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Most of data in a multi attribute decision making (MADM) problem are unstable and changeable, and thus sensitivity analysis can effectively contribute to making proper decisions. Here, we offer a new method for sensitivity analysis of multi-attribute decision making problems so that by changing one element of decision making matrix, we can determine changes in the results of a decision making problem. An analysis is made for simple additive weighting method (SAW) technique, a mostly used multi-attribute decision making techniques, and the corresponding formulas are obtained.

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In traditional data envelopment analysis (DEA) the uncertainty of inputs and outputs is not considered when evaluating the performance of a unit. In other words, effects of uncertainty on optimality and feasibility of models are ignored. This paper introduces a new model for measuring the efficiency of decision making units (DMUs) having interval inputs and outputs. The proposed model is based on interval DEA (IDEA) in which the inputs and outputs are limited to be within uncertainty bounds. In this model, the inputs and outputs take fixed values for each DMU such that the sum of efficiencies is maximized. The DMUs are evaluated by the same production possibility set (PPS). The efficiency is measured based on the proposed conservatism level for each input and output. Indeed, the inputs and outputs are defined by the presented conservatism level. The proposed model is integrated measuring all the DMUs efficiencies simultaneously. These efficiency scores lie between the optimistic and pessimistic cases introduced by Despotis and Similar (2002) [11].

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In third world countries, organizational leaders rarely have figured out to consider happiness and joy of work as a part of the system they are managing. Usually, happiness in organizations is not considered as a management style. Gradually, it became obvious that joy and fun at the workplace will decrease the health care costs, enhances the customers’ loyalty, and increases productivity and profits as a result. Most research on this subject matter relied upon very specific case studies. No research exits dealing with the risks and benefits of Joyful organization. The objectives of this paper are twofold: (1) to utilize hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to determine the most suitable type of Joyful Organization (JO), and (2) to list key risks and benefits of Joyful Organization. This researcher explains the importance of selection criteria for evaluation of Joyful organizations. It provides key elements on JO, Quantitative strategic planning matrix (QSPM), and fuzzy hierarchical TOPSIS methodology. Since QSPM is used with SWOT by many practitioners and researchers in various fields of study, it was selected as a tool for validation purposes. A case study is taken under consideration and results are explained for both approaches.  The finding of this research points to the suitability of semi conventional organization strategy which means implementing about 50% of the rules of main cultural organizations. A sensitivity analysis was performed on TOPSIS using the weights generated by the hierarchical fuzzy TOPSIS approach, Shannon entropy weight, and TOPSIS approach. The ranking results obtained are identical for all these three cases.
 

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Abstract
Urban land allocation, planning and management are a complicate problem challenging the decision makers and policy writers all around the Word. The multi objectivity nature of the problem has engaged researchers to deal with the environmental, ecological, economical, social, recreational, commercial, and residential problems simultaneously, in any region, for better decision making. These modelers neglected to consider people’s satisfaction and wellbeing due to land allocation, planning, and development. Complex problems as such as land allocation and planning are in need of suitable integrated model building for solution and analysis. It was to this end that this author proposes a system dynamics approach for studying the impacts of the decisions made, by the policy makers in the long run, on the community’ satisfaction using computer simulation. Taking one land allocation decision into consideration, the results of our proposed dynamic modeling points to this reality that people’s level of satisfaction improves, their level of incomes enhance, and the quality of their lives increases with the passage of time.
 

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With this research, author presents an understanding of business value that would be enhanced by adopting a new technology into the system. A healthcare center is the case here and the technology considered is radio frequency identification (RFID). To present such framework for evaluation purposes, a two phase analysis is introduced. In the first phase and with the help of a multi attribute decision making in the context of hierarchical fuzzy TOPSIS, an RFID-based system among a set of proposed RFID based-systems are selected. In the second phase, with the help of system dynamics approach, the behaviors of system for goal variables are determined. To fully understand this approach, a sample case is provided and analyzed. This type of integrated decision-making approach can provide a deep understanding of the system because of providing one or more trends on key system variables based upon the optimal decision made at the present time using an MADM tool.  Due to the fact that this research combines four fields of knowledge into an interesting research problem, of highly concerned to the users, it makes a true contribution to health, system dynamics, RFID and MADM. Integration of MADM and SD approaches in healthcare system has some very important benefits for healthcare managers. It allows managers in seeing the system behaviour now under the decision made at the present time using multi attribute decision making approach.

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The biggest issue facing both industrialized and emerging nations these days is traffic congestion, which has changed people's perspectives on public transportation systems and accelerated efforts to make them more efficient. Urban traffic issues are a result of various factors, including population growth and the rise in private vehicle ownership. Urban public transportation is currently one of the primary strategies for reducing urban traffic congestion. Building simulation models allows for a more precise analysis of the basic capacity of buses at transfer stations on a given route, trip frequency, passenger behavior, and waiting times. This study suggests using a digital twin design to plan bus routes, reduce wait times for passengers, maximize bus frequency, and investigate the relationship between overall traffic flow and passengers. The Anylogic package program was utilized, which is a helpful tool for digital twin modeling and multi-method simulation. The usefulness of the digital twin concept—which links the physical and virtual worlds—was highlighted in determining the ideal number of trips and trip intervals as a result of the examinations made with the model's outputs. This allows for the instantaneous monitoring and storing of data in its physical conditions.
 

