Hesitant and uncertain linguistics based executive decision making using risk and regret aversion: Methods, implementation and analysis

Presence of globally-affecting issues, such as the recent COVID-19 pandemic is a major factor impacting the operation of services provided by high-stake companies. These factors create huge hindrances in the regular and proper operations of companies in staying relevant in market while catering to the services they provide. In such cases, in order to maintain and achieve their internal goals should any possible losses that the grave situation might incur, relevant experts within these firms must arrive at optimal decisions taking into account human cognition as well as all possibilities of risk and regrets. A suitable regret theory based linguistic decision-making model called THREAD which computes with inherent hesitancy using interval type-2 fuzzy sets (IT2 FS) and hesitant fuzzy linguistic term sets-based techniques is introduced in this paper.

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

The determination of the areas where the solar power plant will be installed is of great importance for the performance of the solar power plant. Solar and hydroelectric energy are the most widely used renewable energy sources in Kars province. Site selection for these power plants is an important factor in terms of reducing the installation cost of the solar power plant and achieving maximum efficiency during operation. Determining the areas where the power plants will be installed is a very complex and difficult to analyse spatial decision making problem. In this study, firstly GIS is used as a mapping method to obtain the locations of both solar power plants in Susuz, Arpaçay, Akkaya, Kars city centre, Selim, Digor, Kağızman and Sarıkamıș districts of Kars province and then Taguchi loss function based interval type-2 fuzzy approach is applied to the problem. In order to obtain more accurate results, the results of the two methods (GIS and Taguchi loss function based interval type-2 fuzzy approach) were also compared. According to the solar power plant map obtained, it was determined that the total area of suitable areas is 78600 km2.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

Objective

The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials.

Methods

It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments.

Results

The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches.

Conclusions

To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies

BACKGROUND

India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies.

METHODS

We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures.

RESULTS

MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl.

CONCLUSIONS

We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

A comprehensive assessment to offer optimized remediation method for mercury contamination in Musa Bay by using hybrid Fuzzy AHP-VIKOR approach

Musa Bay, the largest wetland in Iran and one of the most important Hg-polluted media, plays a significant role in the ecosystem of the area and supports many forms of life. Mercury pollution has detrimental effects on the human body and at high levels leads to the loss of microorganisms in marine ecosystems. Hence, a comprehensive assessment for selecting an effective and sustainable remediation method is crucial to restoring the ecosystem promptly. The determination of a proper and practical treatment method not only is a case-based approach, but could be challenging due to its multi-criteria decision-making nature. Considering preferred crucial factors involved in the effectiveness of remedial actions, in this study a questionnaire is designed to assess the opinion of environmental experts, stakeholders, and some occupants of the area on remedial actions based on the importance weights of criteria. Subsequently, practical remediation and management strategies ranked by hybrid FVIKOR as a multi-criteria decision making (MCDM) method. Ranking results show that dredging and stabilization could offer a promising solution for the remediation of the case study. The results of the study demonstrate that the development of MCDM methods along with effective criteria and considering the analysis of the questionnaires, could offer the best remediation strategy for a specific contaminated site.

Promoting sustainable management of hazardous waste-to-wealth practices: An innovative integrated DPSIR and decision-making framework

Sustainable valorization of tannery sludge (TS) is vital for achieving several sustainable development goals (SDGs) in the tannery industry. TS is considered a hazardous waste by-product posing a significant environmental challenge. However, TS can be utilized for energy or resource recovery by considering it as biomass and implementing the circular economy (CE) concept. Therefore, this study aims to develop an innovative DPSIR (Driver, Pressure, State, Impact, and Response) framework for promoting sustainable valorization of TS. Further, the study extends to quantify the importance of subjective DPSIR factors by offering interval-valued intuitionistic fuzzy number-based best worst method (IVIFN-BWM), which is relatively new in the literature and able to deal with the uncertainty, inconsistency, imprecise, and vagueness in the decision-making process. The study also investigates the most appropriate TS valorization technologies concerning identified DPSIR factors using a novel IVIFN-combined compromise solution (CoCoSo) approach. This research contributes to the literature by developing a comprehensive solution approach that combines the DPSIR framework, IVIFN-BWM, and IVIFN-CoCoSo method in addressing sustainability and resource recovery challenges for the tannery industry. The research findings highlight the potential of sustainable valorization of TS in reducing the waste amount and promoting sustainability and CE practices in the tannery industry. The findings indicated that response factors 'creation of national-level policies and awareness campaign' and 'facilitating financial support to adopt waste valorization technologies' received the highest priority among other DPSIR factors for managing and fostering sustainable valorization of TS. The IVIFN-CoCoSo analysis confirmed that the most promising TS valorization technology is 'gasification', which is followed by pyrolysis, anaerobic digestion, and incineration. The study's implications extend to policymakers, industrial practitioners, and researchers, who can leverage the research findings to develop more sustainable TS management practices in the tannery industry.

A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: A hybrid AHP-TOPSIS approach

Burkina Faso's agricultural and industrial sectors are sources of biomass production which are not well tapped. However, the exact quantities available and mobilizable with connection to recovery technologies are not available in the literature. Also, there is very little data on the criteria for the optimal selection of the biomasses to be valorized. In this article, quantification of the main biomasses produced in Burkina Faso has been carried out. Additionally, sustainable biomass selection criteria have been established. A hybrid (AHP-TOPSIS) multi-criteria decision-making (MCDM) approach was used to prioritize suitable biomass resources based on defined criteria for bioenergy production. Based on expert opinions and an in-depth review of the literature, six main biomass selection criteria were established: i) biomass availability and accessibility, ii) competitive uses, iii) pollution potential related to residue accumulation, iv) economic impact, v) biomass energy content, and vi) availability of appropriate biomass conversion technologies. Moreover, five potential biomasses were investigated, including cotton stalks, rice husks, cashew nutshells, mango peels, and mango pits. The results of the evaluation showed that cotton stalks were the best option.

