نشریه تصمیم گیری و تحقیق در عملیات
سال ششم شماره 2 (تابستان 1400)

The proposed model is a time-cost-quality trade-off model with three objective functions: minimizing project completion time, minimizing total project cost, and maximizing total quality of activities in a PERT network with multi-mode activities.

After presenting the appropriate mathematical model, based on the design of the experiments, the possible levels of each decision variable were determined. Then, using the simulation process, random values ​​of decision variables and response variables were obtained each time, and by using neural networks, we established a neural network model. To solve this model, two algorithms NSGA-II and MOPSO were used.

To evaluate the efficiency of the model, the designed model was implemented in the maintenance department of Abtin Ardakan Steel Company. According to the results, it is found that the NSGA-II algorithm has better performance than the MOPSO algorithm.

In this paper, a model was presented that by eliminating unrealistic assumptions and taking into account the realities of the project is closer to reality than the models presented in this field and has more application in practice.

The chemical attributes of Technetium-99m have made it popular for most medical imaging procedures. However, in recent years, the decay product of molybdenum-99, i.e., technetium-99m, has become expensive, and its routine availability can no longer be taken for granted. We proposed scenarios to maximize the throughput of Technetium-99m which is used to produce radiopharmaceuticals in a medical imaging center.

We proved a recursive function to imitate the decay dynamics of Technetium-99m, which is used in 80 percent of medical imaging. Then, we proved necessary and sufficient optimality analysis for this function.

We found optimal scenarios for distributing the radiopharmaceuticals into elusion periods according to clinical considerations.

 We developed a rigorous mathematical model based to maximize the throughput of radiopharmaceuticals in a molecular imaging center.

This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic - environmental variables nexus, considering different environmental indicators, are not consistent with each other; it seems necessary to use a comprehensive index that includes all dimensions of environmental pollution.

Using 6 environmental indicators related to two groups of selected OPEC and OECD countries for the period 2010 to 2019 and using three methods including principal component analysis, kernel-based principal component analysis, fuzzy robust principal component analysis, the creation of a composite environmental quality index is examined.

The results showed that FRPCA method has more efficiency and ability in weighting environmental indicators than other methods due to having the lowest error criteria. Therefore, using this method, the composite index was calculated. Moreover, the results showed that along with the upward trend of economic growth, the quality of the environment follows a downward trend in OPEC countries and an upward trend in OECD countries.

 Based on the results, policy recommendations as well as new perspectives and suggestions for future studies are presented.

Increasing the population, followed by increasing human needs and problems related to transportation, has led managers to seek for solutions by the goal of increasing economic profitability and reducing costs. Therefore, in this study, a set of location decisions, vehicle routing and inventory management has been used as the main tools to face with these problems.

model has been solved and validated by Gams software using Epsilon method in small scale. Since this is one of the NP-Hard problems, meta-heuristic algorithms, MOPSO, MOFF, MOIWO, NSGA-II have been used to solve large-scale problems.

The results of all comparative criteria show the superiority of the MOIWO algorithm over other algorithms and the appropriate efficiency of these methods in solving the mathematical model, especially in high dimensions and short times.

 In this study, the design of hazardous materials transportation network is considered by considering the decisions related to location, routing, inventory. For this purpose, a new multi-objective mathematical model with the objectives of minimizing cost, minimizing travel time and maximum social responsibility is presented. This mathematical model can be used in different areas and different dimensions.

Automatic short answer grading is known as the task of automatic assessment of answers based on natural language using computation methods and machine learning algorithms. The proliferation of large-scale intelligent education systems and the importance of assessment as a key factor in the education process have increased the need for highly flexible automated systems for scoring exams.

While in the process of automatic short answer grading, student's answer is compared to an ideal response and scoring is done based on their similarity, semantic relatedness and similarity measures can also be employed for this aim. To this end, several semantic relatedness and similarity measures are firstly compared in application of short answer grading. In the following, a method for improving the performance of short answer grading systems based on semantic relatedness and similarity measures which leverages students' answers with the highest score as feedback is proposed.

In order to evaluate the performance of semantic and similarity relatedness methods in application of automatic short answer grading and the prposed model, various experiments were concucted on Mohler and Mihalcea dataset that contains 7 questions and 630 answers.

