A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM)

Multi-Criteria Decision Analysis (MCDA) for sustainability assessment in food sector. A systematic literature review on methods, indicators and tools

In the last decades, several studies have highlighted the significant impacts of the food sector. Therefore, enhancing sustainability within this sector has become of paramount importance. A crucial step towards achieving this goal involves the definition and implementation of effective sustainability metric and measurements. In this regard, the adoption of multi-criteria decision analysis (MCDA) methods can be seen as one of the most suitable and promising approach to comprehensively capture the complex and broad-ranging effects of agricultural practices and food supply chains. In such context, a systematic review of the scientific literature on multi-criteria approaches and tools for measuring the sustainability of food supply chains (harvest and post-harvest stages) has been carried out, resulting in the selection and analysis of 42 articles. To delve into the selected articles, three main areas of focus have been identified. The first about MCDA methods and their features, revealing the most adopted methods for sustainability assessments of food supply chains. The second, focusing on the participatory approach, led to the definition of a stakeholder's engagement map, highlighting the typology of stakeholders involved, the reasons of their involvement and engagement methods. Lastly, the third focus is related to the analysis and classification of indicators adopted in each study and the sustainability dimensions to which they refer to. The results of the present review study provide a comprehensive overview of the essential aspects to be considered when developing a MCDA for sustainability assessment in the food sector, serving as a valuable resource for both scholars and practitioners.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts' fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Evaluating Malnutrition Practices and Mother's Education on Children Failure to Thrive Symptoms Using Entropy-Weight and TOPSIS Method

BACKGROUND/OBJECTIVES

Failure to thrive (FTT) is mostly caused by insufficient consumption of nutrient-rich food, recurrent infections like diarrhea and intestinal worms, substandard caregiving practices, and limited availability of health and other vital services. Furthermore, there was a correlation between the educational level of mothers and the occurrence of FTT in children aged 6-12 months. Thus, the objective of the current research is twofold: (i) to investigate other factors related to FTT and (ii) to evaluate the impact of them on FTT in Sub-Saharan African countries and their urban areas.

METHODS

We used weight entropy and TOPSIS methods to approach the research question. In particular, the entropy-weight method is effective for precisely evaluating the relative significance of the selected criteria for TOPSIS computation. Thus, data were retrieved from the database of UNICEF for the year 2019 for nine Sub-Saharan countries, and based on the methods used, five criteria have been selected for consideration. Those of mothers in higher education were identified as having a higher weight, which means that this can affect positively the ability of mothers to mitigate the situation of FTT and protect their children.

RESULTS

The findings of the study highlight the factors of maternal education at a higher level and unhealthy habits as those with the greatest weight and impact on the FTT. Moreover, the results indicate that the association between maternal education, and especially higher education, and FTT is stronger in Ethiopia. Despite the limited amount of research on the specified relationship in Sub-Saharan countries, this study is among the initial ones to examine it.

CONCLUSIONS

The current study can aid policymakers in devising appropriate policies and implementing effective measures to tackle FTT in Sub-Saharan Africa, like enhancing the number of mothers in these countries to be integrated into the educational system to help both themselves and their children mitigate or avoid the symptoms of FTT.

Exploring the barriers to implement industrial symbiosis in the apparel manufacturing industry: Implications for sustainable development

Industrial symbiosis, a promising approach for sustainable industrial practices, has garnered attention in recent days for its ability to enhance resource efficiency, minimize waste, and preserve the environment through collaborative exchanges among industries. In emerging economies like Bangladesh, integrating industrial symbiosis in the manufacturing industries offers the potential to balance economic growth with environmental sustainability. However, this integration encounters various barriers that complicate the implementation. Despite research on industrial symbiosis in robust economies, studies on emerging and developed economies are still scarce. To date, no research has yet investigated the barriers hindering the performance of industrial symbiosis in the Bangladeshi apparel manufacturing sector. To address this gap, this study integrates the Bayes theorem and the Best-Worst Method to identify and prioritize barriers to the Bangladeshi apparel manufacturing sector. From extensive literature reviews and expert validation, 17 barriers were identified. Findings reveal the "lack of technology and infrastructure readiness" as the most significant barrier, followed by "lack of inter-company cooperation" and "lack of management support". Conquering these barriers empowers emerging economies to fortify the apparel manufacturing sector's resilience, resource efficiency, and environmental performance while fostering sustainable development via circular economy practices. This study is expected to guide policymakers and stakeholders in crafting targeted strategies for promoting steady growth and sustainable development in the apparel manufacturing sector of emerging economies like Bangladesh.