Using the deterministic approach model for project portfolio selection problem (PPSP) solutions

Selection of projects using a robust technique is rare as most of the techniques are not considered useful due to the limitation on the number of projects that can be selected as well as cost saving projects not being selected. This study investigated the validity of a hybrid model - integrated analytical hierarchy process-goal programming (AHP-GP) - to avoid project portfolio selection problems delaying community development. The proposed model includes two steps: AHP to determine the project criteria, the relative importance of weights, and priority preferences, while the GP model was formulated to select the optimal projects. An empirical study on government agencies was carried out to validate the proposed model, and the results compared against GP as a standalone to solve the same problem. The results proved that the hybrid model (AHP-GP) was better than the GP model. AHP-GP has proved to be a robust mechanism most suitable for managerial use due to its ability to handle multi-criteria decision-making (MCDM) situations. This study showed that the hybrid model can select more projects and will create more jobs in the communities concerned compared to the single model (GP). The novelty of this study is the introduction of an integrated model formed from two distinct models as a deterministic approach to solving project portfolio selection problems.

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

Application of q-rung orthopair fuzzy based SWARA-COPRAS model for municipal waste treatment technology selection

Despite several methods available for the treatment of solid wastes, the management of municipal solid waste is still a crucial and complex process. The available methods for waste treatment range from advanced to conventional techniques. The identification of a proper method for municipal solid waste management involves several techno-eco and environmental considerations. To solve the real-world problems of municipal waste management, the research proposed an integrated q-rung orthopair fuzzy number-based stepwise weight assessment ratio analysis-complex proportional assessment (SWARA-COPRAS) mathematical model to rank the waste treatment techniques. The research aimed to develop a systematic approach for a suitable selection of waste treatment methods. Ten (10) different alternatives for waste treatments were ranked against seven (07) different techno-eco and environmental criteria. The ambiguity in the decision was handled by the q-rung orthopair fuzzy numbers. The proposed integrated model has identified upcycling and recycling of waste having priority values of 100% and 99.9%, respectively, as the suitable practices for the successful management of generated solid wastes, whereas landfilling has obtained a minimum priority value of 66.782% and, therefore, is least preferable for waste management. The ranking of the alternatives followed the sequence as upcycling > recycling > pyrolysis > hydrolysis > biotechnological > core plasma pyrolysis > incineration > composting > gasification > landfilling. The comparison between the rankings of the proposed model with other techniques has revealed that the values of Spearman's rank correlation coefficient are in the range of 0.8545 to 0.9272; thereby, the robustness of the proposed model is verified. Sensitivity analysis for the criteria weight has showed that the ranking results are influenced significantly by the change in criteria weights and suggested that an accurate estimation of the criteria weight is decisive in determining the overall ranking of the alternative. The study has provided a framework for decision-making in the technology selection for solid waste management.

Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems

A Multi-Criteria Recommender System (MCRS) represents users' preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures.

Prioritizing of sectors for establishing a sustainable industrial symbiosis network with Pythagorean fuzzy AHP- Pythagorean fuzzy TOPSIS method: a case of industrial park in Ankara

Difficulty in accessing resources and increasing environmental concerns encourage industrial manufacturing enterprises to establish a symbiosis network. The identification of symbiotic relationships contributes to the more sustainable development of industrial activities. However, businesses operating in industrial parks are diversified by sector. In order to establish a sustainable symbiosis network in industrial parks, the symbiotic relations of each sector in industrial parks should be evaluated separately. Thus, the installation process of the symbiosis network will be easier and more sustainable. In this context, this study aims to prioritize the sector in which a symbiosis network will be established by presenting an innovative approach for the evaluation of symbiosis potentials. For this purpose, criteria for the implementation process affecting the establishment of the symbiosis network were determined. Multi-criteria decision-making methods were used to solve the problem. Considering the uncertainties in the process, fuzzy multi-criteria decision-making methods were used. As a result, a decision-making model has been proposed to determine the priority sector in order to establish a symbiosis network in industrial parks. According to the results obtained with the multi-criteria decision-making methods, the number of enterprises that will evaluate the waste, that is, the number of customers with waste, has been determined as the criterion with the highest level of importance. While evaluating the alternatives, the casting sector was chosen as a priority. This sector is followed by the petro and chemical sector as the second alternative.

Framework for multi-criteria assessment of classification models for the purposes of credit scoring

The main dilemma in the case of classification tasks is to find-from among many combinations of methods, techniques and values of their parameters-such a structure of the classifier model that could achieve the best accuracy and efficiency. The aim of the article is to develop and practically verify a framework for multi-criteria evaluation of classification models for the purposes of credit scoring. The framework is based on the Multi-Criteria Decision Making (MCDM) method called PROSA (PROMETHEE for Sustainability Analysis), which brought added value to the modelling process, allowing the assessment of classifiers to include the consistency of the results obtained on the training set and the validation set, and the consistency of the classification results obtained for the data acquired in different time periods. The study considered two aggregation scenarios of TSC (Time periods, Sub-criteria, Criteria) and SCT (Sub-criteria, Criteria, Time periods), in which very similar results were obtained for the evaluation of classification models. The leading positions in the ranking were taken by borrower classification models using logistic regression and a small number of predictive variables. The obtained rankings were compared to the assessments of the expert team, which turned out to be very similar.

A systematic review of geographic information systems based methods and criteria used for electric vehicle charging station site selection

Many studies have incorporated particular models with various methods and algorithms to resolve the site selection problem for electric vehicle charging stations (EVCS). This paper systematically reviews research that evaluates geographic information systems (GIS) based EVCS location techniques and the variables used for decision making. We classify and characterize those techniques and variables to determine important linkages within the literature. A variety of databases were referenced to extract research published from 2010 to March 2023 pertinent to this specific location optimization problem, and 74 papers were selected after thorough evaluation. The models used in each paper were examined along with the methods for selecting variables and ranking alternate locations. Site selection for EVCS requires a multi-criteria decision making approach to meet the sustainability, efficiency, and performance goals of communities adopting electric vehicle mobility. Our results indicate that map algebra and data overlay methods have been used more frequently with GIS-based analysis than other techniques, while geographic and demographic variables are commonly the most significant site selection characteristics. The reviewed methods have most often been applied to urban locations; however, the transfer of these techniques to a rural EVCS site selection problem has been rarely explored in the current literature. This research assessment contributes relevant guidance for the application of methodologies useful in policymaking and provides recommendations for future research based on these findings.

