Sub‐Watershed Prioritization Using Advanced Multi‐Criteria Decision‐Making Approaches for Proactive Flood Risk Management

Selection of materials using compromise ranking and outranking methods

In manufacturing firms, there is a critical need for proper maintenance of manufacturing facilities. The maintenance process enhances customer satisfaction and reliability of the products, and increases the profit of the manufacturer. Therefore, a proper maintenance policy selection is a critical issue for manufacturers, as an inefficient maintenance policy affects not only the direct cost of the firm but also the other aspects. In the present study, maintenance policy selection at the level of the firm rather than the equipment level is shown, and for selection various criteria have been identified. The presented work not only provides the best alternatives but also provides an alternative ranking, which facilitates decision-makers in choosing alternatives according to their constraints. These selection criteria are different in nature, as some give a crisp value, whereas others are defined in linguistic terms. To select the appropriate maintenance policy, a distance-based fuzzy multicriteria decision-making (MCDM) approach has been employed. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The MCDM approach is efficient in incorporating data, in the form of linguistic variables, triangular fuzzy numbers, and crisp numbers, into the evaluation process of maintenance policy alternatives. A comprehensive example illustrates the application of the distance-based fuzzy MCDM approach.

Websites are important in every organisation and tremendous effort is made to design websites that not only look and feel good, but are usable and of high quality. Nevertheless, one critical task is how to evaluate these websites to ensure that users are satisfied with its quality and usability. Although a variety of methods and approaches have been proposed, there is currently an increase in research efforts to model website quality and usability evaluation from the point of view of decision-makers which existing methods do not handle. Thus, this has led to the application of multi-criteria decision-making (MCDM) approaches in the evaluation of websites to handle complexity in decision-making. This paper, therefore, provides a review of the various MCDM methods that have been used in the usability and quality evaluation of websites. The search strategy which was adopted identified a total of 63 published articles in peer-reviewed journals and international conferences between 2005 and 2017. From the research questions formulated for the study, the papers were classified into various MCDM approaches, website genre, number and list of criteria used over the years and the localization of websites based on country. Some of the findings showed that the Analytical Hierarchy Process approach integrated with fuzzy logic has been the most common method over the years. In addition, e-commerce websites make up the most common website genre. Besides, currently most active websites are from Turkey and five is the average number of criteria for the evaluation of website quality and usability.

Calibrating a hydrological model in ungauged small river basins of the northeastern Tibetan Plateau based on near-infrared images

Given the ambitious targets of carbon emission reduction set for the very near future, it is now expected that retrofitting operations on existing buildings aim both at reducing their operational energy consumption and at improving their seismic performance. Indeed, it is now well acknowledged that, if a sole energy efficiency upgrade is provided to a given building, in case of an earthquake occurrence, double economic and environmental losses will be experienced due to both the lost investment for energy retrofitting and the repair and retrofitting activities for post-earthquake damage. Moreover, social losses may also be experienced in terms of casualties, injured or homeless due to the seismic and structural deficiencies of the existing structure. To aid thus the process of a coupled seismic/energy renovation of the existing building stock, several multi-criteria decision-making (MCDM) approaches have been developed for the identification of optimal retrofitting solutions for buildings. Such procedures typically consider a range of economic, social, technical, and, more recently, environmental aspects that are assumed to be of interest to decision makers (e.g., installation cost, duration of works, architectural impact, need for specialised workers, etc.). The present study demonstrates the application to a case-study school building of two different MCDM approaches, which account for seismic vulnerability and energy efficiency, as well as related environmental impacts of buildings. The main differences between the two procedures are explored in terms of considered decision-making parameters and corresponding weights, rankings of retrofitting options and identification of the optimal retrofitting strategies.