Multi-dichotomous sequencing method MDSM for ordering the importance of variants' properties in multi-criteria decision-making

There are plenty of Multi-Criteria Decision-Making (MCDM) methods that help to choose the most suitable solution assessed by several criteria (e.g. Saaty 1990; Simos 1990; Pamučar et al. 2018). They are applied in cases where several scales of different units describe the variants or the variants' properties are represented by linguistic, non-numbered terms. The inherent part of the MCDM algorithms is calculating the weights of the variants' properties, necessary for ordering the variants. If - in a certain problem - there are several properties to consider, sequencing their importance becomes a problem itself. The innovative method of sequencing is proposed in the article based on dichotomous splitting of the properties' importance. If made several times, it leads to the coherent - internally and with the decision-maker's intention - order of the properties' importance. Then the weights of the properties can be calculated with the use of different MCDM methods. The description of the method can be shortened as follows:•Divide the full set of features into two dichotomous subsets of lower and higher importance•Continue dichotomous divisions until there are only the subsets containing one element or subsets containing elements of equal importance.

Setting a shared development agenda: prioritizing the sustainable development goals in the Dominican Republic with fuzzy-LMAW

The sustainable development goals (SDGs) were established by the United Nations as an international call to eradicate poverty, safeguard the environment, and guarantee that everyone lives in peace and prosperity by 2030. The SDGs aim to balance growth and sustainability in three dimensions: social, economic and environmental. However, in the post-pandemic era, when resources for public development policies are scarce, nations face the problem of prioritizing which SDGs to pursue. A lack of agreement is one of the determinants of low performance levels of the SDGs, and multicriteria decision analysis tools can help in this task, which is especially relevant in developing countries that are falling behind in achieving the SDGs. To test the feasibility and appropriateness of one of these tools, the Fuzzy Logarithm Methodology of Additive Weights, we apply it to prioritize the SDGs in the Dominican Republic, to see if the priorities established are consistent. Seventeen experts were surveyed, and the main result was that Decent work and economic growth was the most important goal for the country. Our findings, consistent with the literature, show the path to similar applications in other developing countries to enhance performance levels in the achievement of the SDGs.

Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

A decision support system for localized planning of reclaimed water around wastewater treatment plants

Exploding population, industrialization, and an increase in water pollution has led to acute shrinkage in freshwater availability. Numerous countries have started exploring municipal wastewater as a new potential source of water to bring a paradigm shift from linearity to obtaining circularity in human water cycle management. This study aims to develop a decision support system for integrated water and wastewater management (DSS_IWWM), targeted towards reuse-focused selection of appropriate wastewater treatment technology, and localized planning around STPs in terms of reclaimed water demand identification, estimation, allocation, and sustainable pricing. The developed DSS_IWWM comprises of a repository of fourteen reuse purposes, reuse quality criteria, and 25 wastewater treatment technologies (WWTTs) in 360 combinations. It is sensitive to local resource scenarios and applies a socioeconomic and technology-focused methodology for addressing the interests of the community and investing agencies and viably. To validate the application of the DSS_IWWM, it is first tested with data from three cities in the state of Uttar Pradesh (India)-Lucknow, Prayagraj, and Agra-and then extended to nine more Indian cities with varying influent quality characteristics, resource inputs, existing STP technologies, and same target quality and decision criteria prioritization, to present a comparison of appropriate WWTTs and associated average prices obtained in different scenarios. It is concluded that influent quality, existing technology, and target quality criteria play significant role in selection of appropriate WWTTs. The traditional technologies such as UASB and ASP are required to be augmented and supplemented with high-performing WWTTs, such as BIOFOR-F with (C + F + RSF) and SBT + WP to obtain desired effluent quality. High-performing advanced oxidation process (AOP)-based systems such as A2O, SBR, and BIOFOR-F require WWTTs with relatively lower average costs (such as SBT and OP). The developed DSS_IWWM may prove to be very useful and beneficial for policymakers, government officials, engineers, and scientific community as it will facilitate rational decision-making for efficient investment planning in reuse focused wastewater treatment towards achieving circular economy in sustainable water resource management.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Functional recovery prediction during rehabilitation after rotator cuff tears by decision support system

BACKGROUND

Today's rehabilitation decision-making still relies on conventional methods and different specific targeted rehabilitation protocols. Our study focuses on the decision support system for early rehabilitation after rotator cuff (RC) tears repair, where a multicriteria decision-making framework (MCDM) is applied for the prediction of successful functional recovery and selection of a rehabilitation protocol.

OBJECTIVE

To identify factors that affect recovery outcomes and to develop a decision support system methodology for predicting functional recovery outcomes at early rehabilitation after RC repair.

METHODS

Twelve rehabilitation experts were involved in the design, calibration, and evaluation of a rehabilitation protocol based on the proposed decision support system constructed using the MCDM framework. For the development of a decision support system, 20 patients after RC surgery undergoing outpatient rehabilitation were enrolled in a prospective cohort clinical trial.

RESULTS

The MCDM framework (SWARA method) sensitively assesses different criteria and determines the corresponding criteria weights that were similar to criteria weights assessed subjectively by rehabilitation experts. The assignment of patients into the classes, according to the heuristic evaluation method based on expert opinion and the standard qualitative evaluation methods showed the validity of MCDM methods remain the best new alternative in predicting recovery during rehabilitation.

