The Decision Science and Intelligent Systems

Government investment in development projects is a primary mechanism for creating new production and service capacities. However, these projects often face challenges, including budget deficits and incomplete funding allocation. Given the competition between current expenditures and development credits for public funds, employing efficient management mechanisms for development projects is crucial. Optimization methods and efficiency analysis models, such as Data Envelopment Analysis (DEA), can be valuable tools. DEA is a non-parametric method used to evaluate the efficiency of homogeneous decision-making units (DMUs) based on multiple inputs and outputs.

This study examines the performance of capital investment credits in Lorestan Province over six years, categorized by COFOG divisions and groups, using the Fair DEA (KA) model. The results show consistent rankings for 2021, 2020, and 2018, while other years exhibit differences. A comparative analysis with other provinces using the CCR and CSW models reveals that Lorestan’s efficiency was below the national average in 2017 and 2018 but improved from 2019 to 2021, surpassing the national average.

In today’s rapidly evolving innovation landscape, managing intellectual assets and assessing their maturity plays a crucial role in gaining competitive advantage, creating value, and achieving organizational success. Therefore, this research aims to present a predictive model for the maturity of intellectual capital in knowledge-based firms located in industrial parks. This model employs a hybrid approach that integrates Multi-Layer Perceptron (MLP) artificial neural networks with genetic algorithms and firefly algorithms. The research is applied-developmental in nature, descriptive-modeling in methodology, and mixed-methods (qualitative and quantitative) in data type. The qualitative sample consisted of 12 experts and specialists selected through snowball sampling, while the quantitative segment included 212 knowledge-based companies chosen using stratified random sampling. Data collection tools included a review of scientific literature, specialized interviews, and standardized questionnaires. After assessing the validity and reliability of the questionnaires, the data were analyzed using the Delphi method, confirmatory factor analysis, MLP neural networks, and their combinations with genetic algorithms and firefly algorithms. SPSS, PLS, and Python software were utilized for this purpose. The results indicated that all models examined were capable of predicting the maturity level of intellectual capital. However, the hybrid model combining neural networks with the firefly algorithm demonstrated significantly better and more accurate performance in predicting intellectual capital maturity levels, achieving evaluation metrics of 95.35% accuracy, 94.35% precision, 95.35% recall, 94.41% F1-score, and an AUC of 0.996.

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Resource depletion and climate change are forcing business managers to transform towards intelligent sustainability-based models, and the pharmaceutical industry is no exception. With the aid of management approaches and technologies of industry 4.0, it is possible to become efficient with least costs, while considering environmental and social principles. Based on these issues, the present study provides some important findings. First, circular economy practices are effective on technological innovation in Industry 4.0. Industry 4.0 technologies increase the efficiency of circular economy practices. Second, innovation technology is positively related to supply performance from a sustainability perspective. Finally, by relying on the transformational style and creating plans, a way to change towards sustainability and sustainable creation can be gained. Hence, the study provides a deeper understanding of the concepts of the circular economy and Industry 4.0 technologies and provides insights into ways to improve intelligent sustainable performance in the current digital age. The statistical population of the current research is various pharmaceutical companies such as buying companies (Exir, Alborz Daro, Caspian Tamin, etc.), raw materials producer (Sina Glass Company) and glass pharmaceutical company that are related to the supply of upstream and downstream products, and this makes the present study comprehensive.

Evaluating the performance of human resources is considered one of the main pillars of the preservation and survival of any organization. Universities, as organizations that have the goal of training specialized human resources in addition to producing science, need to evaluate the performance of their faculty members more than any other organization. The evaluation of the performance of the academic staff revealed their strengths and weaknesses and is considered a prelude to scientific development and achieving the goals of the university. Today, evaluating and improving the performance of professors in universities is one of the most important and key issues for the promotion and development of the level of research and science production. In this research, taking into consideration the evaluation dimensions of faculty members' promotion, which includes cultural, educational, research, and executive activities, each person's rank was determined using the DEA technique. By using the results of solving the data envelopment analysis model, the people who had the conditions to be promoted to a higher rank were identified, and suitable suggestions were made to improve the rank and finally to raise the rank of the faculty members.

The performance of the economy has a great impact on the welfare of the society and people's lives. Considering the importance of the topic mentioned, in this study, the performance of Iran's economy has been examined from an economic perspective.

Data Envelopment Analysis and justice indicators have been used to survey the performance. The indicators of justice used in this study are the gross domestic product at constant prices of 2010, the share of the population of higher education graduates among the working people of the country, the employment ratio of the population aged 10 years and older, the inflation rate, the unemployment rate and the Gini coefficient, which are the most important indicators economic. The time period considered in this study to survey the performance from the economic dimension is the data of 2011 to 2018. After collecting the data, GAMS software was used to run the model. The results of this study show that the best economic performance is from 2018 and the worst performance is from 2011. Then, using the results obtained for years, they have been ranked based on their economic performance.

According to the results obtained from this study, it can be seen which economic factor has caused the decrease in the performance of the economy, therefore, one should look for a way to improve the performance of this factor. In this case, the performance of the economy improves and it causes the living standards of the people in the society to improve and as a result, the welfare in the society increases.

Budget evaluation and ranking play a crucial role in understanding a country's financial priorities and shaping its economic direction. In recent years, economic challenges such as sanctions, fluctuations in oil revenues, and inflation have influenced the distribution of financial resources, leading to shifts and restrictions in spending priorities. During periods of rising oil prices, there is greater flexibility for investing in public services, while in times of recession, austerity measures tend to reduce these expenditures, affecting overall development in these areas. Given the importance of budgeting, the main objective of this article is to evaluate and rank the country's budgeting system from 1374 to 1398 using the Data Envelopment Analysis (DEA) technique. The purpose of budget allocation ranking is to provide a clear understanding of how resources have been distributed over time and to assess the impact of these decisions on national development. This analysis helps identify strengths and weaknesses in fiscal policy and offers insights for future budgeting strategies that can better balance economic growth, social welfare, and infrastructural development.