Choosing the right place to set up temporary hospitals is one of the most important and urgent measures for pandemic response. To solve this problem, we propose a new hybrid approach based on two steps. Firstly, since human health is highly sensitive to the impact of decisions about temporary hospital locations and a mistake may pose severe threat to human lives, we propose a new risk-averse multi-criteria decision-making (MCDM) approach. To choose the alternatives with the fewest possible weaknesses, a comprehensive hierarchal structure of relevant criteria categorized into environmental, social, economic, and infrastructural, along with a new risk-averse best-worst method (BWM), are presented as the two components of the MCDM approach. Secondly, considering the output of risk-averse BWM and the distance between the selected centres for better patient coverage as parameters, a portfolio optimization model is used for the hospital selection locations. Our new approach has been applied to a real case study to select cities in Mazandaran, a northern Iranian province seriously affected by the Coronavirus (COVID-19), as places to build temporary hospitals.

Transportation of petroleum-based products is a complex and hazardous logistics process. Selecting the appropriate transportation vehicles for this process is crucial for ensuring safety and efficiency. Therefore, prioritizing selection criteria and choosing the most suitable fuel tanker is essential in reducing potential risks and improving overall fleet operations. Given the many types of tankers available for petroleum product transportation, decision-makers should employ a multi-criteria decision-making (MCDM) approach to identify the best option for their specific needs. This study introduces a novel MCDM approach that combines the Weighted Aggregated Sum Product Assessment (WASPAS) and the Criteria Importance through Intercriteria Correlation (CRITIC) in a Fermatean fuzzy (FF) environment, addressing five main criteria and evaluating five fuel tanker alternatives. Using this approach, the research identified and prioritized key criteria for selecting an appropriate tanker model by providing insights into 24 sub-criteria. The results reveal that tanker price emerges as the most important sub-criterion, leading us to determine the M3 tanker model as the most suitable fuel tanker after carefully evaluating all 24 different sub-criteria. The findings can significantly assist petroleum firms in making informed decisions when choosing fuel tankers by considering various technical, operational, comfort, environmental, and economic factors. The results can also be used to inform policy on fuel distribution.

The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process.

A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets

Selecting suitable suppliers is a critical decision for organizations, and multi-criteria group decision-making (MCGDM) has been proven as an effective technique in selecting the most appropriate supplier. However, most existing supplier selection methods do not fully explore the trust relationships among decision-makers (DMs), fail to effectively tackle complex and uncertain information, and neglect the interdependencies among criteria. To overcome these obstacles, we develop a social trust network (STN) based hybrid supplier selection approach that adopts the best-worst method (BWM) and the preference ranking organization method for enrichment evaluation (PROMETHEE) under interval-valued Pythagorean fuzzy (IVPF) environment. The main contributions are as follows: Firstly, a novel STN is constructed and extended to the IVPF setting to fully capture both direct and indirect trust relationships among DMs. Secondly, a new trust propagation operator is designed to determine DMs’ weights more objectively. Thirdly, the BWM method, known for its high consistency and efficiency, is adapted to the IVPF environment to derive optimal criterion weights and the preference relation-based PROMETHEE method is also integrated to rank the candidate suppliers. Fourthly, the IVPF weighted Hamy mean (IVPFWHM) operator is employed to aggregate DMs’ preferences and evaluations while explicitly capturing the interrelationships among criteria. The approach is practically applied to a real-world decarbonized and sustainable supplier selection case to validate the efficacy in complex and uncertain group decision-making scenarios while incorporating social trust dynamics. The sensitivity and comparison analyses are also conducted to validate the robustness and benefits of the method.

Selection of cotton fibers in terms of their quality value has created a domain of emerging interest among the researchers. In this study, a newly developed Best-Worst Method (BWM) was integrated with Revised Analytic Hierarchy Process (RAHP) to rank cotton fiber lots on the basis of six apposite fiber properties namely fiber bundle tenacity, elongation, micronaire, upper half mean length, uniformity index, and short fiber index. Ranking performance of this integrated approach closely resembles those of the other multi-criteria decision-making (MCDM) approaches. No occurrence of rank reversal during the sensitivity analyses corroborates the stability and robustness of the BWM-RAHP method. Uniqueness of the present study lies in the fact that this is the maiden application of the vector-based BWM approach, that uses fewer pairwise comparisons than other variants of MCDM, in a cotton fiber grading problem. The RAHP adds value to the decision model by overcoming the problem of ranking inconsistency. Rank correlations between the ranking based on quality value of cotton and those based on yarn tenacity are also encouraging, and further bolster the efficacy of the BWM-RAHP method.

