Journal of Economic Modeling Research

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  In this paper a new model for optimal investment in advanced manufacturing machines is proposed, using fuzzy linear programming. In the first step decision-makers determine the strategic objectives of the company and their minimum acceptable achievement levels, using fuzzy numbers. Thereafter, feasible alternatives and their degree of influence to the achievement of each objective are concluded in the form of linguistic variables. To construct the model, the degree of influence of each alternative in the achievement of the objectives are considered as technological coefficients, and the minimum level of acceptance of objectives are considered as constraints (right hand side variables). Furthermore, the mutually exclusive alternatives, the interaction between machines and the constraint of limited investment of budget are included in the model. The aim of the model is to determine the number of machines that needs to be purchased in order to maximize the present value of investment. The calculation of net present value is executed based on discount cash flow, inflation rate, interest rate, revenue and costs of each machine on a fuzzy environment. Finally by presenting an empirical illustration, the performance of this model is clarified.

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  Storing the electrical energy in large scale is impossible. So, it is necessary to identify the factors affecting the electricity demand. Researchers have used different methods to forecast the future demand of electricity, among them intelligent methods and fuzzy based methods are more popular. Since ANFIS structure is based on researcher’s experience about phenomenon, the created structure may not have the best result. Therefore, we used PSO-ANFIS structure.

  In this paper long term electricity demand is forecasted until the year 2025 by hybrid PSO-ANFIS algorithm. The results confirm the high power of the Adaptive Neural based Fuzzy Inference System in forecasting the electricity demand. Results also indicate that the forecasted electricity demand will be 401 billion KWh in 2025. The prediction performance of the proposed technique is more accurate than the ARIMA model.

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In this study, by applyig a combination of Autoregressive Conditional Heteroskedasticity  and stochastic differential equations Models with Markowitz model we estimate the optimal portfolio investment in the housing market are discussed. For this purpose, use of assets, stock prices, housing prices, the price of coins and bonds during the period 1999-2013 with the monthly data. Autoregressive Conditional Heteroskedasticity  Models and stochastic differential equations results as input variables used to estimate the optimal portfolio Markowitz. Mean-variance analysis shows that during the real estate boom, housing as the dominant assets in risky assets and the largest share of funds to be allocated. During recent periods of recession as the housing sector, the housing of the optimal portfolio investment abroad and instead of stocks and investment coins in the basket of assets is considered dominant. Generally, bonds as risk-free assets in all periods as a reliable asset in the portfolio is considered optimal investor.

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In recent years, the sudden increase in environmental awareness has resulted in more attention to this sector. On this basis, the economic load distribution models, that previously observed merely the minimization of the cost of production and determination of optimal arrangement of producers based on minimization of the total cost, are now facing a fundamental change in execution and modeling. Based on this, the optimal arrangement of producers will now be determined based on two objectives of a minimum cost of production and a minimum environmental pollution. Obviously, with the situation in mind, the problem changes from a single- objective one to a multi-objective problem. The present study takes into account the question of optimal economic and environmental distribution, and its goal is to determine the optimal arrangement of producers in a situation where both the economic and environmental objectives are achieved. The model has been implemented by E-Constraint algorithm. The modeling in this study has been performed for the practical development in Esfahan Electricity Inc. market, in 2012. The results from this model show that the real performance of the market is different from the economic and environmental optimums. The results show the fact that because of the disregard for the environmental costs, the real deviation of performance from the optimum condition is practically much more serious and extensive in the environmental sector.

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Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures.  The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution. This essay based on modified  PSO  methodology .The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes , and  using updated data  during 2008- 2012 for final forecasting , to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

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In recent decade in developing countries, lack of government budget or lack of access to modern technology, persuade governments to attract private sector participation in the economy. One of the most common methods is Public-Private Partnership agreements. The real implementation of this type of partnership needs to set contracts that satisfies preferences of both parties. This research aims to solve this problem using the solutions available in the Contracts Therory Knowledge. Theoretical modeling with analyzing public-private partnership model, provide an optimal model for BOT contract. We use library method to explain the basic contract and mathematical modeling  by MATLAB software with Particle Swarm Optimization to specify the parameters of utility functions and to provide optimal contract. 
The simulation results for an optimal contract were calculated using the supposed parameters (life time, incom, costs, future incoms discount rate, salvage value of project costs) 38 years (project utilization time), 78% (principal participation after transfer time), 45% (principal participation during the operation), 7% (riskes to the principal).