Combining SWOT analysis and neutrosophic cognitive maps for multi-criteria decision making: a case study of organic agriculture in India

The conventional agricultural system heavily depends on chemicals and inorganic fertilizers, which cause environmental issues. Organic agriculture impacts 6 of the 17 Sustainable Developmental Goals (SDGs) of the United Nations. Strategies to develop organic agriculture have used SWOT and MCDM techniques for analysis. However, the examination of the influence of one strategy over the other strategies has yet to be investigated. This paper proposes a model that combines the existing SWOT analysis with neutrosophic cognitive maps (NCM) models to analyze interconnections among the various strategies obtained from SWOT. This research deploys the proposed SWOT-NCM model to analyze the case study of developing organic farming in Tamil Nadu, India. It offers insights into the strategy's influence over other strategies so that the best is given maximum importance while implementing organic farming. The framework captures the interconnections and ranks the strategies by order of influence, providing fresh insights by taking the farmers' perspective while working with the strategies from the SWOT analysis to model an NCM. A comparative analysis of this SWOT-NCM model with other MCDM models that use SWOT to analyze the agriculture problem, and a sensitivity analysis of the proposed model, is performed. According to our study, the best possible strategy to encourage organic farming is minimum support price (MSP) and centralized procurement. This proposed model can analyze other MCDM problems that use SWOT analysis.

Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero-one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.

A matheuristic for customized multi-level multi-criteria university timetabling

Course timetables are the organizational foundation of a university's educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure's capability of generating high quality schedules.

How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

GIS-based AHP analysis to recognize the COVID-19 concern zone in India

This study was planned to identifying the Corona concerns zone during COVID-19 transmission in India. The death rate was very high due COVID-19 pandemic outbreaks which are one of the main reasons for impairment the countries, and it will takes several years for the re-establishment of the fundamental need to ensure the demand of public supply. Currently, like many countries around the world, India is also facing a drastic health crisis due to Corona virus disease. Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) play important role in making the multi-criteria decisions and identifying the corona concern zone of a larger populated areas across the country in a single platform which can be further helpful for better control, planning, and management during several pandemic outbreaks. The present work is based on the AHP and GIS-assisted identification, analysis, and representation of the state-wise corona concern zone of India. Consequently, the current examination is essential to investigate the Corona concern zone in order to support the management and planning authority of India to improve their strategies in respect to reduce or check the health risk during the emergency of pandemic due to COVID-19. The present study indicated that the state-wise priority of corona concern zone recorded higher in state Maharashtra, Uttar Pradesh, and Kerala as compared to the other part of the India. Hence, GIS and AHP are the potential to identify, observe and analyze the COVID-19 Concern Zone.

Effective machine learning, Meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover

Employee turnover is one of the most important issues in human resource management, which is a combination of soft and hard skills. This makes it difficult for managers to make decisions. In order to make better decisions, this article has been devoted to identifying factors affecting employee turnover using feature selection approaches such as Recursive Feature Elimination algorithm and Mutual Information and Meta-heuristic algorithms such as Gray Wolf Optimizer and Genetic Algorithm. The use of Multi-Criteria Decision-Making techniques is one of the other approaches used to identify the factors affecting the employee turnover in this article. Our expert has used the Best-Worst Method to evaluate each of these variables. In order to check the performance of each of the above methods and to identify the most significant factors on employee turnover, the results are used in some machine learning algorithms to check their accuracy in predicting the employee turnover. These three methods have been implemented on the human resources dataset of a company and the results show that the factors identified by the Mutual Information algorithm can show better results in predicting the employee turnover. Also, the results confirm that managers need a support tool to make decisions because the possibility of making mistakes in their decisions is high. This approach can be used as a decision support tool by managers and help managers and organizations to have a correct insight into the departure of their employees and adopt policies to retain and optimize their employees.

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.

Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%-95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ - ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers.

Applying the AHP to Conflict Resolution: A Russia-NATO Case Study

In this paper, we apply the Analytic Hierarchy Process approach to conflict resolution in the context of the Russia-Ukraine conflict. We build models that illustrate the evaluation criteria, strategic and sub-criteria, and concessions for each party in this negotiation. Ratings are used to evaluate the degree to which concessions contribute or take away from successful resolution of the conflict. Afterwards, gain ratios are built to determine the benefit-cost scores so that concessions may be traded that result in equitable solutions. The approach presented here demonstrates for the first time why all concessions that parties to a conflict may offer might not trade all at once. A Max-Min optimization approach is used to maximize the gain to both parties of the conflict while minimizing the disparity in gain between the two.