 Based on the empirical experiments not only semantic relatedness and similarity measures have great efficiency in automatic short answer grading but also using students' answers as feedback can considerably improve the accuracy and performance of semantic relatedness and similarity measures for this task.

Using hesitant fuzzy numbers as a combination of two common types of evaluation: self-evaluation and evaluation by judges, in order to make real and fair evaluations. Updating the Choquet integral method to apply with hesitant fuzzy numbers in the evaluation process, and use it to solve decision problems such as evaluating employees and organizations. 

 The method of conducting these studies is based on the pattern of library studies.

 Deficiencies such as showcasing the evaluators during the evaluation period on the one hand, and the lack of mastery of external judges on some organizational complexities and the apparent and hidden motivations of the evaluators for unrealistic evaluation in the self-evaluation process, on the other hand, are some of factors that challenge the evaluation results, and these defects in the hybrid evaluation model are eliminated using hesitant fuzzy numbers. In addition, evaluation indicators in many cases interact with each other and have so-called positive and negative effects on each other. Choquet Integral is able to take this into account and take the assessment one step closer to becoming more realistic. Therefore, its computational development with hesitant fuzzy numbers, which has been considered in this article, can helps the evaluation system and performance of employees and organizations.

 Computational development of hesitant fuzzy numbers with the help of Choquet integral, using the Choquet integral of hesitant fuzzy numbers in solving multi-criteria decision making problems such as employee and organizational evaluation.

Strategy is the main source of long-term growth of organizations and if the strategy is not successfully implemented, even if the appropriate strategies are adopted, this process is useless. The purpose of this paper is to propose a comprehensive hybrid model for predicting organizational performance indicators.

In order to achieve the research goal, first, a balanced scorecard as a tool for designing performance evaluation indicators and network data envelopment analysis as a tool for performance evaluation has been used. Then, by matching the Malmquist productivity index with the mentioned hybrid model, the model of progress and regression of organizations in two consecutive periods is presented. Finally, by combining the proposed models and artificial neural networks, a solution is presented to evaluate the performance of 500 bank branches and also to identify their progress and regression.

The obtained results show good accuracy and less computational time of the proposed hybrid models.

The present study can add to the existing knowledge on performance appraisal of enterprises by providing a hybrid model using network data envelopment analysis and balanced scorecard; And the proposed methods can be promising tools for evaluating the performance of organizations, especially big data.

Deciding on the existence of the mean reversion property in financial data has attracted the attention of many researchers, and different tests have been proposed to evaluate the presence of this feature in the data. However, due to the highly variable nature of financial markets in different time periods, each of these tests generally presents different results and on the other hand, detailed theoretical analyzes have not been performed on how to identify the points of mean reversion at each moment of the process. In this study, a method was proposed to identify the mean reversion points in financial data. This method can be used as a decision criterion for entering or leaving the market in swing trading strategies.

The historical data on the price of gold (Oz), Bitcoin, EURUSD ratio, Tehran Stock Exchange index, exchange rate (Dollar) and the price of gold coins during the period 2013-2020 and Ethereum during 2016-2020 were collected and analyzed. First, the hypothesis of mean reversion was tested through augmented Dickey-Fuller and Hurst exponent tests and in order to identify the points of mean reversion at each moment of the process path, the maximum values of the difference of data with its moving average value were modeled through the Gumbel distribution.

The results showed that Dickey Fuller and Hurst exponent tests provide different results in detecting this feature. Also, the results by estimating 95% quantile of the distribution showed that detection of these points using the quantile of the extreme value distribution (Gumbel) ​​was correct in at least 47.78% (57.5% excluding Bitcoin) and at most 92.85% of the detected points. The results of the prop test for evaluating the accuracy of detected points showed that the detection of these points in the process is not random and the proposed theory for the identification of these points works well. Optimization of results based on further analysis is also expected.

 Recognition of successive mean reversion points can be considered a sign of the formation of price bubbles in capital markets. Therefore, the extreme values distribution ​​in consecutive maximums of the price difference from the trend can also be a suitable distribution to detect bubble formation.