CONCLUSIONS

The results of this paper show that sustainable rehabilitation is an area that is quite suitable for the use of MCDM. The most of rehabilitation protocols are based on traditional methods and approaches, but the sensitive results showed the validity of MCDM methods and remains the best new alternative in prediction recovery protocols during rehabilitation.

Optimal selection of healthcare waste treatment devices using fuzzy-rough approach

The escalating volume of healthcare waste (HCW) generated by healthcare facilities poses a pressing challenge for all nations. Adequate management and disposal of this waste are imperative to mitigate its adverse impact on human lives, wildlife, and the environment. Addressing this issue in Bosnia and Herzegovina involves the establishment of a regional center dedicated to HCW management. In practice, there are various treatments available for HCW management. Therefore, it is necessary to determine the priority for procuring different treatments during the formation of this center. To assess these treatment devices, expert decision-making employed the fuzzy-rough approach. By leveraging extended sustainability criteria, experts initially evaluated the significance of these criteria and subsequently assessed the devices for HCW treatment. Employing the fuzzy-rough LMAW (Logarithm Methodology of Additive Weights), the study determined the importance of criteria, highlighting "Air emissions" and "Annual usage costs" as the most critical factors. Utilizing the fuzzy-rough CoCoSo (the Combined Compromise Solution) method, six devices employing incineration or sterilization for HCW treatment were ranked. The findings indicated that the "Rotary kiln" and "Steam disinfection" emerged as the most favorable devices for HCW treatment based on this research. This conclusion was validated through comparative and sensitivity analyses. This research contributes by proposing a solution to address Bosnia and Herzegovina's HCW challenge through the establishment of a regional center dedicated to HCW management.

An identification of optimal waste disposal method for dumpsite remediation using the Fermatean fuzzy multi-criteria decision-making method

Improperly managed wastes that have been dumped in landfills over the years pose various challenges, but they also offer potential benefits. The feasibility of recycling such waste depends on the type of wastes, the condition of dumpsites, and the technology implemented for disposal. The selection of an alternative waste disposal method from the many available options for dumpsite remediation is a complex decision-making process among experts. The primary aim of this study is to assist in an extended multi-criteria decision-making (MCDM) method to reduce complexity in the proposed dumpsite remediation problem influenced by multiple criteria and to identify the optimal waste disposal method. Data uncertainties are managed with the proposed Fermatean fuzzy preference scale, and the importance of all socio-economic criteria is assessed using the full consistency method (FUCOM). The final ranking results of the weighted aggregated sum product assessment (WASPAS) method identify that the Waste-to-Energy (WtE) process could play a significant role in the disposal of land-filled unprocessed wastes, promoting sustainable waste management. Meanwhile, the methodology explores the idea that financial and logistical constraints may limit the feasibility of large-scale recycling efforts. This combination of environmental science and decision science addresses real-world challenges, helping municipal solid waste management authorities implement sustainable waste management practices.

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review

BACKGROUND

Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques.

OBJECTIVE

The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field.

METHODS

The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined.

RESULTS

The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications).

CONCLUSIONS

Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

Research on the location decision-making method of emergency medical facilities based on WSR

The need for emergency medical services increased drastically during disaster relief. Poor location selection of emergency medical facilities may harm the interests of healthcare workers and patients, leading to unnecessary waste of costs. It involves multiple stakeholders' interests, a typical multi-criteria decision-making problem. Based on multiple-criteria decision-making technology, most current location selection decisions methods comprehensively consider the evaluation criteria of "issue" and "problem" simultaneously and establish mathematical models to achieve the results. Such methods are difficult to take into account the influence of different attribute factors on the final location selection results in practice. Therefore, in this study, we used the WSR methodology as a guide to divide the factors of location selection into "Wuli", "Shili" and "Renli", and proposed the WSR methodology-based multi-criteria decision-making (MCDM) framework for selecting the appropriate location for emergency medical facilities. The integrated framework consists of the Entropy Weight Method, Best-Worst Method, and interval type-2 fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. Combined with the comparative analysis of actual cases, the results under the guidance of this framework were consistent with practicalities. Also, the sensitivity analysis showed that the location selection ranking fluctuations were not apparent with the fluctuation of criteria weights. Wherefore, the validation of the proposed method's effectiveness, feasibility, and robustness was proved, which provided a valuable reference for the location selection of emergency medical facilities.