A Multi-Criteria Decision-Making Model with Interval-Valued Intuitionistic Fuzzy Sets for Evaluating Digital Technology Strategies in COVID-19 Pandemic Under Uncertainty

Coronavirus diseases 2019 (COVID-19) pandemic is an essential challenge to the health and safety of people, medical members, and treatment systems worldwide. Digital technologies (DTs) have been universally introduced to improve the treatment of patients during the pandemic. Nevertheless, only a few governments have been partly successful in executing the DT strategies. In this regard, it is critical to demonstrate a suitable strategy for the governments. This problem is built based on the experts' opinions with some conflicting criteria to evaluate various types of alternatives. Hence, this research presents a new multi-criteria decision-making (MCDM) model under uncertain conditions. For this reason, interval-valued intuitionistic fuzzy sets (IVIFSs) are employed to help decision-makers (DMs) evaluate in a broader area and cope with uncertain information. Moreover, a new extended weighting method based on weighted distance-based approximation (WDBA) and a new combined ranking approach are proposed to determine the DMs' weights and rank the alternatives under IVIF conditions. The developed weighting method is constructed based on computing the DMs' weights with objective criteria weights. Furthermore, a new ranking approach is proposed by obtaining two ranking indexes separately: The first and second ranking indexes are calculated according to the positive and negative ideal solutions distances and the nature of criteria weights, respectively. Afterward, the final values of rankings are computed by considering a new aggregating procedure. The results of the proposed model represent the first alternative as the best strategy. Comparisons between the IVIF-TOPSIS and IVIF-VIKOR methods are also provided to investigate the proposed model to determine the rankings of main alternatives. Sensitivity analyses are conducted to check the reliability and the robustness of the model. For this purpose, criteria weights are analyzed to compute the dependencies' degree of the new extended weighting method. The dependencies of the ranking model are discussed on the criteria weights as well.

An analytical hierarchy process based decision support system for the selection of biogas up-gradation technologies

Recent developments in biogas upgradation have opened new horizons for its utilisation because upgradation technologies are fully developed and commercially available. However, the implementation of biogas upgrading technologies is not at the scale required to harness the full potential of biogas. Therefore, it is requisite to adopt a multicriteria decision-making methodology (MCDM) to select the most appropriate biogas up-gradation technology as each technology has its own set of benefits and downside. In this multifaceted scenario, the analytical hierarchy Process (AHP), one of the most preferred MCDM methods in rational decision-making, is applied in this study to select the most appropriate biogas upgrading technology. The broader recognition of AHP is its provision for converting multifaceted problems into a simple hierarchy. The research results reveal that biogas up-gradation technologies based on water scrubbing and membrane separation rank first and second among the alternatives. This research will show a direction to researchers and the MCDM community involved in biogas upgradation technologies on a broader scale.

Minimality and comparison of sets of multi-attribute vectors

In a decision-making problem, there is often some uncertainty regarding the user preferences. We assume a parameterised utility model, where in each scenario we have a utility function over alternatives, and where each scenario represents a possible user preference model consistent with the input preference information. With a set A of alternatives available to the decision-maker, we can consider the associated utility function, expressing, for each scenario, the maximum utility among the alternatives. We consider two main problems: firstly, finding a minimal subset of A that is equivalent to it, i.e., that has the same utility function. We show that for important classes of preference models, the set of possibly strictly optimal alternatives is the unique minimal equivalent subset. Secondly, we consider how to compare A to another set of alternatives B , where A and B correspond to different initial decision choices. This is closely related to the problem of computing setwise max regret. We derive mathematical results that allow different computational techniques for these problems, using linear programming, and especially, with a novel approach using the extreme points of the epigraph of the utility function.

Assessment of agri-environmental situation in selected EU countries: a multi-criteria decision-making approach for sustainable agricultural development

In recent years, humanity has faced with multiple crises, both of economic and environmental nature. Among the key reasons for such turbulences, the deteriorating agri-environmental situation appears as an important facet. This article evaluates agri-environmental situation of selected European Union (EU) countries using the multi-criteria decision making methods (SAW, TOPSIS, and EDAS) to identify the potential strategies for improvement of agricultural activities and environmental situation in general. The set of indicators, compiled from the database, prepared by the European Commission (EC) was used for this research. The empirical results show that the trends in agri-environmental situation of selected EU countries are similar under all the methods used. The best agri-environmental situation both at the beginning and at the end of the research period was in Finland, Ireland, and Sweden. On contrary, the worst situation was identified in the Netherlands, Denmark, and Germany. The only case of decline in the agri-environmental performance is observed for Lithuania, whereas ascension in ranks is observed for Austria and Poland. The results are of particular importance in the period of development of agri-environment and climate schemes for the European Union Common Agricultural Policy post-2020.

A new application of multi-criteria decision making in identifying critical dust sources and comparing three common receptor-based models

Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM₁₀ in local and remote area. The Yazd city in Iran with a high PM₁₀ level located downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM₁₀ monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012-2019. The trajectory cluster analysis was used to identify the main dust transport pathways and wind systems. Three methods of Cross-referencing Backward Trajectory (CBT), Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) were used to identify the most critical dust sources. Multi-Criteria Decision Making (MCDM) methods were also used to integrate the results. Nine dust sources affecting central Iran were determined, and six criteria from different aspects were considered. To prioritize the dust sources affecting central Iran from four new MCDM methods, including WASPAS, EDAS, ARAS and TOPSIS were used. The results showed that the Levar wind system (51%), the Shamal wind system (32%) and the Prefrontal wind system (18%) were the most important wind systems to cause dust events in central Iran. The MCDM approach to identify dust sources also showed that Dasht-e-Kavir in central Iran was the most critical dust source. The results also showed that in hot seasons (spring and summer), local and Central Asia dust sources and cold seasons (autumn and winter), Middle East dust sources have the greatest impact on dust events in central Iran. Also, a comparison of common receptor-based methods for identifying dust sources showed that CBT, CWT and PSCF were the most appropriate methods for identifying dust sources, respectively.

Life cycle environmental and economic comparison of thermal utilization of refuse derived fuel manufactured from landfilled waste or fresh waste

This paper analyses environmental and economic performance of thermal utilization technologies of two different refuse derived fuel (RDF) manufactured from landfilled waste or fresh municipal waste, including incineration of landfilled RDF (I-LRDF), gasification of landfilled RDF (G-LRDF), replacement of partial coal by landfilled RDF for the cement industry (C-LRDF), incineration of municipal RDF (I-MRDF), and replacement of partial coal by municipal RDF for the cement industry (C-MRDF). The preference among the RDF utilization options is identified from the standpoints of various stakeholders by integrating the life cycle assessment (LCA) and techno-economic analysis (TEA) with the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) approaches. RDF thermal utilization technologies bring an economic profit of $17.29∼$35.77 per ton of waste. Especially, I-LRDF has the worst effect on ecosystem quality and human health and can yield the greatest economic profit of $35.77 per ton of landfilled waste, while I-MRDF has the least impact on environment. In terms of the five RDF thermal utilization technologies, I-MRDF has the best comprehensive performance from the perspectives of different stakeholders. The improvement of the RDF thermal utilization efficiency is the most critical factor affecting the economic benefits for all cases.

