In this research connectionist modeling of Decision Making has been presented. Important areas for Decision Making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional Decision Making. In these kind of Decision Making the role of Amygdala is so important and we expect that a model with two parts (thalamus and Amygdala) can have the best result in modeling participants Decisions without considering any part for cortex process. For this purpose 56 participants composed of 20 men and 36 women wanted to do Iowa Gambling Task. Results show that the networks related to two parts model predict 62. 57 Percent’ s of participant’ s Decisions and the 3parts model has 68. 46 Percent’ s of that. In conclusion it can be said that three parts modeling has been more success than mathematical two parts model in predicting the performance of participants and the difference is significant. In other words cortex role in this kind of Decision Making is quite important.

Meat in terms of providing needed proteins, food security in the country and its share of value added agriculture in agricultural sector has a special place. In this study, first steer feedlot was modeling (Holstein and racial composition) in order to estimate the optimal age for the sale of animals using a simple linear mathematical programming (static) and then Decision Making for livestock feeding or replacement of new one was discussed by Dynamic planning. Required data was prepared by questionnaire from 5 industrial livestock in Qazvin (Holstein and racial composition) and the Statistical Center of Iran. Results showed that if an animal life considered cyclical, sixteen months is optimal; and if it is several cycles, nine months will be optimum production. For sensitivity analysis of parameters in the model, different scenarios were considered in terms of growth performance parameters, weight was more effective than other livestock parameters. Ultimately the Decision Making of animal feed and replacing it with a new one was evaluated using stochastic Dynamic programming.Results showed that in weights 300-398, livestock nutrition should continue and therefore in average 0.5 kg weight per day increase and in weights 405-440, new livestock must be replaced.

This study aimed at presenting a method for formulating optimal production, repair and replacement policies. The system was based on the production rate of defective parts and machine repairs and then was set up to optimize maintenance activities and related costs. The machine is either repaired or replaced. The machine is changed completely in the replacement process, but the production rate of defective parts decreases in the repair process. The repair time and number of repairs will affect this process. The aim of this study is to find Decision variables that minimize the final cost of repair, replacement, maintenance and prevention of failures in a given time horizon. As a case study, the variables were evaluated at Arak Pishgam Company to achieve optimal conditions. The proposed model was developed based on the semi-Markov Decision process (SMDP). In addition, stochastic Dynamic programming model was used to achieve optimal conditions.

The concept of a multi-fuzzy set (MFS) is a hybrid mathematical approach that aids reasoning and Decision-Making in situations characterized by impre cise information and multiple occurrences. Researchers have developed robust MFS-based frameworks that facilitate Decision-Making by identifying optimal alternatives. However, these frameworks often struggle to select the desired alter native in uncommon situations. To address the limitations of existing methods, we incorporated relations and operators with MFS to better measure levels of uncertainty. To enhance the effectiveness of Decision-Making, we proposed two approaches based on the significance of the criteria within the MFS framework. First, we introduced a relative weight-based approach, where the weight of each criterion is estimated Dynamically. Second, we developed a normalized-based Decision-Making approach, which generates optimal solutions based on normal ized score factors. We demonstrated the effectiveness of our proposed approaches using semi-realistic cases in health science and healthcare management. Further more, we evaluated the performance of the healthcare system across different socio-economic regions, which helped to illustrate the relative importance of the criteria

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of Decision Making units (DMUs) which consume the same types of inputs and producing the same types of outputs. Assuming that future planning and predicting the efficiency are very important for DMUs, this paper first presents a new Dynamic random fuzzy DEA model (DRF-DEA) with common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and expected values of the objective functions. In the initial proposed DRF-DEA model, the inputs and outputs are assumed to be characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective functions are the expected values of functions of normal random variables. In order to improve in computational time, we then convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of runtime and solution quality.

Renewable energy is vital for the future of the energy supply because of its properties, which include sustainability, affordability, and environmental friendliness. The low dependability of these sources is a disadvantage due to their nondeterministic and unpredictable production patterns. Utilizing several energy production sources in conjunction with energy storage and backup sources could relieve the problem of system dependability. This study recommends a hybrid microgrid that is independent of the grid and consists of primary sources of wind and solar energy production in addition to a battery and generator backup system. This is achieved through the use of Dynamic Decision-Making algorithms, modeling, construction, and assessment, as well as thirteen distinct strategies for the electrical supply of residential units. Under the scenario to use 24% renewable energies, the consumption of fossil fuel is 1,1 liters per day, and the yearly production energy of the total renewable energy conversion system is comparable to 1,697 kWh with a net present value of $553.68. By increasing the renewable energy factor to 54 percent, the consumption of fossil fuel is reduced to 0.69 liters, and the annual production energy is increased to 1,652 kWh. Consumer energy management with a renewable energy factor of 100 percent, an annual energy usage of 1,933 kWh, and a net present value of -$379.