Knowledge and its intangible appurtenances have not only resulted in movement in various businesses, but also, nowadays, have been viewed as the whole or a part of products of distributors' companies as well as service and military organizations.In recent years, estimation of knowledge level in organizations and industry companies has attracted considerable attentions. Contrary to a lot of prevalent MODELs used for measuring efficiency, Data Envelopment Analysis (DEA) can take into account multiple inputs and outputs. In this regard, DEA was traditionally applied with crisp inputs and outputs, while in practical cases, we need to estimate organization efficiency in a different situation in which we have to deal with fuzzy or imprecise data. The aim of this paper is to present a DEA, employing fuzzy input and output data, toward assessing the knowledge levels established in a knowledge based-organization in various time intervals. In this case, the organization is able to define some areas in which it can improve its established knowledge level.

Elastic and inelastic performance of structure members can be evaluated by Bending moment–axial force interaction surfaces in a non - linear process. The use of composite shear wall structures is common method in the past two decades. In this research, this kind of surfaces are being introduced for the first time to be utilized in composite shear walls, which can be used in analyzing and designing without the need to non - linear analysis. Composite shear walls used for high-rise buildings, offer more lateral stiffness, bending resisting moment, Energy dissipation and ductility in comparison with other ordinary walls. Here, a simple, efficient and expeditious method is presented for nonlinear analysis of reinforced concrete structural members, based on fiber method concepts. One of the considerable advantages of this method is its high speed in nonlinear analysis of structural members that makes it distinctive from the others. The OpenSees software is used for MODELling the discussed shear walls.

Haulage in open-pit mines allocates half of operational costs. Improvement in this system has significant effects in decrease of costs and increase of efficiency. Productivity in mines is a mixture of machinery efficiency and implementation of production strategies. In this paper after classifying dispatching MODELs in open-pit mines, a new MATHEMATICAL MODEL will be introduced, to execute at the beginning and also during the working shift. Execution of the MODEL before shift start creates a general vision of production parameters and grade control according to production strategies. Also execution of MODEL during the shift with real-time information tries to keep the results near the standard deviation of initial results. This MODEL can be used to develop heuristic MODELs and truck dispatching simulation softwares.

A one-dimensional MATHEMATICAL MODEL for the description of solar drying of beds of sewage sludge is developed and implemented with the software Engineering-Equation-Solver. After the discussion of relevant literature, the assumptions and general conditions of the MODEL are explained. Governing differential equations for heat and mass transport inside the sludge are derived, followed by a discussion about correlations used for the determination of surface transport phenomena and for the description of thermophysical properties. Numerical discretization is achieved locally through the Finite Difference Method and temporary through the Runge-Kutta-Method. Finally, a parametric analysis on the sludge drying process is carried out. The obtained results demonstrate the effect of ambient parameters such as solar radiation, airflow rate, gas temperature, geometric aspects etc. on the drying process. The developed MODEL may be used for further prediction and estimation of drying characteristics under several conditions.

The main purpose of planning is reducing the amount of acceptable risk regarding probable events and offering some arrangement for better success. Due to limited resources and unlimited needs, the optimal resource allocation problem is discussed. Optimal resource allocation strategy is the most important tool for strategy implementation and long tem plans for research division of universities and it can reflect policies and goals of each research division. Since resource allocation is carried out traditionally and based on experiences and intuitions, its application has caused some dissatisfaction and can not be inferred and matched MATHEMATICALly. The purpose of this research is to use mathematic theory for approaching quantity and quality of human deduction and mathematics, and will be tried by accurate studies to offer a suitable MODEL for resource allocation in the research area of university.  

Nowadays most of resource allocation is mental, empirical and based on the old methods. So it does not take general satisfaction and does not have generalization ability and MATHEMATICAL confirm, Hence using from quantity theory for nearing of quantitative and qualitative human resource is essential objective. Budgeting lows based on forecasting which always face with errors and uncertainty. These errors are Prediction errors, Measurement Errors, Implementation Errors, so mirror change in forecasting amount, Measurement and Implementation cause to hesitation on optimally and feasibility for budgeting lows. Goal of this article is designing of a budgeting MODEL in the public sector to be able for glancing uncertainty factor which with preserving optimally and feasibility allocation area in the budget low in contorting with changing, guaranty his flexibility in the also implementation. There are different approaches for glancing of effect of uncertainty which from these approaches, we selected robust optimization approach. And this MATHEMATICAL budgeting MODEL developed by using of Soyster robust MODEL, Ben-Tal & Nemirovski MODEL and Bertsimas & Sym MODEL. Until on foundation of measurement convex, decision makers from situation of now and future of country, be able use from every one these robust MODELs.

This paper deals with the cellular manufacturing system (CMS) that is based on group technology concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a cell. The most proposed MODELs for solving CMS are focused on cell formation problem while machine layout is considered in few papers. This paper addresses a MATHEMATICAL MODEL for the joint problem of the cell formation problem and the machine layout. The objective is to minimize the total cost of inter-cell and intra-cell (forward and backward) movements and the investment cost of machines. This MODEL has also considered the minimum utilization level of each cell to achieve the higher performance of cell utilization. Two examples from the literature are solved by the LINGO Software to validate and verify the proposed MODEL.

design of two phrase MATHEMATICAL MODEL for green supply chain Green supply chain has attracted considerable attention of the governments and organizations over the past decades. The objective of this research is to suggest an integrated approach for green supply chain management including two stages. In the first stage, a TOPSIS-Fuzzy approach was presented to consider uncertainty in human’ s judgment. In the second stage, we applied a robust Multi Objective Mixed-Integer linear Programming MODEL to design the green supply chain network. Although many researches have been conducted on green supply chain management, most of them have not given enough importance to integrate decision making in supply chain. Moreover, most of former researches assume that supply chain is a balanced system. Also very few studies have considered carbon emission and the related issues in supplier evaluation which cause on overall warming and pollution of the planet. The MODEL was designed for an active company in glass industry. results have revealed that the MODEL is capable of controlling the network uncertainties. . .