A novel methodology for perception-based portfolio management

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment.

Analysis of factors affecting industrial symbiosis collaboration

The rapidly increasing population causes an increase in consumption amounts day by day. This leads to negative effects such as the reduction of limited resources. In order to eliminate or reduce such negative effects, sustainable approaches are adopted for the future. Industrial symbiosis is one of these sustainable approaches. Industrial symbiosis is when two or more economic enterprises operating independently of each other form beneficial partnerships. In this study, the factors affecting industrial symbiosis collaboration were determined by literature review and by analyzing these factors; it is aimed to eliminate inefficiencies and to ensure the sustainability of established relations. The criteria determined are weighted with the Analytical Network Process method, which is one of the multi-criteria decision-making methods, and it is aimed to calculate the degree of importance and priority.

Identifying future partner agencies: helping Brazos Valley Food Bank in the fight against food insecurity

Brazos Valley Food Bank (BVFB) is a non-profit organization in the Bryan-College Station area of Texas. It distributes food supplies through partner agencies and special programs to eradicate hunger in Brazos Valley. However, a big gap exists between the meals distributed by BVFB and the size of the food-insecure population. This research is motivated by BVFB's desire to reach more people by recruiting more sustainable partner agencies. We used Geographic Information Systems (GIS) to map food desert areas lacking access to nutritious food. We combined expert knowledge with multi-criteria decision-making (MCDM) to address the challenges and time consumption of manually identifying sustainable partner agencies for local food delivery. We identified evaluation criteria for all agencies based on BVFB managers' preferences using a qualitative approach, and then applied three quantitative decision-making models: the Weighted Sum Model (WSM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-criteria Optimization and Compromise Solution (VIKOR) models to obtain ranking results. We compared the quantitative models' rankings to BVFB managers' manual choices and discussed the impacts of our research. The key innovation of the research is to develop a mixed method by combining expert knowledge with mathematical decision models and GIS to support spatial decision making in food distribution. Although our results were specific to BVFB, these procedures can be applied to food banks in general. Future studies include finetuning our models to measure and address human biases, wider applications and more data collections.

Implementation of the circular supply chain management in the pharmaceutical industry

The ever-increasing levels of pollution and waste creation have subjected industries around the world to incorporate the concept of circular economy (CE) in their supply chains. The amalgamation of the CE approach along with supply chain management is called circular supply chain management (CSCM). Among other industries, the pharmaceutical industry is also involved in damaging the ecosystem. Hence, an effective framework for the adoption of CSCM in a particular industry is very essential. Therefore, this paper aims to devise a model that will help the pharmaceutical industries to adopt CSCM in their organizations. For this purpose, the study in the first phase identifies ten barriers that are working as an impediment in the adoption of the CSCM approach. To counter those barriers, the study in the second phase identifies a set of twelve enablers. To analyse the barriers and enablers, the study uses a new hybrid methodology. For allocating weights and prioritizing the barriers, the fuzzy multi-criteria decision-making (MCDM) technique, i.e. fuzzy full consistency method (F-FUCOM) is used, whereas the total quality management tool, i.e. fuzzy quality function deployment (FQFD) is used to rank the enablers. The results from F-FUCOM suggest "lack of financial resources and funding", "market challenges", and "lack of coordination and collaboration among the entire supply chain network" to be the top-most barriers, respectively, whereas the results achieved from the FQFD suggest "industrial symbiosis", "Reverse Logistic (RL) infrastructure", and "block chain technology" to be the top-ranked enablers, respectively. The provision of a facilitating framework for the adoption of CSCM in the pharmaceutical industry and the newly developed hybrid methodology are both novelties of this study.

Two-stage weighted PROMETHEE II with results' visualization

Multicriteria decision-making methods are widely spread and used to assist the decision-makers to resolve problems. Many of the methods are simple to deploy (WSA, TOPSIS), which is an advantage and because of the computer boom, there is no problem with calculations. However, more sophisticated methods are evolving. The modelling of preferences is improved (from linear in WSA to Gaussian in PROMETHEE), multilevel decision-making (such as AHP) is extended to modelling of dependencies between individual criteria (ANP). The presented method, two-stage weighted PROMETHEE, combines the advantages of generalized preferential functions in PROMETHEE methods, unambiguous arrangement (PROMETHEE II) and hierarchical approach (AHP). In addition, this paper demonstrates the application of the method to evaluate the order of 14 regions of the Czech Republic in regard to economic indices such as the unemployment rate, economic activity, average age, wages, free working places, income, consumption and investments. Data are taken from the Czech Statistical Office web and include the years 2012-2019. In the first stage, the position of each region is calculated; in the second stage, all years mentioned are considered, including the aspect of the weighted time series. Result visualization is made possible using the Visual PROMETHEE software.

Prioritizing the noise control methods by using the analytical hierarchy process (AHP) method in an Iranian tire factory

BACKGROUND

Noise is a common harmful physical factor in the work environment.

OBJECTIVE

This study sought to prioritize noise control methods using the analytical hierarchy process (AHP) in a tire factory.

METHODS

The study, which adopted a cross-sectional, descriptive, analytical design, was conducted in the baking hall of an Iranian tire manufacturing factory in 2018. 4 criteria (namely implementation and maintenance cost, method applicability, method effectiveness and efficiency, and intervention in the process) and 8 alternatives (including reducing individuals' noise exposure time, designing and installing sound isolation chamber for operators, using of earmuffs and earplug simultaneously, changing processes or operational procedures in machinery with excessive noise generation, forming noise control engineering teams, requiring people in charge to quickly fix the leaks and change baking press washers on time, using acoustic panels in the ceiling and walls, and designing and manufacturing silencer and nuzzle for the steam and compressed air outlet of baking press machinery) were selected. Then, to prioritize noise control methods based on objectives, criteria, and alternatives, an AHP questionnaire was developed and completed by domain experts and noise control specialists. Data analysis was performed using Expert Choice V. 11 and Excel.

