A Multi Objective Optimization Approach for Resources Procurement of Bank

Calculating total cast of bank resources procurement methods which include current -free loan deposit, saving interest-free loan deposit, regular and net short-term investment deposit, long-term investment deposit and surety bond cash deposit and presenting their optimal integration require precise scientific studies. Hence, this study is an attempt to know which methods are the best optimal integration banking resources. Linear and ideal planning techniques are used to find an optimal solution according to existing mathematical models. We use three algorithms to construct mathematical models. In the suggested mathematical models, linear planning has 6 variables and 2 constraints in getting no information algorithm and 6 variables and 7 constraints in getting information algorithm and the problem is solved by WINQSB software. But, 6 variables and 8 constraints are solved by LINGO8 software. The results of the study show that presenting an optimal integration of resources procurement methods by using mathematical models is possible and is applicable with regard to determining rational and suitable constraints and ideals for all resource procurement methods. In addition, with regard to the calculation and investigation of procurement cost in financial procurement methods, it is found out that total cost (i.e. real operational cost plus nonoperational cost) is the basis of judgment for studying resource procurement cost. Further, as with the total cost of resource procurement methods, current interest-free loan deposit and long-term investment deposit are the most expensive methods while surety bond cash deposit is the cheapest resource procurement method and other methods fall in between

Developing a Fuzzy-Stochastic Multi Objectives Inventory Model

In this paper we developed an inventory model in mixed imprecise and uncertain environment. Presented model is developed form of (r,Q) and is a multi-items model with two objectives as minimizing costs (holding & shortage) and risk level under constraints including available budgetary, the least service level, storage spaces & allowable quantities of shortage. Demand distribution functions are assumed to be exponential and extra demands are supposed in two situations as lost sales and backlogging. At first we develop crisp model then fuzzy stochastic model with fuzzy budgetary, allowable quantities of shortage and shortage spaces (i.e. stochastic with normal distribution function) parameter. All of fuzzy numbers are triangular type. In methodology of solution we change model to a crisp multi-­objective by using difuzzification of fuzzy constraints and fuzzy chance-constrained programming methods, and then solve it by fuzzy logic method. Finally an illustrated example is taken and solved using LINGO package

Developing (r,Q) & (R,T) inventory control models

In this paper we have developed inventory control models (r,Q) & (R,T) in multi-items environment by two objectives as minimizing costs (holding & shortage) and risk level under four constraints. These constraints include: available budge, service level, storage space & allowed shortage quantities. Demand functions assumed normal in the study and extra demands also are backlogged. First we developed crisp models and then fuzzy stochastic models with fuzzy budge, allowed shortage quantities and shortage space which are fuzzy-stochastic parameters with normal distribution. All of fuzzy numbers are triangular typically. In this methodology we changed fuzzy-stochastic models to crisp multi objectives problem, by using difuzzification of fuzzy constraints and then solving by Fuzzy logic method. Finally we have tested an example to describe the model and methodology which is solved by LINGO package