Modern Research in Decision Making

Abstract Today, one of the main components of the sustainable development of organizations and among the methods of overcoming economic and technological challenges is innovation. Evaluation of the innovation process is an important issue in knowledge-based companies. One of the innovation evaluation methods is two-stage DEA, which allows considering multiple inputs and outputs and the internal structure without any special assumption regarding the production function. In this research, the theory of cooperative games was used to determine the optimal value of intermediate variables. A non-linear cooperative model for a consecutive two-stage structure with surplus input and output and conditions to the variable scale of development and two calculation procedures of variable first stage efficiency and variable second stage efficiency were proposed to solve it. Considering the two stages of research and development and commercialization for the innovation process, the efficiency of the stages and the total of 9 knowledge-based information and communication technology companies was calculated. The results showed that only one company is efficient in both stages and research and development has a greater contribution to the ineffectiveness of innovation in companies compared to commercialization. The results give important insights to managers to identify the source of inefficiency and prioritize resource allocation.

Abstract The Paris Agreement broadly seeks to prevent increased warming and emissions of greenhouse gases at the ecosystem level. This issue has forced companies to review their structures due to the strict laws of governments and the desire for sustainability from customers. Therefore, this study has been carried out with the aim of designing a sustainable multi-echelon closed loop supply chain to move towards deep decarbonization. An uncertain mixed integer linear programming model is developed to minimize cost, energy consumption and carbon emissions while maximizing job opportunities. Network staff training to improve its social conditions is included in the development of this study's model, which has rarely been considered before. A chance-constrained programming approach is considered to deal with uncertainty, and second-order (NSGA-II) and third-order (NSGA-III) non-dominated sorting genetic algorithms are used to solve the problem. The results show the significant impact of using clean energy and electric trucks on reducing carbon emissions in operational facilities and the transportation sector. The findings also show that an increase in the value of the confidence level in meeting uncertain constraints leads to an increase in the objective functions of cost and environment. The sensitivity analysis of the results shows the significant impact of the returned products in reducing network costs, carbon emissions and conservation of resources and raw materials. The Pareto solution set provides greater flexibility to decision makers in making strategic and operational decisions.

Abstract The spread of environmental pollution caused by factories producing petroleum products has led to the increasing attention of governments to the issue of green supply chain management in this category of industries. In this regard, the main goal of this research is to draw a group decision-making structure that addresses the issue of green supplier selection (GSS) in one of the largest refineries producing petroleum products in Iran. For this purpose, first after conducting detailed studies on the most used decision-making criteria, 8 criteria were selected for final decision-making including price, quality, transportation, energy consumption, environmental management system, green production, carbon emission and waste management. Then, after examining the various types of decision-making methods considering the existing research gap, a new method has been proposed that integrates the CLOUD model based on uncertain linguistic variable with the PROMETHEE method. The innovative CLOUD-PROMETHEE method was investigated in solving the problem of outsourcing paraffin production in this refinery factory and provided significant and useful results. The final results were successfully presented in the form of a preference graph, which provides a very suitable view for evaluating suppliers to company managers. The proposed model has been able to consider the advantages of PROMETHEE method including the possibility of combining quantitative and qualitative data, choosing preference functions, independent weighting and without normalization by solving decision-making uncertainties using the cloud model.

Abstract With the growth of digitalization in industries and the advent of the Fourth Industrial Revolution, organizations have begun transforming their business processes and adopting technologies to become more advanced, customer-centric, and sustainable. Although the introduction of new technologies and the implementation of a circular economy present numerous challenges for organizations, they have proven their value in various industries for achieving sustainability goals. This study aims to identify and prioritize the barriers to the adoption of Industry 4.0 and the circular economy in the home appliance industry. Based on a literature review, ten significant barriers have been identified. For analyzing the collected data, the Interpretive Structural Modeling (ISM) method was used to prioritize the barriers to the adoption of Industry 4.0 and the circular economy, categorizing the factors into five levels of priority. In the structural model, the lack of government support was identified as the most influential and initial barrier, while the lack of motivation and willingness, along with the absence of modular design aspects, were found to be highly susceptible to influence. Furthermore, Structural Equation Modeling was employed to validate the obtained structure. For this purpose, a questionnaire containing 33 questions on a five-point Likert scale was designed, and 170 employees from the domestic home appliance industries were surveyed to complete it. The validity and reliability of the questionnaire were confirmed. The findings of this study can serve as a guiding light for implementing Industry 4.0 and the circular economy in the home appliance industry.

Abstract During infectious disease outbreaks, vaccines are always considered a vital element in safeguarding public health. This study aims to propose an integrated approach to select appropriate strategies for vaccination. The proposed approach is presented in three phases. In the first phase, suitable strategies are identified using the SWOT method, analyzing strengths, weaknesses, opportunities, and threats. In the second phase, the weights of evaluation criteria for strategies are calculated using the Z-numbers theory-based stepwise weight assessment ratio analysis (SWARA) method. Considering reliability alongside uncertainty is one of the advantages of the developed Z-SWARA method. Subsequently, in the third phase and based on the outputs of the first two phases, strategies are prioritized by applying the Z-numbers theory-based measurement of alternatives and ranking according to compromise solution (MARCOS) method, considering uncertainty and reliability. To demonstrate the effectiveness of the proposed approach, it was implemented to select appropriate strategies for vaccination implementation in Iran. Based on the results, central procurement and creating sufficient space, the necessity of providing coordinated Antibody/PCR testing across sectors, and the ongoing increase in research to acquire knowledge related to vaccination, even during the downturn of the pandemic wave, have been identified as superior strategies for vaccination implementation.

Abstract Customer churn presents a significant challenge for the telecommunications industry, necessitating effective strategies for prediction and prevention. While prior research has explored diverse methodologies, including Agent-based Modeling (ABM), limitations persist. Existing approaches often rely heavily on theoretical constructs, resulting in oversimplified models and constrained data utilization. This study addresses a critical research gap: the absence of a comprehensive framework integrating empirical data, agent-based modeling, and machine learning techniques for churn prediction in telecommunication markets. By bridging the gap between theoretical abstraction and empirical reality, proposed framework enables more proactive churn management strategies. Additionally, it facilitates the simulation of diverse market scenarios, empowering stakeholders to optimize key metrics such as revenue and market share. Through the implementation of the proposed framework within a specific telecom market scenario involving two competing entities, this study demonstrates its efficacy in achieving desired market share objectives. This research contributes to advancing the understanding and management of customer churn in the telecommunications industry.