RESULTS

The results showed that the inconsistency rate in all cases was less than 10%, hence the consistency of responses was approved. Based on experts' opinion about the selected criteria, "implementation and maintenance cost" had the highest weight (0.481), while "method effectiveness and efficiency" recorded the lowest one (0.046). With regard to the alternatives, "change in the process" registered the greatest weight (0.193), whereas "individuals' noise exposure time" had the lowest weight (0.046).

CONCLUSIONS

Based on the final weights, the most appropriate noise control methods in this industry are changing processes in machinery with excessive noise generation, forming noise control engineering team, and manufacturing silencer and nuzzle for the steam and compressed air outlet of baking press machinery. Furthermore, AHP is a suitable approach for prioritizing decisions related to noise control.

Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol' sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.

Implementation of the new easy approach to fuzzy multi-criteria decision aid in the field of management

Decision-making is one of the most important management functions and a critical task for managers. The tools that support decision makers in making decisions are Multi-criteria Decision Making/Aid/Analysis (MCDM/MCDA) methods. Since most decisions are made under conditions of uncertainty, the fuzzy MCDM/MCDA methods are particularly important as they allow capturing the uncertainty and imprecision of the information used in making decisions. This method is the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy PROMETHEE), and its extension in the form of New Easy Approach to Fuzzy PROMETHEE (NEAT F-PROMETHEE). However, the unavailability of software using the NEAT F-PROMETHEE method significantly reduces its ease of use and may discourage potential users and researchers considering using it in their studies. Therefore, to facilitate the use of this MCDA method, the article presents the implementation of NEAT F-PROMETHEE in the MATLAB environment. Moreover, the verification of the developed implementation and its application in the management decision-making problem is presented, together with the analysis of the operation of the mapping correction function used in NEAT F-PROMETHEE. The results obtained with NEAT F-PROMETHEE were compared with the results of the Fuzzy PROMETHEE method which did not apply correction. The analysis shows that the correction applied in NEAT F-PROMETHEE allows obtaining results with a smaller error than the non-corrected implementations of PROMETHEE Fuzzy. Therefore, a more accurate solution of the decision problem is obtained.•

improving the process of mapping fuzzy numbers in the Fuzzy PROMETHEE method

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implementing a correction mechanism while mapping trapezoidal fuzzy numbers

Analytical tuning rules for Reduced-order Active Disturbance Rejection Control with FOPDT models through Multi-Objective optimization and multi-criteria decision-making

Active Disturbance Rejection Control (ADRC) emerged as a promising control solution in various engineering domains. However, increased ADRC order makes it difficult to implement and tune in practice. On the other hand, Reduced-order ADRC (RADRC) structure solves this issue with the appropriate tuning of its parameters to achieve the desired performance. This paper aims to develop analytical tuning rules for RADRC for processes approximated as First-order plus dead-time models (FOPDT). These rules meet the conflicting goals of tracking and disturbance rejection restricted by robustness specification. The tuning rules are derived based on a multi-stage approach. In the first stage, the tuning problem is formulated as a multi-objective optimization problem with appropriate constraints. A Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) Algorithm solves the optimization problem to produce a collection of Pareto-optimal solutions (alternatives) in the second stage. In the third stage, using the Best-Worst based PROMETHEE method, the best one is chosen among the available options. Finally, using linear regression, analytical tuning rules are developed. Separate tuning rules are proposed for lag-dominated and dead-time dominated cases. Simulation experiments on benchmark industrial processes are performed, and the findings assess the efficacy of the suggested tuning rules relative to the methods recently published. The proposed tuning rules are experimentally validated to assess their applicability in the practical scenario. Besides, the closed-loop system's stability with the suggested tuning rules is confirmed by the small-gain theorem and the dual-locus process.

Assessing the life cycle study of alternative earth-retaining walls from an environmental and economic viewpoint

This research aims to assess the sustainability of the most common earth-retaining walls (Gravity Walls and Cantilever Walls) in terms of environmental impacts, economic issues, and their combination. Gravity walls observed in this study consist of Gabion Wall, Crib Wall, and Rubble Masonry Wall, while Cantilever Walls include Reinforced Concrete Wall. Six different criteria were taken into account, including global warming potential, fossil depletion potential, eutrophication potential, acidification potential, human toxicity potential, and cost. To achieve the aim of this study, life cycle assessments, life cycle costs, and multi-criteria decision-making methods were implemented. The results showed that the most environmental-friendly option among all alternatives was the Gabion Wall, followed by the Rubble Masonry Wall. However, in terms of economic aspects, the Cantilever Concrete Wall was the best option, costing about 17% less than the Gabion Wall. On the other hand, the results of multi-criteria decision-making showed that the Gabion Wall was the most sustainable choice. This study addressed the research gap by carrying out a sustainability assessment of different retaining walls while considering cost and environmental impacts at the same time.

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment

COVID-19, which emerged in December 2019, has affected the entire world. Therefore, COVID-19 has been a subject of research in various disciplines, especially in the field of health. One of these studies was the report made by the Deep Knowledge Group (DKG) consortium in which safe regions for COVID-19 were determined. In the report, the main criteria of quarantine efficiency, government efficiency of risk management, monitoring and detection, health readiness, regional resilience, and emergency preparedness are used in the evaluation of countries and regions (alternatives). As the data and research structure used in this report are based on multi-criteria, the purpose of this study is to evaluate and analyse the safety levels of 100 regions in the world in terms of COVID-19 using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Complex Proportional Assessment (COPRAS) methods. The data and information required in the methods were obtained from a report prepared by the DKG. The results of the methods were compared with the ranking results presented in a report of the DKG. Accordingly, it has been observed that the method that provides the closest results to the results of the report is the COPRAS method, and the method that gives the most distant results is the VIKOR method.

