In this article, we will review and compare all defined kinds of variables in Modeling section of operations research process. Since in real world, problems related to managerial decision makings because of humanities and complex factors like social, psychological, economical, political and etc, have high degree of uncertainty. It seems that available kinds of variables in literature are unable to explain all possible states of real conditions; so we will introduce new variables such as second class random variable, random uncertainty variable, uncertainty fuzzy variable, fuzzy uncertainty variable and second class uncertainty variable. Also, we will propose generalization of different combinations of variables to higher levels. Then, in base of second class uncertainty variable, we will provide new versions of linear programming Model titled second class uncertainty and full uncertain linear programming Models. In continue, we will present a numerical example from uncertain portfolio selection problem to verify proposed Model. After that uncertain optimal solutions will attain by disciplined convex programming software.

A Model of consumer behavior has been formulated by using an additive utility function and the hedonic pricing approach, in a virtual market. Since, there is a time lag between ordering and purchasing products (goods and services) online and receiving them, it means the consumer makes decision under uncertainty. The level of satisfaction with products with distinctive characteristics is described by the probability vector of their expected quality. Consumer choice, as well as interpretation of the equilibrium, is derived from a mathematical process. The results show that hedonic pricing which indicates willingness to pay is affected by probabilities of the expected quality or satisfaction with product characteristics. In addition, the expected marginal rate of substitution for every two characteristics of the product equals to the ratio for probabilities of the expected quality of them.

In shallow river, regardless of the vertical acceleration against gravity acceleration, great simplifying will be created in equations that the result of them are shallow water equations. In this study shallow water equation has been used to simulate transport of pollution in 2D limited volumes method. In this method it is possible to turn differential equations ruling fluid to be solved in numerical form. To achieve better results in estimating pollution concentration in the river and Reliable range, the pollution Transformation Model uncertainty analysisprepared were examined. To uncertainty assessment in this study 5000 times repetition of uncertainty 3 calibration parameter of pollution transferring Model by the use of uncertainty algorithm GLUE had happened in river. For this matter of pollution Transformation Problem breaking a dam was used. By applying acceptability threshold indicator of total average cubes (SSE) on all the obtained simulations, 1000 top simulations were known as efficient simulations. Considering about 95 percent confidence as high and low bound of uncertainty, and by applying used d-factor and p-factor indicators, 68 and 75 respectively were obtained Which reflects the high level of concentration was observed in 95 percent confidence. Results had showed using GLUE method caused reduction of uncertainty pollution transferring Model parameters. Extraction probabilistical charts last corresponding parameter distributed with efficient simulation, parameter (n) as a sensitive and effective parameter on simulation Models. And the optimal value obtained for this parameter 0.2477.

The purpose of this article is to develop a process Model of robust strategy by using Interpretive structural Modeling. Robust strategies are capable of performing well in a variety of possible future environments. This process Model is combination of other techniques. The techniques are obust ecision making takeholder Analysis A, Assumption ased Planning A P, Failure ode and Effect Analysis F EA, Fault Tree Analysis FTA , Potential Problem analysis PPA, olution Effect analysis EA, Change Impact Analysis, ncertainty ensitive Analysis P, and cenario based planning P. Critical comparative review of techniques demonstrated that these techniques are not complete, but provide a good starting point. In this article, these techniques were combined together in a systematic manner by using Interpretive tructural odeling. The resultant prescriptive process Model of robust strategy has sub processes. The sequence of sub processes was specified and also the input, output, and feedback of each of them were obtained.

Material Requirements Planning (MRP) Model is a production management system which is successfully implemented in many factories. This paper extends this Model for a special kind of production and service systems in which the decision making parameters regarding the operations are stochastic. Vehicle overhaul plants, locomotive rebuilding factories and maintenance centers are some cases in which material requirements to fulfill the opeations are subjected to part situations. This paper considers the part situations in three categories: Operational (does not need any repair operation), Repairable (need to get some correction actions) and Replaceable (need to be replaced by a new part). Modifying the classic MRP, we enhanced it to plan the material requirements under these categories. The proposed MRP Model benefits from all simplex features already exist in classic MRP.