Novel semi-quantitative risk model based on AHP: A case study of US driving risks

Road safety is a priority, worldwide. The European Commission aims to reduce fatalities by 2030. The same goal was set for the US. These goals stem from the World Health Organization's (WHO's) broader global context, which has distinctly emphasized a substantial reduction in road traffic injuries. Although different risk factors were observed in different geographical locations, the major risk factors for all locations were similar. They involve influencing human behavior, such as speeding or driving. Several methods have been used to better understand and extract risk factors. However, the complexity of road traffic implies the need for a multi-criteria method. As a result, the analytical hierarchy process (AHP) has emerged as a potential method for this type of risk. The AHP is commonly associated with the use of qualitative methods such as surveys. We propose a novel semi-quantitative multi-criteria risk model (SMCRisk) based on the AHP, deployed in a quantitative and partially qualitative manner by adding a severity factor. The multi-level framework differentiates between the driver's behavior and the driver's state. Our method results correspond to a real situation and confirm that driver behavior and state are major risk factors. In future, this method will lay the foundation for integrating a fully quantitative method by considering the potential use of data originating directly from the IoT, which is a part of our research on holistic risk assessment.

Analysis of the fire risks and mitigation approaches in the apparel manufacturing industry: Implications toward operational safety and sustainability

Fire risks pose a substantial threat to the apparel manufacturing industry since they can lead to immense property damage, potential loss of life, disruption of business operations, and reputational damage. In an emerging economy like Bangladesh, fire-related hazards are crucial due to the numerous deadly industrial fire incidents in recent years. This research, thereby, proposes an integrated multi-criteria decision-making (MCDM) framework to identify and mitigate fire risk hazards in the apparel manufacturing industry. Initially, the study identified 30 significant fire risk factors from the literature review. Then, after expert validation, an integrated Best Worst Method (BWM) and Weighted Sum Model (WSM) framework was utilized to prioritize the fire risk factors. Twenty-three mitigation actions were proposed afterward for the top-ranked risk factors based on National Fire Protection Association (NFPA) codes. An Interpretive Structural Modeling (ISM) with a Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was later used to explore the interrelationships and dependencies among the mitigation actions. The ranking obtained from the BWM-WSM assessment revealed 'combustible storage unseparated by fire-rated construction,' 'non-standard inspection, testing, and maintenance', and 'inadequate means of egress for the occupant load' as the three most critical fire risk factors. The ISM-MICMAC analysis revealed 'fire-rated construction' and 'standardized detection and protection' as the most-driving mitigation actions. The study outcomes are expected to aid the managers and policymakers in emerging economies in formulating sustainable fire risk management strategies for the apparel industry and thus improve the operational safety and resilience of the sector.

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

Enhancement of resilience and quality of cold supply chain under the disruptions caused by COVID-19: A case of a developing country

Cold supply chain (CSC) comprises temperature-sensitive processes, starting from the supply of raw materials, manufacturing, and finally the delivery of finished goods to the end consumers via transport services. Pandemics such as COVID-19 pose threats to its overall functioning and to cater to this issue, the study will ensure the sustainable functioning of CSC by recommending resilience strategies. To do so, the COVID-19 disruptions in the CSC and the resilient sustainability strategies were collected via a vigorous literature review and were analyzed via a Fuzzy QFD technique. The results concluded “crisis simulation,” “identification and securing of logistics,” and “digitalization of cold supply chain” as the top three strategies to ensure the resilience of CSC under disruptions caused by COVID-19. The study recommends necessary steps to the policymakers to ensure a resilient and quality effective CSC. The application of the study proves to be the first of its kind in a developing country such as Pakistan.

JEL Classification: C54, D81, H12

Assessment of sustainable waste management alternatives using the extensions of the base criterion method and combined compromise solution based on the fuzzy Z-numbers

A number of factors have contributed to the increase in waste production and diversity of waste, such as the increase in population, alterations in consumption patterns, economic development, income changes, urbanization, and industrialization. The production of different types of waste, such as electronic, urban, hospital, and industrial waste, makes it necessary to classify waste accurately and recognize effective criteria for waste management. To design and operate waste management systems, it is necessary to understand the sources and types of waste, as well as information about their composition and rate of production. As a result, this study aims to rank 21 types of waste according to Iran's economic, social, and environmental criteria, as well as 13 sub-criteria related to those criteria. For this aim, proposed a novel decision-making approach based on the extension of the base criterion method (BCM) and combined compromise solution (CoCoSo) methods under fuzzy Z-numbers. Additionally, sensitivity analysis and comprehensive analysis are conducted on the results of the criteria and alternatives of sustainable waste management. Based on the results of this study, direct profit and reduced landfill are the most important criteria for assessing sustainable waste management alternatives. According to the results of this study, the sub-alternative of industrial metal waste is the most important waste management option. Examining the next sub-alternative ranks under sustainable waste management options (mobile, communication equipment, and battery) shows that electronic waste requires more attention for recycling and sustainable waste management.

Multi-Criteria Analysis of Startup Investment Alternatives Using the Hierarchy Method

In this paper, we discuss the use of multi-criteria analysis for investment alternatives as a rational, transparent, and systematic approach that reveals the decision-making process during a study of influences and relationships in complex organizational systems. It is shown that this approach considers not only quantitative but also qualitative influences, statistical and individual properties of the object, and expert objective evaluation. We define the criteria for evaluating startup investment prerogatives, which are organized in thematic clusters (types of potential). To compare the investment alternatives, Saaty's hierarchy method is used. As an example, the analysis of three startups is carried out based on the phase mechanism and Saaty's analytic hierarchy process to identify investment appeal of startups according to their specific features. As a result, it is possible to diversify the risks of an investor through the allocation of resources between several projects, in accordance with the received vector of global priorities.