The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal

Generation of healthcare waste from different patient care activities in hospitals, pathology labs and research centres has been a matter of great concern for environmental and social bodies across the world. This concern comes from its infectious and hazardous nature which brings life taking disease such as human immunodeficiency virus and Hepatitis-B. Moreover, with the outbreak of corona virus disease 2019 (COVID-19) pandemic across the world, healthcare waste has become even more infectious like never before and showing its potential for claiming lives if not disposed properly. Additionally, the COVID-19 has put up another challenge in terms of exponentially increasing demand for personal protective equipments for healthcare workers such as doctors, nurses, ward boys, and sanitation workers. In this paper, seven criteria related to smart healthcare waste disposal system infused by circular economy aspects to recover value from disposables are identified and analysed using a decision making trial and evaluation laboratory (DEMATEL) method. The criteria have been prioritized by its importance and net cause and effect relationship through a causal diagram. Two criteria, (i) digitally connected healthcare centres, waste disposal firms and pollution control board, and (ii) providing a pollution control board's feedback app to public and other stakeholders, feature as strong reasons for a smart healthcare waste disposal system. Conclusively, this study provides a causal relationship model among the intertwined drivers of industry 4.0 and circular economy for developing a smart healthcare waste disposal system enriched with the benefits of circular economy.

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

Multi-criteria risk evaluation model for developing ventilator-associated pneumonia

Ventilator-associated pneumonia is a hospital-acquired infection of the lungs occurring in mechanically ventilated patients. An active risk management approach can prevent the occurrence of the disease and promote positive organizational changes, subsequently decreasing mortality and hospitalization costs. Using scientific and clinical practice knowledge, a risk evaluation model was developed to identify patients more at risk of developing the disease. For this purpose, a Decision Expert qualitative multi-criteria decision method was used, in which alternatives are evaluated according to predetermined hierarchically arranged criteria. Characteristics of each evaluated alternative are described by the members of an interdisciplinary expert team and are represented by the values of the basic criteria. Values of hierarchically higher aggregated criteria are computed in an upwards fashion according to utility functions, which are defined as simple logical rules. This method is integrated into a software solution, DEXi. The approach is applicable to vastly diverse decision problems and has been successfully used before for health-related decision support. The designed model was tested using actual clinical data. Evaluations of alternatives that most distinctly demonstrated the functionality of the evaluation model were selected and are presented in the results. The evaluation model is intended to assist a holistic evaluation of the risk of developing ventilator-associated pneumonia, by considering patient-related risk factors and the use of preventive measures. The model incorporates nursing-specific data that have hitherto been poorly utilized in preventing ventilator-associated pneumonia and promotes the active engagement of nurses in confronting this interdisciplinary healthcare problem, which has gained more prominence with the onset of COVID-19 disease.

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods

CONTEXT AND BACKGROUND

Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers.

OBJECTIVES

Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.

METHODS

The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.

RESULTS

The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.

CONCLUSIONS

This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

Ranking provincial power generation sources of China: a decision-maker preferences based integrated multi-criteria framework

The ranking of power generation sources is a very important prerequisite for power generation installation planning and power supply security. This study proposed a new multi-criteria system for ranking regional power generation sources in one country, including resources, economy, technology, environment, and society, using 11 sub-criteria. Based on the system, a novel decision-maker (DMs) preference-based integrated MCDM framework involving four methods (Visekriterijumsko Kompromisno Rangiranje (VIKOR), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and Weighted Sum Method (WSM)) was developed for ranking six power generation sources (thermal, nuclear, wind, hydro, solar PV, and biomass) at the level of China's 30 provinces. Six different preferences of DMs are considered in the ranking according to five criteria. The results show that wind should be the power generation source given the top priority in most provinces in China whereas nuclear power and thermal power are the last choice for 26 provinces. Biomass is the most preferable power source for 17 provinces based on technological preference in which DMs regard the technology criteria is prior to all other criteria. Thermal power would still the preferred or secondary power source for provinces rich in coal resources such as Shanxi, Inner Mongolia, Henan, and Shaanxi.

Towards identification of important plant areas (IPA) for Peninsular Malaysia. Methodology and future directions

Malaysia is a megadiverse country and listed as one of the world's biodiversity hotspots. Land use changes and deforestation have led to the threat of, and extinction of plant species. In order to mitigate loss in population numbers, and to prevent species extinction events, Important Plant Areas (IPA) for Malaysia shall be identified. The identification of IPA is important to ensure that key natural areas are adequately protected and managed to preserve the species and its habitats. Currently, there are 1771 IPA identified globally and only seven tropical countries are actively involved excluding Malaysia. Inventory and biodiversity research are actively conducted in Malaysia, however, the initiative to identify IPA is still in its infancy. The first attempt for IPA identification was in the state of Terengganu by using herbarium database through scoring technique. In this paper, we discussed methods and criteria used in IPA identification globally. We also deliberated current IPA development in Terengganu and challenges such as collections biases and the need for a robust scoring technique to reduce judgement uncertainty. We suggested GIS based multi-criteria decision making, analytical hierarchy process and species distribution for Malaysian IPA. These strategies were considered to be effective tools in providing decision support for spatial planning aimed at plant conservation in Malaysia.

Norm-dist Monte-Carlo integrative method for the improvement of fuzzy analytic hierarchy process

This paper presents the novel approach of the Norm-dist Monte-Carlo fuzzy analytic hierarchy process (NMCFAHP) to incorporate probabilistic and epistemic uncertainty due to human's judgment vagueness in multi-criteria decision analysis. Normal distribution is applied as the most appropriate distribution model to approximate the probability distribution function of the criteria and alternatives within Monte-Carlo simulation. To test the applicability of the proposed NMCFAHP, the case study of non-destructive test (NDT) technology selection is performed in the Petroleum Company in Borneo, Indonesia. When compared with the conventional triangular fuzzy-AHP, the proposed NMCFAHP method reduces the standard error of mean values by 90.4–99.8% at the final evaluation scores. This means that the proposed NMCFAHP significantly involves fewer errors when dealing with fuzzy uncertainty and stochastic randomness. The proposed NMCFAHP delivers reliable performance to overcome probabilistic uncertainty and epistemic vagueness in the group decision making process.

