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Improving the competitiveness of businesses through investment in industrial parks is one of the challenges that managers face for economic development. This study has been done with the aim of identifying, determining relationships and providing a structural model of investment to competitive advantage in industrial parks. The data have been analyzed in two parts, qualitative and quantitative. In the qualitative part, the variables of the model were extracted using the method of content analysis and coding and confirmed by the fuzzy Delphi method, and in the quantitative part, the hierarchical structure model was presented. The statistical population of the research is 25 experts who were selected by non-probability purposeful and snowball sampling and answered the pairwise comparison questionnaire. The findings of the qualitative section showed that the model has eight variables including "infrastructure investment", "motivational investment", "economical investment", "institutional investment", "environmental investment", "support investment", "Political investment" and "competitiveness". The findings of the quantitative section showed that the structural model has six levels, that "political investment" and "infrastructure investment" have the most influence in the model and are in the first priority. We conclude that in order to competitive advantage, managers should pay more attention to technical and informational infrastructure, as well as appropriate policies and providing regulations.

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.

There are many opportunities and threats in the decision-making environment for managers, and an organization must use research and information systems to change, monitor, and anticipate this environment. Futurism reflects how tomorrow reality gives birth to tomorrow's reality is. The purpose of this research; Analyzing the role of futures studies in the existing patterns of critical factors of multi-criteria decision-making techniques in operations research using the philosophical assumptions of the classical and critical paradigms and finally determining the appropriate strategy based on these components, to increase success and life expectancy. The present study intends to formulate exploratory scenarios of this knowledge by using the critical uncertainty approach. To develop credible scenarios of knowledge of decision techniques, the opinions of 15 experts in this field were collected using the fuzzy Delphi approach and through the critical uncertainty questionnaire. After extracting the most important uncertainties, plausible scenarios of decision-making techniques were determined with the help of experts. According to the results obtained from the opinion of experts from the critical uncertainty questionnaire, four research uncertainties were identified, identified and the scenario design criteria were used. Each of the four cases, which includes low attention to social issues versus high attention to social issues, low attention to ethical models versus high attention to ethical models, low attention to soft approaches versus high attention to soft approaches, Low attention to complex issues versus high attention to complex issues indicates a specific dual situation in the future. Each of these dual situations indicates uncertainty about the future of multi-criteria decision making. Based on these uncertainties, three scenarios were identified. These scenarios include Achilles heel, phoenix, and heel. Also, semi-structured interviews with the theme analysis approach were used, and the philosophy of past and future approaches in this field was reviewed and critiqued.