Performance evaluation model of transportation infrastructure: Perspective of COVID-19

The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.

Solar PV power plant site selection using a GIS-based non-linear multi-criteria optimization technique

The ongoing rise in energy consumption imposed serious environmental challenges by using fossil fuels. The use of renewable energy sources is being increasingly explored as a potential answer for achieving sustainable energy production and minimizing adverse environmental effects. In the modern day, photovoltaic (PV) systems are viewed as a possible replacement for fossil fuels as a clean energy source. The installation of solar PV power plants requires vast land and huge investment. Therefore, it is necessary to select a suitable site to achieve maximum efficiency and low cost. A feasible location of photovoltaic (PV) system must consider certain criteria including land restrictions, access to roads, and transmission lines. This study analyzed ten factors grouped into four categories: geographic, technical, economic, and flood susceptibility criterion. The data of each factor is extracted from various governments, United Nation (UN), and non-government organizational bodies. Weights were assigned to ten factors by using a non-linear multi-criteria optimization technique called full consistency method (FUCOM). A geographic information system (GIS) software, ESRI ArcGIS pro, performs the weighted overlay analysis of the ten factors with weighted importance calculated by the above technique. A suitability map is created showing that a total of 2.02% of the country's area is suitable for PV power plants, which are further divided into five suitability classes. The results highlight the distribution of suitable sites for the construction of solar PV power plant throughout the country. A sensitivity analysis is performed to highlight the impact of the factor on the final suitability map. These findings can promote the future widespread development and application of solar energy resources.

Groundwater potential assessment based on GIS-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) Method

In this study, the most suitable areas in terms of groundwater potential within the borders of the adjacent area of Sivas Municipality (Sivas/Turkey) were determined with the help of Geographic Information System (GIS)-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) methods. Slope, drainage density, Topographic Position Index (TPI), lineament density, lithology, soil types, land use, geomorphology, and rainfall criteria were selected to determine groundwater potential areas. These criteria were weighted with the help of BWM, SWARA, and BWM-SWARA methods and the Groundwater Potential Index (GPI) was calculated according to the weighted linear combination method. According to the calculated GPI values, the groundwater potential of the study area was represented as "excellent," "very good," "good," "moderately good," "low," and "very low." According to all three methods, areas in the "excellent" class constituted 10.99%, 8.40%, and 11.16% of the study area, respectively, while areas in the "very low" class covered 8.33%, 7.98%, and 9.04% of the study area, respectively. The linear correlation coefficient (R²) values of the BWM, SWARA, and BWM-SWARA methods were calculated as 0.80, 0.82, and 0.75, respectively, while the area under the curve (AUC) values were determined as 0.83, 0.79, and 0.81, respectively. These results showed that the accuracy of the model was "very good" overall. As a result, groundwater potential mapping created for the study area will contribute to better development of groundwater resources and water management planning.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Synthesis of strategies in post-COVID-19 public sector supply chains under an intuitionistic fuzzy environment

Entities in public sector supply chains (SCs) often operate independently despite having interdependent objectives. Such a fragmented operational design poses several problems magnified by the presence of necessary public health measures fueled by COVID-19. This work contributes to the domain literature by introducing an overarching framework for synthesizing strategies in public sector SCs. The underlying component is the translation of information from the upstream to the downstream entities of the SCs, which is carried out by a Kano-enhanced quality function deployment. The proposed framework introduces intuitionistic fuzzy (IF) decision maps with the aid of the full consistency method to incorporate inherent interrelationships among strategies in the translation agenda. Under an IF environment that better captures judgment uncertainties, an actual case study of a multi-level public sector SC motivated by a government-funded project under the COVID-19 pandemic is demonstrated in this work. Findings of the case suggest that the government prioritizes meeting all project objectives. This requirement is reflected in the downstream SC. The project planning entity focuses on creating an overarching plan of operations, material request entity on complying with government procurement protocols, and maintaining public health and safety in operations for the procurement entity. Results show the effective synthesis of strategies across the SC, ensuring SC integration and collaboration. The case study demonstrates that maintaining public health and safety is a significant component of post-COVID-19 public sector SCs. Several practical insights on the synthesis of public sector SC strategies are also provided in this work.