Catchment-scale soil conservation: Using climate, vegetation, and topo-hydrological parameters to support decision making and implementation

The geomorphometric analysis of watersheds provides useful quantitative information on stream hydrology and potential landscape change that can be used by soil conservation decision makers to determine areas prone to land degradation. In this study, we develop a methodology for the assessment of catchment-scale sensitivity to sediment yield using various topo-hydrological, vegetation, and climatic parameters using four multi-criteria decision making (MCDM) techniques: the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), weighted-sum analysis (WSA), and combined factor (CF). To identify the most important factors affecting sediment yield and soil erosion, a model incorporating principle component analysis with MCDM was devised, using infiltration number (IF), drainage density (Dd), length of overland flow (Lo), channel maintenance (C), stream frequency (Fs), and ruggedness number (Rn) as indices of sediment and erosion risk. Data from a previous study that employed the RUSLE3D model and sediment-yield field data were used to validate the results. The TOPSIS model achieved the highest correlation with the RUSLE3D results. The correlation of watershed activities to the experimental erosion and sediment prioritization results is 0.32. The TOPSIS results indicate that all 23 sub-watersheds yielded moderate amounts of sediment. Based on the VIKOR method, 17.39% (78.96 km²) of the region was classified as having very high erodibility, 26.08% (241.93 km²) high erodibility, 34.78% (225.95 km²) moderate erodibility, and 21.73% (105.05 km²) low erodibility. Considering the high sensitivity of Taleghan watershed to soil erosion, it is recommended that conservation efforts be implemented to minimize land degradation in the area. This methodology can be adapted to other regions that lack detailed topo-hydrological, vegetation, or climatic data.

Multi-criteria decision making for sustainability assessment of boxboard production: A life cycle perspective considering water consumption, energy consumption, GHG emissions, and internal costs

Papermaking is a capital-intensive industry that requires a high consumption of plant fibers, energy, and water. Previous sustainability assessments of papermaking industry primarily focused on separate evaluations for multiple criteria without the integration for criteria and could not compare the overall priority of the production alternatives. The life cycle sustainability for the most representative boxboard production is analyzed as a case study in this work. Life cycle water consumption, energy consumption, greenhouse gas emissions, and internal costs are selected as the assessment criteria. The two multi-criteria decision-making methods are applied to integrate the above criteria to obtain the sustainability sequence under different production pathways. When the papermaking enterprises are regarded as decision-makers, the alternative using waste paper as raw material to manufacture boxboard is the most sustainable, following by mixed fiber. The sustainability sequence of the alternatives using wood and straw as raw materials is controversial due to the different calculation models. Changing the proportion of raw materials and the criteria weights might adjust sustainability sequence of the alternatives.

Investigating power styles and behavioural compliance for effective hospital administration

PURPOSE

The purpose of this paper is to examine the use of power tactics by hospital administrators in order to gain employee compliance. It attempts to understand the influence of power bases of hospital administrators on the employee compliance using an analytic hierarchy process (AHP) technique.

DESIGN/METHODOLOGY/APPROACH

The study adopted a mixed method technique and was conducted in two phases. In the first phase, qualitative analysis was carried out through content analysis of the anecdotes collected from the employees working in tertiary hospitals. Content analysis of responses aided in obtaining a list of criteria and sub-criteria affecting employee behavioural compliance. In the second phase, quantitative analysis was carried out using the AHP technique. While applying AHP, the issue pertaining to employee behavioural compliance with hospital's policies, procedures and related instructions was formulated in form of a hierarchy of one objective, two criteria, six sub-criteria and five alternatives established through literature review and content analysis. Furthermore, the subject matter experts were asked to conduct pairwise comparison wherein priority rankings were achieved.

FINDINGS

The results indicated that reward power (25 per cent) is the most significant power style exercised by effective hospital administrators in achieving employee behavioural compliance followed by expert (24 per cent), referent (22 per cent) and legitimate powers (17 per cent). As coercive (12 per cent) came out to be the least preferred power style, it should be cautiously exercised by hospital administrators in the present day scenario.

RESEARCH LIMITATIONS/IMPLICATIONS

The major limitation of this study is that the sample was drawn only from three tertiary hospitals in Jammu district that limits the generalizability of the findings in all the hospital settings across different regions. No attempt is made in this study to understand the variations with regard to demographics of the respondents that can be taken as a future research study. This study is cross-sectional in nature and provides the perspective of specific time. A longitudinal study could further provide insights into different time variations and the comparison and henceforth can be more comprehensive, thus supporting the generalizability of this study.

PRACTICAL IMPLICATIONS

The study empirically identifies the relative importance of exercising power styles in order to gain employee behavioural compliance. The study helps in understanding the complex problem of behavioural compliance in hospital setting by examining the intensity of each factor affecting employee behavioural compliance. This knowledge is very critical in effective hospital management and getting the work done. The priority rankings obtained for power styles can be used for developing selection batteries and performance records of hospital administrators. As the behaviour of the employees is not static, there may exist the inherent limitations of adopted cross-sectional design for the present study. Furthermore, longitudinal study can be conducted at different time periods, to understand the variations in the patterns of employee's compliance behaviour and associated practiced power styles by hospital administrators.

ORIGINALITY/VALUE

This is perhaps the first study that has scientifically attempted to integrate the power styles and analyzed their effective use in hospital administration. This research study has attempted to develop an elementary base for academicians, scholars as well as management practitioners on the effective use of power styles for achieving employee behavioural compliance in hospitals.