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As the chain stores grow, crop cooperatives are in danger of being destroyed In this study, strategies for increasing the efficiency of crop cooperatives in one of the northern provinces of Iran are studied. A knapsack problem mathematical model was presented, and then The fuzzy analytic hierarchy process (FAHP) method calculated the desirability (weights) of the identified criteria. Field survey, structured interview,status analysis questionnaire,intra-system strengths and weaknesses, opportunities and threats (SWOT) were evaluated. After analyzing the results of the assessments in the 10 General Productivity Indicator Criteria, 3 Specific Indicator Criteria and 7 Criteria Indicates that there is a deep weakness in the co-operative system and reveals the need for a fundamental change.Therefore, according to the results obtained in this study Criterion (Member Investment -Training-5S-Productivity and Problem Solving-Customer Orientation) made the most investment. The most important point in this issue is limited financial resources .For this reason, considering the utility of the criteria with the expert opinion and the current status of each cooperative in each criterion are two crucial factors. Finally, the strategic goals of the research, using the SWOT matrix analysis, are based on the results of the interviews all cooperatives form of success packages and executive policies.

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This study  investigates the impact of excessive virtual space usage on social laziness within the executive bodies of Sirjan. Utilizing structural modeling to analyze data collected through standardized questionnaires, the study reveals that heavy use of virtual space can lead to an increase in social laziness, which, over time, negatively affects employees' efficiency and productivity. Structural analyses further indicate that this phenomenon can gradually reduce effective participation and interaction among organization members. The paper also offers practical recommendations for managers, such as holding awareness workshops, enhancing time management, and promoting in-person interactions, all of which can help reduce the adverse effects of excessive virtual space usage. This study provides valuable insights into the challenges posed by digital engagement and its consequences for organizations, paving the way for future research in this field.

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Real-world decision makers are faced with different aspects of the world, and each of the types of hard and soft operations research methods have advantages for responding to certain aspects. Based on this, it can be claimed that by using two or more combined methods, more levels and dimensions of a problematic situation can be investigated and the results are definitely more reliable.  The main goal of this research is to solve the problems of electricity shortage caused by cryptocurrency mining in Iran by combining three methods: Strategic choice Approach(SCA), Strategic Option Development and Analysis(SODA), Critical Systems Heuristics(CSH), while examining more detailed levels and dimensions of the problem, solutions or decisions should be adopted to be more reliable and more effective.The combination method is such that SCA is placed as the basis and platform of the research method, In the fourth and eighth steps of sca, soda and csh methods have been used, respectively. The results of this research show wider levels and dimensions of the problems and problems of electricity shortages caused by cryptocurrency mining and lead to the important point that the advantages and benefits of digital currencies should not be ignored due to electricity shortages.
Real-world decision makers are faced with different aspects of the world, and each of the types of hard and soft operations research methods have advantages for responding to certain aspects. Based on this, it can be claimed that by using two or more combined methods, more levels and dimensions of a problematic situation can be investigated and the results are definitely more reliable.  The main goal of this research is to solve the problems of electricity shortage caused by cryptocurrency mining in Iran by combining three methods: Strategic choice Approach(SCA), Strategic Option Development and Analysis(SODA), Critical Systems Heuristics(CSH), while examining more detailed levels and dimensions of the problem, solutions or decisions should be adopted to be more reliable and more effective.The combination method is such that SCA is placed as the basis and platform of the research method, In the fourth and eighth steps of sca, soda and csh methods have been used, respectively. The results of this research show wider levels and dimensions of the problems and problems of electricity shortages caused by cryptocurrency mining and lead to the important point that the advantages and benefits of digital currencies should not be ignored due to electricity shortages.
 

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Leukemia is one of the most common and dangerous types of cancer in the world. In many cases, the disease is curable if detected in its early stages. One of the effective tools for early detection is the analysis of microarray data, which measures the expression of thousands of genes simultaneously. However, the large volume of features and the presence of noise make the analysis process complex and time-consuming. Therefore, the selection of effective genes plays a key role in increasing the accuracy and reducing the computational cost of learning models. In this paper, a two-step hybrid approach is presented for feature selection and classification of leukemia types. In the first step, the features are filtered using the mutual information criterion and the genes with the highest correlation with the disease label are selected. In the second step, the XGBoost model is used to rank and stably select the features to identify the genes that are most important in different iterations. In the final stage, classification will be performed using the temporal fusion transformer method, which allows for fast and efficient learning of complex patterns among selected genes. Experimental results on real microarray datasets show that the proposed method outperforms the baseline methods with an accuracy of 99.2% and has been able to identify key genes effective in differentiating leukemia types by effectively reducing the data dimensions.

 

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Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In classic studies of InvDEA, decision-Making Units (DMUs) as black boxes, ignoring their internal structure. This paper estimates input levels and new intermediate products to achieve a predetermined efficiency score set by the decision maker. In traditional inverse data envelopment analysis models, precise data are required to determine the input and/or output levels of each decision-making unit. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts’ opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an inverse Network DEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts’ belief degrees. To demonstrate the model is performance, we explore efficiency of healthcare systems during COVID-19 pandemic.
 

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Fraud is a phenomenon that involves deviations and manipulations in financial statements. These actions can lead to tax non-compliance and erode the trust of investors and other stakeholders. Given the intricate nature and vast amount of financial data within organizations, leveraging artificial intelligence as a sophisticated tool can greatly enhance fraud detection in financial statements and bolster confidence in the face of evolving fraudulent tactics. Fraud or deception in the financial information of individuals or organizations reduces the level of trust and confidence that people have in the reliability and integrity of this information. This can lead to serious negative impacts, including loss of trust from customers, investors, and other entities, negative financial and legal consequences, and the exposure of illegal or improper operations that may involve financial crimes. This paper introduces an intelligent method for detecting fraud in financial statements. Initially, the Apriori algorithm is utilized to select pertinent features in the financial data. Subsequently, the performance of the proposed method is enhanced by augmenting the dataset using the GAN-CNN network. Finally, fraud detection is executed with the assistance of XGBoost. The results demonstrate that the proposed method has achieved a fraud detection accuracy of 95.3%.