Entropy and discrimination measures based q-rung orthopair fuzzy MULTIMOORA framework for selecting solid waste disposal method

Fastest growing population, rapid urbanization, and growth in the disciplines of science and technology cause continually development in the amount and diversity of solid waste. In modern world, evaluation of an appropriate solid waste disposal method (SWDM) can be referred as multi-criteria decision-making (MCDM) problem due to involvement of several conflicting quantitative and qualitative sustainability indicators. The imprecision and ambiguity are usually arisen in SWDM assessment problem, and the q-rung orthopair fuzzy set (q-ROFS) has been recognized as one of the adaptable and valuable ways to tackle the complex uncertain information arisen in realistic problems. In the context of q-ROFSs, entropy is a significant measure for depicting fuzziness and uncertain information of q-ROFS and the discrimination measure is generally used to quantify the distance between two q-ROFSs by evaluating the amount of their discrimination. Thus, the aim of this study is to propose a novel integrated framework based on multi-attribute multi-objective optimization with the ratio analysis (MULTIMOORA) method with q-rung orthopair fuzzy information (q-ROFI). In this approach, an integrated weighting process is presented by combining objective and subjective weights of criteria with q-ROFI. Inspired by the q-rung orthopair fuzzy entropy and discrimination measure, objective weights of criteria are estimated by entropy and discrimination measure-based model. Whereas, the subjective weights are derived based on aggregation operator and the score function under q-ROFS environment. In this respect, novel entropy and discrimination measure are proposed for q-ROFSs. Furthermore, to display the feasibility and usefulness of the introduced approach, a case study related to SWD method selection is presented under q-ROFS perspective. Finally, comparison and sensitivity investigation are presented to confirm the robustness and solidity of the introduced approach.

Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best-worst approach: an emerging economy perspective

The food waste hierarchy offers that prevention of the waste is the best solution to deal with this complex issue. Herein, to take a preventive action against food waste, this paper attempts to prioritize the key causes and drivers of retail food waste employing Dombi-Bonferroni operators-based fuzzy best-worst (DB-FBWM) framework. Drawing upon stakeholder theory and contingency theory, causes of retail food waste are categorized into six aspects and their 23 drivers. The results revealed that the "customer-driven" cause is found as the foremost aspect of retail food waste, whereas the "undesirable in-store customer behaviors" is the most vital driver. Identifying the causes and drivers of retail food waste through a reliable model can advance theory and allow building partnership among food supply chain stakeholders for sustainable waste management. The sensitivity and comparison analysis are further performed to confirm the solidity of the approach conducted.

Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Location selection of electric vehicle charging stations (LSEVCS) is a complex multi-attribute group decision-making (MAGDM) problem involving multiple experts and multiple conflicting attributes. Spherical fuzzy sets (SFSs) can deeply excavate fuzziness and uncertainty in MAGDM. In this paper, we first propose some new spherical fuzzy distance measures based on Dice and Jaccard indexes to detect the differences between SFSs or inputs. Secondly, considering risk preferences of decision makers, we integrate cumulative prospect theory (CPT) and combined compromise solutions (CoCoSo) method to develop a spherical fuzzy CoCoSo based on CPT (SF-CPT–CoCoSo) model for settling MAGDM issues. At the same time, we extend the improved CRiteria Importance Through Intercriteria Correlation (CRITIC) method, called the distance correlation-based CRITIC (D-CRITIC) method, to reasonably obtain unknown attribute weights under SFSs. Finally, this paper applies the developed model for LSEVCS to verify its practicability. Moreover, sensitivity analysis and comparative discussion with existing methods further demonstrate the robustness and effectiveness of the SF-CPT–CoCoSo model.

Suitable site selection by using full consistency method (FUCOM): a case study for maize cultivation in northwest Turkey

The agricultural land evaluation procedure is a valuable guide for growing plants where they are best suitable, and it has a critical role in actualizing sustainable plans for providing food security for the growing population. In agricultural land suitability analysis, different multi-criteria decision-making methods are applied. The main objective of this study is to introduce the potential usage of a new multi-criteria decision-making method the Full Consistency Method (FUCOM) in agricultural land suitability analysis. The study was carried out in the northern part of the Karamenderes plain in NW Turkey. Nine land characteristics (soil texture, soil depth, organic matter content, electrical conductivity, pH, slope, drainage, CaCO3%, and cation exchange capacity) were used for the land evaluation study. The weighting values of the land characteristics were calculated by the FUCOM. According to the results, 223 ha (6.26%) were highly suitable, 2650 ha (74.40%) were moderately suitable, 508 ha (14.26%) were marginally suitable, and 181 ha (5.08%) were not suitable for maize cultivation. The weighted values of the parameters were also tested with Analytic Hierarchy Process (AHP) and the Best-Worst Method (BWM). There is a general compatibility between the methodologies. The data obtained from these methods showed that analysis consists of a very positive relationship with each other. The comparisons of these methodologies showed that FUCOM's prioritization order simplicity in parameter weighting and ability to reduce the processing intensity would provide a significant contribution and advantage to the land evaluation experts and planners. It is recommended that the Full Consistent Method could be reliably used in agricultural land suitability analysis.

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Method

The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics.

Results

Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were "ReF-decision tree," "IG-decision tree," and "Chi2-decision tree," with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.

