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The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability.

The goal of this study is to present a DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance. The study demonstrates a real-life use of the proposed model, mainly designed for hospitals. Data envelopment analysis enhanced with fuzzy analytic hierarchy process are collectively utilized to quantify the data and structure the model in decision-making. The juxtaposition of the two methods is used to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid model provides several benefits, one of which is the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model.

Global warming, climate change, and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution, greenhouse effect, poor working conditions, child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system, actor, objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the inter-relationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance, reciprocity, fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance, social performance and channel efficiency of the supply chain, thereby ensuring sustainability and socio welfare of all the supply chain.

PurposeMegaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.Design/methodology/approachThe study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.FindingsIt was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.Originality/valueThe contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

For the past four decades, the methodology of fuzzy analytic hierarchy process based on fuzzy trapezoidal or triangular numbers with the linear type of membership functions has witnessed an expanding development with applicability to a wide variety of areas, such as industry, environment, education, government, economics, engineering, health, and smart city leadership. On the other hand, the interval gray analytic hierarchy process is a more practical method when a significant number of professionals have large variations in preferences and interests in complex decisions. The paper examines the management of architectural heritage in smart cities, using methods of multi-criteria decision making. Two appropriate methods generally recommended by the scientific literature have been applied: fuzzy and interval grey analytic hierarchy process. By using both techniques, there is an opportunity to analyze the consensual results from the aspect of two different stakeholder groups: architectural heritage experts and smart city development experts. Trapezoidal fuzzy analytical hierarchical process shows better stability than a triangular one. Both approaches assign priority to the strategy, but the interval approach gives a more significant rank to architectural heritage factors. The similarity of the proposed methods has been tested, and the similarity factor in the ranking indicates a high degree of similarity in comparing the reference rankings.

Flash flood is the most recurrent natural threat in northeastern India, especially during the peak of the monsoon season. In recent decades, the frequency of flash floods has considerably increased in the Himalayan watersheds, causing enormous human suffering, infrastructure damage, ecosystem disruption, and economic losses. Considering the exponential rise in the frequency of flash flood events, identifying the Flash Flood Vulnerable Zones (FFVZs) is one of the most crucial findings to limit their negative consequences. In this study, grey fuzzy analytic hierarchy process-based model integrated with the geographic information system is implemented to assess the flash flood vulnerability in the Ranikhola watershed, East Sikkim, India. Additionally, a novel Flash Flood Vulnerability Index (FFVI) is proposed to determine the flash flood vulnerability that takes into account twelve natural and anthropogenic parameters. Further, the FFVI map is classified into three FFVZs: low, moderate, and high. The effectiveness of the methodology implemented is judged by the high EV value (6.4286) and the comparison parameter. The harshest conclusion of this analysis, the major cities, such as Gangtok, Ranipool, and Singtam, are designated high FFVZ. The sensitivity analysis results show the LULC has the maximum positive influence on high FFVZ.

Lao PDR faces seasonal power supply problems due to its heavy reliance on hydropower. Thus, the aim of this paper was to prioritize renewable energy (RE) resources for sustainable electricity generation in Lao PDR using the analytic hierarchy process (AHP) method, and to further estimate the energy available for the prioritized RE to enhance the seasonal power supply. Four RE alternatives were assessed considering technological, economic, environmental, and social criteria with twelve overall sub-criteria. The results indicated that hydropower was the most highly prioritized alternative, followed by solar. The resulting weights of the RE prioritization were in agreement with the Lao energy policy and plan. In order to address the seasonal power supply problem, setting-up floating photovoltaic (FPV) units in the existing hydropower reservoirs was proposed. The FPV potential was estimated, and the results revealed that the predicted power demand by 2030, as calculated in the latest Lao national power development strategy, could be fully covered by integrating the FPV output from 10% coverage of the water surface in four existing hydropower reservoirs with the existing power supply in 2020. The proposed FPV technology would provide a solution to enhance the seasonal power supply and reduce the power import.

In the current study, two widely used multi-­criteria decision­-making methods, the Fuzzy analytic hierarchy process (AHP) and the Fuzzy VIKOR method, have been implemented to prioritize the criteria of a multi­-criteria decision­-making problem. Herein, the case study is an autonomous vehicle, the TalTech iseAuto AV shuttle, developed at TalTech University. The criteria of the present problem are evaluated by experts, and after forming the pairwise matrices, these matrices are aggregated by the max­-min method with the arithmetic mean. Subsequently, in the case of Fuzzy AHP, by calculating the weights and normalizing them, the relative importance of each criterion is obtained, which leads to the ranking of the criteria. Moreover, in the case of the Fuzzy VIKOR method, the aggregated pairwise matrix is weighted and normalized. The ranking obtained from both methods is presented and compared. The advantages and disadvantages of the multi­-criteria decision­making methods Fuzzy AHP and VIKOR, featured for risk analysis of the autonomous vehicle systems, are discussed.

The use of fossil fuels has caused many environmental issues, including greenhouse gas emissions and associated climate change. Several studies have focused on mitigating this problem. One dynamic direction for emerging sources of future renewable energy is the use of hydrogen energy. In this research, we evaluate the sourcing decision for a hydrogen supply chain in the context of a case study in Thailand using group decision making analysis for policy implications. We use an integrative multi-criteria decision analysis (MCDA) tool which includes an analytic hierarchy process (AHP), fuzzy AHP (FAHP), and data envelopment analysis (DEA) to analyze weighted criteria and sourcing alternatives using data collected from a group of selected experts. A list of criteria related to sustainability paradigms and sourcing decisions for possible use of hydrogen energy, including natural gas, coal, biomass, and water, are evaluated. Our results reveal that political acceptance is considered the most important criterion with a global weight of 0.514 in the context of Thailand. Additionally, natural gas is found to be the foreseeable source for hydrogen production in Thailand with a global weight of 0.313. We also note that the analysis is based on specific data inputs and that an alternative with a lower score does not imply that the source is not worth exploring.

The main aim of this research was to quantify the parameters related to forest situation (according to DPSIR framework) using decision-making processes and fuzzy methods in the Zagros forests of Iran. In this study, the situation factors (e.g., socioeconomic, biophysical, and environmental factors) were evaluated by fuzzy analytic hierarchy process (FAHP) using α -cuts in addition to the Chang method for fuzzy pairwise comparisons. The results of the study clearly illustrate that the decision-making process is the most important input in forest management planning in the Zagros forests, Iran. In such situations, decision-making techniques can be of great help in differentiating the factors influencing decision-making and policy-making for these forests. We found that α -cuts could improve the quality of the decision-making process, but only after secondary analysis. Initially, we did not find any significant difference in the results between α -cuts and research results, but using the differences in rankings, we could identify a significant difference. We propose that this method, which requires lengthy calculations to get the answer, should only be used by forest managers when the quality of the results and the difference between the parameters are very important to them; otherwise, they may be able to achieve the same desired results in a much easier way.