of Robust Optimization techniques and the solution space is explored by Tabu Search. The results show that the Tabu Search method achieves optimality in small-scale cases, while providing desirable... 

In this thesis, unconstrained convex quadratic optimization problems subject to parameter perturbations are considered. A robustification approach is proposed and analyzed which reduces the sensitivity of the optimal function value with respect to the parameter. Since reducing the sensitivity and maintaining a small objective value are competing goals, strategies for balancing these two objectives are discussed. Numerical examples illustrate the approach  

Facility location decisions play a prominent role in strategic planning of many firms, companies and governmental organizations. Since in many real-world facility location problems, the data are subject to uncertainty, in this thesis, we consider the P-center problem under uncertainty of demand nodes. Using Bertsimas and Sim approach, we modeled the problem as a linear optimization model. Furthermore, we develop a tabu search algorithm to solve the problem. Finally we designed some experiments to adjust the parameters of tabu search algorithm. We presented the numerical result accordingly  

In facility location problem with uncertain data, some model parameters such as demand or transportation time are not known in advance and in many cases, no historical information exists to derive their probability distribution functions. In this situation, robust optimization is a prevalent approach. In this study, to apply robust optimization in our model, first we review its concepts and methods. Then, a mathematical model for a new facility location problem, as well as its robust counterpart is developed. Furthermore, we also develop a model for simultaneous decision making regarding facility location problem and sequencing. In this model, it is also assumed the process times are... 

Location-allocation problem is known as one of the classical problems in industrial engineering. The goals of these problems are finding optimal location of facilities and assigning these facilities to service demands from a given set of point. Location-allocation decisions are widespread through all organization in the world concerning private and public businesses. Therefore, in this study, a practical mathematical model is formulated and solved.In this paper we present a new approach for a budgeted constraint capacitated location-allocation problem integrated with cellular layout approach containing cell formation and group scheduling. Main decisions are composed of formation of...