Prioritizing population-based nutrition-related interventions to prevent and control hypertension in Iran: a multi-criteria decision-making approach

Background

Numerous nutrition-related policy options and strategies have been proposed to tackle hypertension and other risk factors of non-communicable diseases (NCDs). In this study, we developed a comparative analysis using a multi-criteria decision-making (MCDM) model for prioritizing population-based nutrition-related interventions to prevent and control hypertension in Iran.

Methods

We employed a combination of Delphi technique and Analytic Hierarchy Process (AHP) method as the methodological tool to prioritize decision alternatives using multiple criteria. The prominent assessment criteria and intervention strategies were derived using a literature review, focus group discussion (n = 11), and a 2-round modified Delphi technique with specialists and experts involved in different stages of health policy-making (round 1: n = 50, round 2: n = 46). Then, the AHP was used to determine the weightage of the selected interventions and develop the decision-making model. The sensitivity analysis was performed to test the stability of the priority ranking.

Results

Nine alternative interventions were included in the final ranking based on eight assessment criteria. According to the results, the most priority interventions to prevent and control hypertension included reformulation of food products to contain less salt and changing the target levels of salt in foods and meals, providing low-sodium salt substitutes, and reducing salt intake through the implementation of front-of-package labeling (FOPL). The results of the sensitivity analysis and a comparison analysis suggested that the assessment model performed in this study had an appropriate level of robustness in selecting the best option among the proposed alternatives.

Conclusion

MCDM techniques offer a potentially valuable approach to rationally structuring the problem, along with the opportunity to make explicit the judgments used as part of the decision-making model. The findings of this study provide a preliminary evidence base to guide future decisions and reforms aiming to improve appropriate population-based interventions for tackling hypertension and other risk factors of NCDs.

Operational efficiency evaluation of urban and rural residents' basic pension insurance system by utilizing a picture fuzzy TOPSIS method based on the cumulative prospect theory

Population aging is the most serious challenge facing the pension insurance system in China in the next few decades. Compared with the employees of civil servants and enterprises and institutions, urban and rural residents are unstable vulnerable groups with less income. In order to deal with the pension risks caused by the growing aging population and solve the security problems of urban and rural residents, our government has carried out a series of constructive works in the pension insurance system: in view of the rural and urban residents, new rural social endowment insurance system and the social endowment insurance system for urban residents have been set up and combined into a unified basic old-age insurance system for urban and rural residents in 2014. With the continuous expansion in the scale of income and expenditure of urban and rural living insurance funds and the size of the insured number, it is of great necessity to evaluate the efficiency of this system. The operational efficiency evaluation of urban and rural residents' basic pension insurance systems is viewed as multi-attribute group decision-making (MAGDM). In this paper, we propose an approach by combining the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with cumulative prospect theory (CPT) which can be widely used with vague information. Thus, the main contribution of this study is as follows: (1) the TOPSIS method is extended by picture fuzzy sets (PFSs) with unknown weight information; (2) entropy method to obtain the original weights of attributes; (3) the picture fuzzy-CPT-TOPSIS (PF-CPT-TOPSIS) method is used to deal with the MAGDM problems under PFSs; (4) a numerical instance for operational efficiency evaluation of urban and rural residents' basic pension insurance systems is proposed to testify the effectiveness of new method; and (5) some comparative studies are provided to give effect to the rationality of PF-CPT-TOPSIS approach.

Research on expert group decision-making and conflict resolution in complex engineer-ing consulting process

Expert group decision-making in the process of engineering consulting is an important part of the smooth development of engineering projects. Whether the conceptual design scheme of the project is reasonable or not will directly affect the construction quality of the project. After the preliminary selection of a river ecological governance project, four conceptual design schemes were obtained. The owner invited 20 experts in relevant fields to make decisions on the four schemes collected in the early stage. The experts gave preference information for each scheme after reading the relevant materials of the project and clarifying the actual needs of the project. Based on this background, this paper uses a combination of quantitative and qualitative methods to construct a model for group decision-making and conflict resolution in the engineering consulting process. We use the preference relationship to reflect the degree of experts’ preference for the scheme, cluster them through similarity calculation, calculate the conflict degree of group preference and personal preference respectively, and comprehensively use the sequence difference method and personal preference correction method to resolve the conflict, so that their opinions can be quickly agreed within the specified time. The results calculated by model are consistent with the actual situation of the project, which verifies the effectiveness of the model proposed in this paper, which can provide a reference for similar project decision-making and conflict resolution process.

