MODERN RESEARCHES IN DECISION MAKING
Year:2020 | Volume:5 | Issue:1|Papers Count:7

Ensuring the disposal of hospital waste is a major challenge for hospital waste management. the outsourcing process waste disposal can be a good alternative. The aim of this study is to select a company to outsourcing waste disposal to the hospital To help right decision making for managers. The research in terms of purpose is applied and the way of doing is descriptive-exploratory. The research community of Ministry of Health and Medical Education experts is about 10 people. By reviewing the theoretical literature, the most important criteria were identifing an screened by experts and Fuzzy Delphi method. Then The combined method of analytical network process based on fuzzy DEMATEL was used to determine the relationships and weights of the criteria, and the modified Grey Relational Analysis method was used to rank the companies. The results of analyzing the data collected through questionnaire and DEMATEL method showed that "financial" criteria is influencing and "serviceability" influenced for success in the outsourcing process. The "service accountability" sub-criterion has the highest weight and the first priority.

Customer retention is an important issue for banks. This issue in the field of machine learning and statistical issues is focusing on the problem of accurately predicting customer needs and responding to it in the bank’ s dynamic environment. Since rarely effective action has been taken by considering personalized actions to improve customer retention rates, but these decisions are at least as important as the proper identification of endangered customers. Deciding about what to do to provide specific and personalized services for customers typically leads to managers who can only rely on their knowledge. By reviewing the literature on CRM, this research provides a model that can be used to generate customer retention and marketing activities in personal banking, which includes analyzing clients and producing actions to maintain them, and analysts also set Using personalized actions to maintain customers using one of the approaches presented in this article, they are using an recommender system utilizing the opinion and experiences of customers.

Background and Aim: Decision-making as an important managerial function means choosing the best option out of available alternatives for solving a problem. Female Managers face several challenges in decision-making in healthcare organizations. The study aimed to explore women managers’ challenges in decision-making in healthcare organizations. Materials and Methods: A qualitative approach was used in this study. In-depth semi-structured interviews were conducted with 23 women managers in Tehran healthcare organizations. The participants were selected using purposive and snowball sampling techniques. The qualitative data was analyzed using thematic analysis method. Results: Twenty-two decision making challenges were identified and grouped into 5 categories. These challenges include structural challenges (i. e., mission and type of functions, organization size, workload, resource scarsity, and law and regulation limitations), procedural challenges (i. e., weakness in defining the problem, wrong goal setting, and lack of criteria for choosing the best solution), human challenges (i. e., staff shortages, and their low motivation, knowledge and skills), cultural challenges (i. e., personal belifs, personal profit seeking, indifferences and gender stereotyping), and managerial challenges (i. e., emotions, insufficient authorities and lack of top managers’ support). Female managers used solutions such as using evidences, group decision making, problem prioritizing, effective internal and external communication, transparency, systems approach, employees’ education and training and improving organizational culture to overcome these challenges. Conclusion: Healthcare female managers face several important challenges in decision-making. Appropriate measures should be taken to reduce these challenges to achieve better results.

What determines the fate of organizations today, successes or failures is to achieve an appropriate sustainable business model to create value for key stakeholders. Despite this necessity, few studies have been conducted on sustainable business models in the real world based on a standard model. This study has designed a comprehensive model for the Farassan industrial and manufacturing company, one of the active companies in the pipe industry, by using the soft systems methodology and applying the new business model canvas of value triangular. The current research has two aims first, to identify the key components of the model and second, to design an appropriate model to create value for key stakeholders. From a practical point of view, this research achieves a suitable sustainable business model for the Farassan Company and from a theoretical point of view, it presents a new approach to design sustainable business models. As an applied case study, this research can help organizations and industries in the country to create value for key stakeholders such as shareholders, customers, employees, society and environment and to improve sustainable development.

Developing and maintaining a competitive advantage for companies is a critical topic and business intelligence is one of the key factors towards this goal, in business competitive environment. Present study seeks to investigate the effect of KM capabilities on business intelligence with mediatory role of open innovation. Sample size determined 90 persons by Partial Least Squares technique which chosen randomly. Data collected by questionnaire, which its reliability investigated by Cronbach’ s Alpha and Composite Reliability, and analyzed by SPSS and SmartPLS based on path analysis with partial last squares approach in structural equations modelling to test hypothesis. Testing research hypotheses using path analysis method with partial least squares approach in structural equation modeling shows that between open innovation and business intelligence has a positive and significant relationship. In addition, knowledge management infrastructure capabilities has a positive and significant effect on business intelligence directly and indirectly. Moreover, about one third of the total effect of Knowledge management infrastructure capability on business intelligence is indirectly explained by open innovation.

In this paper, a modified scalarization technique for finding Pareto optimal points of multiobjective optimization problems is provided. The proposed method is a combination of the normal constraint and elastic constraint method. First, we introduce the optimization problem of the modified method and then we present an algorithm for obtaining the set of Pareto points. Thereafter, the relationship between optimal solutions of this scalarization problem and (weakly, properly) efficient solutions of the multiobjective optimization problems are analyzed. Indeed, some necessary conditions for (weak, proper) efficiency are presented. All the provided results are established without any convexity assumption. Furthermore, we propose a new algorithm for approximating the Pareto front of multiobjective optimization problems. We solve some test problems by applying the suggested algorithm and compare the results with some existing methods, including the epsilon-constraint method, the Pascolleti-Serafini method and the NBI method. The obtained results highlight the efficiency of our approach in comparison with examined methods.

In recent years, knowledge management has become a vital issue in all organizations. Knowledge management is one of the effective factors in creating and expanding innovation. With innovation, the long-term advantages of the organization are maintained in the competitive arena. Evaluating and predicting the level of knowledge management for managers is very important. Among the new methods of modeling, fuzzy systems have a special place in different fields of science. The purpose of this research is applied and according to data collection method is survey. The Neural-Adaptive Fuzzy Inference System (ANFIS) is a good method for solving nonlinear problems. This method combines fuzzy inference method and artificial neural network which benefits from both methods. In this study, five main components were selected as inputs for fuzzy inference system to measure and predict the level of knowledge management of the organization. For evaluating the model performance, the parameters of mean square error of error (RMSE), percentage of relative error (ε ), mean absolute error (MAE) and coefficient of explanation (R2) were used. Their values are 0. 12, 0. 0152%, 0. 036 and 0. 995. This indicates the accuracy and reliability of the model. The output of this study is the neural-adaptive fuzzy inference system (ANFIS).