By considering the growing demand for energy supply and increment in fossil fuel consumption which are accelerated by population growth, attentions have been focused on Renewable Energy Sources (RES). The corresponding environmental corollaries are another motivation to further follow this aim. Solar energy is willingly assented to be a reliable source in this regard. There are well-cited researches investigating about enhancing the efficiency of RESs particularly solar energy. Geographical and climatic conditions are crucial factors in designing a promising solar project. There are diverse approaches to optimize the energy planning systems. Multi-Criteria Decision Making (MCDM) analysis is a well-known approach employed for assessing the energy systems so as to find the most optimum case. There are several attempts applying different types of MCDM analysis in the pertinent literature. In this study, it has been managed to find the best approach for priority analysis of solar projects. Three major types of MCDM approaches have been evaluated including VIKOR, SAW and TOPSIS. The evaluation has been conducted in Iran as a high potential country in solar irradiation. In order to find the best approach, twenty five locations have been selected in deferent parts of the country with diverse climatic conditions. Furthermore, the considered criteria are technical, economic and environment ones which have been weighted through Entropy-Shannon methodology. The Methods have been evaluated by comparing with Data Envelopment Analysis result and applying a sensitivity analysis to the considered weights. It has been concluded that MCDM approaches are reliable methods for priority analysis and SAW is performing better comparing with other methods.

Best-Worst Method (BWM) is a structured approach for eliciting criteria weights in multi-criteria decision-making. This review paper revisits the original BWM formulation and procedural logic, and discusses behavioral motivations for dual anchoring and structured elicitation. It then synthesizes major methodological developments, covering linear and multiplicative formulations, interaction modeling extensions (nonadditive models), Bayesian formulations, group-aggregation models, tradeoff-based elicitation, parsimonious and disaggregation-based variants, sorting extensions, and fuzzy and belief-based treatments of imprecision and epistemic uncertainty. Because the reliability of inferred weights depends on judgment quality, the paper consolidates and compares consistency checking approaches and discusses their implications for practice. Representative application domains are reviewed to illustrate how BWM is deployed in practice. Finally, future research directions are outlined, emphasizing behavioral validation, integration into complete decision pipelines, scalable elicitation support, and cautious human-AI co-production that preserves problem-specific preference meaning. These directions also include transferring BWM’s anchored elicitation principle to other preference elicitation approaches, including MACBETH, UTA, and conjoint analysis. • Revisits BWM’s dual-anchored elicitation and behavioral rationale. • Synthesizes BWM extensions: linear, multiplicative, Bayesian and interaction models. • Compares output- and input-based consistency ratios for BWM judgments. • Maps representative application domains and growth of BWM research over 2015–2025. • Sets agenda: behavioral tests, full pipelines, scalable elicitation and human-AI use.

Accident-prone road section (APRS) treatment is the most effective strategy for accident reduction. Because use of multicriteria decision making (MCDM) in the decision process improves the quality of the decision, especially in problems involving multiple criteria, MCDM approach may be the appropriate approach for APRS determination. In this study, an MCDM-based methodology was developed for the determination of APRSs. Weighted linear combination method was used to combine criteria. Because use of experts' opinion for criteria weight determination in MCDM studies may bring subjective results, all possible criteria weight combinations (with some simplifications) were tried to determine the best weight combination. “Relative risk” was used as common unit for criteria standardization and past accident data is utilized to express the “relative risk”. The methodology was tested on D850 State Highway, Tokat, Turkey. Five criteria (horizontal alignment, vertical alignment, intersections, significant places, shoulder width) were used in the process. At the end, although 63 out of 94 sections (67.02%) were labeled as “accident prone”, 173 out of 206 accidents (83.98%) take place in these sections. Therefore, the methodology can be used for APRS determination. Also, the effect of any change in the road properties on the accident-proneness can be simulated by simply changing the criterion values.

Exploring the green waste management problem in food supply chains: A circular economy context