A Multi-Criteria Approach to Assess the Performance of the Brazilian Unified Health System

Brazil's Unified Health System (SUS) provides universal free access to health services and is considered a model for the rest of the world. One of the tools used by the Brazilian government to assess this system is the Index of Unified Health System Performance (IDSUS). However, this method has a number of limitations, such as disregarding the opinion of healthcare decision makers. Thus, the aim of the present study was to propose a model based on the R-TOPSIS in order to assess the performance of the SUS. Methodologically, the main steps for proposing multi-criteria models were followed, and to validate the model, a real case study with a set of six cities (alternatives) of the state of São Paulo was used. The results provide a clearer picture of the differences in terms of potential and obtained access, as well as the effectiveness of health services in the cities analyzed. Likewise, the proposal of integrating multiple criteria as well as considering healthcare decision makers proved to be decisive for the results obtained, even in comparison with the other approaches. It was concluded that the proposed method provides a robust and adequate analysis of health systems performance.

Evaluation of critical risk factors in the implementation of modular construction

Modular construction is considered as a preferred construction method over conventional construction due to a number of benefits including reduction in project completion time, improved environmental performance, better quality, enhanced workers’ safety and flexibility. However, successful implementation of modular construction is hindered by various risk factors and uncertainties. Therefore, it is imperative to perform a comprehensive risk assessment of critical risk factors that pose a negative impact on the implementation of modular construction. Moreover, there is also a relatively less rate of modular construction adoption in developing countries, highlighting the need to focus more on underdeveloped regions. This study aims to propose a risk assessment framework for identification, evaluation and prioritization of critical risk factors affecting the implementation of modular construction in Pakistan. 20 risk factors were identified from previous literature which were then evaluated to shortlist the most significant risks using Fuzzy Delphi. The most significant risk factors were then prioritized using a novel Full-Consistency Method (FUCOM). The results specified ‘Inadequate skills and experience in modular construction’, ‘Inadequate capacity of modular manufacturers’ and ‘Inability to make changes in design during the construction stage’ as top three critical risks in the implementation of modular construction. This is the first study to propose a risk assessment framework for modular construction in Pakistan. The results of the study are useful to provide insights to construction industry practitioners in highlighting and eliminating risks involved in modular construction planning and execution.

TOPSIS methods for probabilistic hesitant fuzzy multiple attribute group decision-making and application to performance evaluation of public charging service quality

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide a new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) A novel PHF-TOPSIS method is proposed; (2) The extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) The FUCOM method is used to obtain the attribute weight; (4) The normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

Modification of the DIBR and MABAC Methods by Applying Rough Numbers and Its Application in Making Decisions

This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods. The methods are modified by applying rough numbers, which present a very suitable area for considering uncertainty following decision-making processes. The DIBR method is a young method with a simple mathematical apparatus which is based on defining the relation between ranked criteria, that is, adjacent criteria, reducing the number of comparisons. This method defines weight coefficients of criteria, based on the opinion of experts. The MABAC method is used to select the best alternative from the set of the offered ones, based on the distance of the criteria function of every observed alternative from the border approximate area. The paper has two main innovations. With the presented decision-making support model, the ATMS selection problem is raised to a higher level, which is based on a proven mathematical apparatus. In terms of methodology, the main innovation is successful application of the rough DIBR method, which has not been treated in this way in the literature so far. Additionally, an analysis of the literature related to the research problem as well as to the methods used is carried out. After the application of the model, the sensitivity analysis of the output results of the presented model to the change of the weight coefficients of criteria is performed, as well as the comparison of the results of the presented model with other methods. Finally, the proposed model is concluded to be stable and multi-criteria decision-making methods can be a reliable tool to help decision makers in the selection process. The presented model has the potential of being applied in other case studies as it has proven to be a good means for considering uncertainty.

A New Fuzzy Extension of the Simple WISP Method

The purpose of this article is to introduce to the literature a new extension of the Simple WISP method adapted for utilizing the triangular fuzzy numbers. This extension is proposed to allow the use of the Simple WISP method for addressing decision-making problems related to uncertainties and inaccuracies, as well as for solving problems related to predictions. In addition, this article also discusses the use of linguistic variables to collect the attitudes of the respondents, as well as their transformation into appropriate triangular fuzzy numbers. The article discusses the use of two defuzzification procedures. The first normalization procedure is easy to use, while the second procedure uses the advantages that the application of asymmetric fuzzy numbers gives in terms of analysis. The usability of the proposed extension is presented through two examples.

A multi-criteria assessment of policies to achieve the objectives of the EU marine litter strategy

This paper proposes and assesses policy options to achieve the objectives of the EU marine litter strategy, based on the existing EU legislation. A group of experts and stakeholders was involved through a multi-staged workshop organized to generate the information to assess the contribution of the policies to the set of objectives, as well as the relative importance of the objectives, on qualitative scales. The ELECTRE TRI multi-criteria decision analysis method was used to rate the policies, which were subsequently ranked. Per the results, all policies deserve to be considered, even when the more pessimistic assessments (33rd percentile) are used. Revising the Urban Wastewater Treatment Directive, setting legislative targets on marine litter and ensure they are fully monitored, and funding proven clean-up technologies were deemed to be the most potentially impactful policies. The remaining policies assessed can nevertheless play an important role in complementing the higher-ranked policies.