Over the last decade, the total primary energy consumption has increased from 479 × 10¹⁵ BTU in 2010 to 528 × 10¹⁵ BTU in 2020. To address this ever-increasing energy demand, as well as prevent environmental pollution, clean energies are presented as a potential solution. In this regard, evaluating and selecting the most appropriate clean energy solution for a specific area is of particular importance. Therefore, in this study, a comparative analysis in Jiangsu province in China was performed by describing and implementing five prominent multi-criteria decision-making methods in the field of energy technology selection, including SAW, TOPSIS, ELECTRE, VIKOR, and COPRAS. The decision problem here consists of four clean energy options, including solar photovoltaic, wind, nuclear, and biomass, which have been evaluated by twelve basic and important criteria for ranking clean energy options. The obtained results, according to all five MCDM methods, indicate that solar photovoltaic was the optimal option in this study, followed by wind energy. The nuclear and biomass options placed third and fourth, respectively, except in the ELECTRE method ranking, in which both options scored the same and thus neither was superior. Finally, by conducting a comprehensive two-stage sensitivity analysis, in the first stage, it was found that changes in the weights of land use and water consumption criteria had the greatest impact on the performance of options, among which biomass and nuclear showed high sensitivity to variations in criteria weights. In the second stage, by defining five scenarios, the ranking of options was evaluated from different aspects so that the decision maker/organization would be able to make appropriate decisions in different situations.

In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.

The education faculty aims to assess departmental effectiveness by analyzing the relationship between service levels, output variables, and input variables. This objective is coupled with the formulation of faculty development strategies tailored to enhance efficiency while accommodating individual professionals’ unique requirements and aspirations. Furthermore, beyond the annual University Evaluation System, faculty members conduct thorough evaluations to identify discrepancies in departmental reports detailing service levels and input components (factors of production) over a three-year period. Through descriptive statistics and hypothesis testing, reliable data guides the decision-making process regarding the optimal level for data envelopment analysis (DEA). The study employs four simulated scenarios to evaluate the overall performance of five departments using DEA. Findings reveal varying efficacy ratings across departments, with the Department of Health and Physical Education achieving a moderate rating and the Educational Technology Department exhibiting the lowest efficacy. Conversely, departments such as Art Education, Music Education, and Business Education showcase exemplary efficacy levels. Insights gleaned from quantitative analysis and questionnaires contribute significantly to faculty development, enhancing knowledge, competencies, values, attitudes, and future endeavors. Recommendations provided advocate for the widespread implementation of efficacious approaches to bolster faculty efficacy. Moreover, the study underscores the potential to enhance the efficiency and quality of service across faculties through the adoption of best practices and initiatives.

This research paper presents an all-encompassing methodology for multi-criteria decision-making in the apparel sector, with the particular objective of aiding in the determination of the most appropriate location within the CLMV subregion. The research is conducted in three crucial stages. The process began with the administration of a survey to proprietors of garment businesses in both Thailand and the CLMV countries. This survey resulted in the compilation of an exhaustive list of site-selection criteria and sub-criteria. Based on the findings of subject matter-expert interviews, Cambodia (C), Vietnam (V), and Myanmar (M) were identified as feasible alternatives. Subsequently, the questionnaire criteria and sub-criteria were evaluated utilizing the Fuzzy Analytic Network Process (Fuzzy ANP), which involved the utilization of meticulously designed pair-wise comparison matrices and local priorities. Five specialists from the Thai entrepreneurial community affirmed the effectiveness of Fuzzy ANP and expressed interest in expanding manufacturing operations in the CLMV subregion. The optimal location for Thai apparel manufacturers was subsequently determined using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The results indicated that Vietnam is the most favorable option. In order to improve the dependability of results, an amended mixture-design scenario analysis was implemented. This analysis assessed the sensitivity and dependability of the proposed model in different scenarios, ensuring its applicability in real-world situations. In contrast to traditional models, this study integrates managerial judgments and preferences into the decision-making procedure, thereby accounting for the complex interdependencies among numerous criteria. The suggested methodology functions as a beneficial instrument for decision-makers, both domestic and international, as it integrates effortlessly into the organizational structure of the CLMV region. By harmonizing objectives pertaining to data acquisition, manipulation, retention, and dissemination, this framework not only enables enhanced decision-making processes, but also optimizes system efficiency.