Modern Research in Decision Making

Abstract Iran's current electricity generation systems rely on large-scale power plants situated far from consumers, utilizing fossil fuels as their primary source. However, as demand for electricity surges, greenhouse gas emissions, electricity losses, and reliability issues have become prevalent. Smart cities have emerged as a promising solution to these challenges, with smart grids being a critical component of their energy systems. Efficient energy management necessitates the optimization of resource usage, minimization of costs and environmental risks, and maximization of reliability. This study proposes a multi-objective optimization model for managing a smart grid's energy. The model aims to minimize costs, pollution, and peak consumption while simultaneously maximizing reliability. To account for uncertainties such as renewable energy output, demand, and price, a stochastic approach is utilized. The problem is formulated as a mixed integer linear programming model, and the Pareto solutions are obtained using the epsilon constraint method. In order to achieve the most optimal Pareto solutions, we have employed the use of the fuzzy satisfiability method. Results indicate that the smart energy grid can effectively reduce energy consumption, costs, and pollution while significantly increasing system reliability.

Abstract electricity supply chain plays essential role in providing energy for economic development and social welfare, and knowing the opinions and needs of its beneficiaries is most basic issue in knowing this industry. This recognition allows the electricity supply chain to implement effective improvements and revisions in its processes and technologies in view of facing challenges and changes. This article analyzes the stakeholders of the electricity supply chain, focusing on the principles and concepts of Industry 4.0. In this regard, the soft systems methodology has been used to analyze the stakeholders and various factors of the electricity supply chain according to the Industry 4.0 approach. The mentioned methodology, by defining the targeted activity systems related to the electricity supply chain, creates models to represent the different dimensions of this issue. The article details the methodology used, analyzes the results and its effects on electricity supply chain, and also explains the sustainable, resilient and technological perspectives. In the sustainable perspective, concepts such as energy management, reduction of greenhouse gases, sustainable use of resources and waste management have been investigated. On other hand, in the resilience perspective, flexibility of resources, improvement of the transmission network and exploitation of new energies have been introduced as key factors. In another part of the article, root definitions are presented using the CATWOE analysis method for each of the resilient, technological and sustainable perspectives in the electricity supply chain. Finally, by combining the research literature with the opinions and views of experts of each point of view,

Abstract Various techniques and approaches have been presented to solve multi-objective decision making problems under different assumptions. The STEM technique is also one of the most widely used techniques for dealing with this group of problems, especially for solving multi-objective linear programming (MOLP) models. In the current research, a new approach has been proposed as the development of the mentioned method. In this regard, the second phase of the method is integrated with the simulation process and the concept of utility function has been used in order to determine the probability of selecting targets. For each of the selected targets, several random adjustment rates are defined. Random selection of satisfactory functions based on their utility and using simulation tools, will "create diversity in the selected objectives for adjustment". Also, by considering different and random adjustment rates for selected functions, while facilitating the decision-making process and providing an analytical report to the decision-maker, it will be possible to apply "different levels of satisfaction" of the decision-maker. A three-objective problem with five constraints, is solved using the proposed method and its results are presented. The results indicate that applying the above changes will lead to solving some of the basic limitations of this method that have been mentioned in previous studies. Comparing the proposed technique with the basic method, shows the overall superiority of the proposed method, especially in the criteria related to interaction with the decision maker.

Abstract The construction industry is the one of the biggest sources of pollution which has caused significant harmful effects on the environment.Adopting green supply chain management can be a suitable strategy to control and reduce harmful environmental effects.The aim of the current research is to identify and present a model of effective barriers to the implementation of green supply chain management in the construction industry.In this research, a mixed approach and a purposeful sampling method have been used.To identify the components, a meta-synthesis approach was used and then a semi-structured interview with 14 experts who were managers and contractors active in the construction industry,has been used. In order to analyze the data, was used the hidden content analysis strategy by using MAXQDA software. Finally,the components based on the power of influence and dependence were identified using the MICMAC analysis and the research model was presented with the interpretive-structure method. Based on the findings, the barriers affecting to the implementation of green supply chain management were classified into 16 main factors and into 2 categories of internal and external barriers. Also, the results have shown a ten-level model lack of government support as the most influential variable in the tenth level and the variables: non-cooperation between stakeholders, lack of support from senior and middle managers, desire to maintain current construction methods, lack of green supply chain management measures in the vision and mission of the company are ranked as the most influential variables of this model are placed at the first level.

Abstract Performance-based budgeting (PBB) aims to allocate public budget resources to organizations based on their performance. Consequently, the necessity of evaluating entities' performance in implementing and executing a performance-based budgeting system has spurred some researchers to expound PBB maturity models, while others have concentrated on designing models to gauge its maturity level. In this investigation, mindful of the developed model of performance-based budgeting in Iran, endeavors have been made to fashion a data envelopment analysis (DEA) model in harmony with the conceptual framework of PBB maturity. Considerations such as the network structure of the PBB maturity model, quantitative-qualitative data, and the hierarchical structure of indicators and sub-indicators within the PBB maturity model should be factored into the design of the mathematical model. Consequently, the primary aim of this article is to devise a two-stage-multilayer fuzzy network data envelopment analysis model that encompasses all three of these considerations and delineates the level of budgetary maturity based on performance for various entities. The outcomes indicate that taking into account the uncertainty present in the data and adjusting levels in optimistic, pessimistic, and moderate, while bolstering the discriminative power of the model, will furnish decision-makers with more precise information.

Abstract Digitization of human resource management has become increasingly widespread in modern organizations due to advancements in technology and the need for efficient workforce management. In this context, understanding the factors influencing the decision to DHRM is crucial for organizations that seek to effectively use technology to achieve strategic human resource goals. Therefore, the aim of the research is to find out firstly what are the factors influencing the decision-making towards DHRM and secondly, what are the relationships between these factors. In terms of research orientation, the current research is considered to be a type of applied research, inductive in terms of research approaches, and mixed (qualitative-quantitative) in terms of research strategies. First, in order to identify the factors influencing decision-making for digital human resources management, a semi-structured interview was conducted with 11 experts in this field. Sampling of experts was in the form of snowball. Then, using thematic analysis method, the interviews were analyzed and the factors were obtained. In the second step, the fuzzy cognitive mapping method was used to discover the relationships between the extracted factors. The results showed that these factors are: organizational culture (0.86), resource allocation and investment (0.83), technology infrastructure (0.79), leadership and perspective (0.73), acceptance change and Resistance (0.69), organizational structure and governance (0.62), organizational size and complexity (0.58), employee skills and participation (0.54), data security and privacy (0.48), customization and Personalization (0.45). Also, the relationships of each component with its sub-components were determined and suggestions were made for using the research findings.