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Objective: One of the important issues in project management is project scheduling. Since projects are faced with high uncertainty, project scheduling is of high importance under uncertain conditions. The purpose of this research is to present a robust multi-objective model to optimize project scheduling with limited resources by considering uncertainty, in which activities have several execution modes with uncertain duration, costs, and resources. Methods: After reviewing the extant theoretical literature on project scheduling, the assumptions, parameters, and variables of the mathematical model were determined. Then, considering the goals and limitations of the project scheduling problem in deterministic conditions, a mathematical model was developed. This model was transformed into a single-objective model by the epsilon constraint method. To consider the uncertainty in the parameters of the problem, robust optimization, and Bertsimas and Sim's approach were used. Also, Robust optimization of the single-objective model was developed to consider the uncertainty. Results: According to the obtained results, while the duration of the project increases with the increase in the tolerance of non-deterministic parameters; the percentage of project duration changes decreases for higher values ​​of robust parameters. Conclusion: The robust parameters are with negative coefficient in the objective function, so their increase leads to a decrease in the objective function value. The highest reduction of the objective function is when the robust parameter is changed from two to one. This coefficient decreases with the increasing value of the corresponding parameter i.e., as the value of stability parameters increases, its effect on changing the value of the objective function decreases. The cost parameter was changed between -40% to +40% for the value of gamma 10 (the state where 10 activities of the project are non-deterministic) for different variation values of parameters of non-deterministic activities. Its effect on the values of the objective function indicated that the variation of the cost parameter in the range of 0 to +40% turns it into an unnecessary constraint and that its change has no effect on the value of the objective function (project duration). Also, in the range of 0 to -40%, the cost reduction caused a decrease in the value of the objective function (increasing the project duration) and the maximum impact of the reduction of the project budget related to the situation where the uncertain parameters of time and cost change by 40% and 50%.

Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition.

Objective: Energy saving regarding its high share in energy consumption of industries has a significant impact on the growth and development of countries. This study aims to evaluate the performance of Iran's electricity generation, transmission, and distribution processes. Methods: By using a network data envelopment analysis (DEA) model, the overall efficiency scores, and efficiency scores of production, transmission, and distribution processes are calculated. The network structure considers the main and surplus inputs (fuel consumption costs, internal consumption, transmission substation capacity, power transmission lines length, transformers capacity, low and medium voltage network length), intermediate sizes (net power generation, gross power generation, and delivered energy), desirable (nominal power, actual power, and delivered energy) and undesirable outputs (environmental pollutants, and energy losses). Results: An algorithm based on a multi-objective programming model is presented to evaluate network performance and simultaneously to evaluate processes efficiency. The proposed algorithm is used to evaluate 16 electricity areas in Iran. Conclusion: The results showed that Tehran, Khorasan, Khuzestan, and Zanjan are the most efficient areas.

a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria.

Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty where demands are considered as stochastic variables, while cost and utilization parameters are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a numerical example.

Inventory control is an important subject in supply chain management. In this paper, a three echelon production, distribution, inventory system composed of one producer, two wholesalers and a set of retailers has been considered. Costumers' demands follow a compound Poisson process and the inventory policy is a kind of continuous review (R, Q). In this paper, regarding the standard cost structure in an inventory model, the cost function of system has been approximated using Response Surface Methodology as a combination of designed experiments, simulation, regression analysis and optimization. The proposed methodology in this paper can be applied as a novel method in optimization of inventory policy of supply chains. Also, the joint optimization of inventory parameters, including reorder point and batch order size, is another advantage of the proposed methodology.

The development of Multiple Criteria Decision Making Techniques is to provide the possibility of using multiple criteria in decision making problems. Multi-criteria decision making problems primarily relate to choosing the best option among several available options and according to some indicators. One of the major issues in this regard is to determine the importance weights of decision criteria. Various methods such as Entropy, LINMAP and Saaty’s method have been presented for determining the weights of the indicators. In this paper, a non-linear model based on the logic of TOPSIS method is proposed to estimate the ideal weights. Simplicity, ‌ Reduction of received data from the decision makers in the evaluation process, and the possibility of considering his comments about the preference of indicators to each other can be cited as the advantages of this method