Full Multiplicative Form Research Articles

A two-stage framework for site selection of deep-sea offshore wind-to-hydrogen projects based on GIS - MULTIMOORA method under Pythagorean triangular fuzzy environment

Constructing deep-sea offshore wind-to-hydrogen projects (DS-OWHP) is crucial to solve the problem of offshore electricity storage and consumption. Addressing the uncertainty of the decision-making environment, and the lack of a comprehensive indicator system, this paper proposes a two-stage decision framework based on Geographic Information System (GIS). In the first stage, GIS is used to analyze the suitability of study area and identify alternative sites. In the second stage, the fuzzy MCDM method is used to further optimize alternative sites. Firstly, a comprehensive evaluation indicator system is established, and the Pythagorean Triangular Fuzzy Number (PTFN) is used to describe evaluation information. Then, a combination of stepwise weight assessment ratio analysis (SWARA), entropy, and game theory is used to determine weights, and the Multi-Objective Optimization on the basis of a Ratio Analysis plus the Full Multipli-cative form (MULTIMOORA) method is used to rank alternative sites. Finally, the feasibility of framework is proved through a case study in Guangdong Province. The results show that average wind speed has the highest weight of 0.149, and alternative site A2 which is close to zhuahai is the optimal choice. Through sensitivity analysis and comparative analysis, the reliability and stability of proposed model is verified.

Modelling the conundrums to cyber-risks management in logistics firms for supply chain social sustainability

PurposeCybersecurity has received growing attention from academic researchers and industry practitioners as a strategy to accelerate performance gains and social sustainability. Meanwhile, firms are usually prone to cyber-risks that emanate from their supply chain partners especially third-party logistics providers (3PLs). Thus, it is crucial to implement cyber-risks management in 3PLs to achieve social sustainability in supply chains. However, these 3PLs are faced with critical difficulties which tend to hamper the consistent growth of cybersecurity. This paper aims to analyze these critical difficulties.Design/methodology/approachData were sourced from 40 managers in Nigerian 3PLs with the aid of questionnaires. A novel quantitative methodology based on the synergetic combination of interval-valued neutrosophic analytic hierarchy process (IVN-AHP) and multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA) is applied. Sensitivity analysis and comparative analysis with other decision models were conducted.FindingsBarriers were identified from published literature, finalized using experts’ inputs and classified under organizational, institutional and human (cultural values) dimensions. The results highlight the most critical dimension as human followed by organizational and institutional. Also, the results pinpointed indigenous beliefs (e.g. cyber-crime spiritualism), poor humane orientation, unavailable specific tools for managing cyber-risks and skilled workforce shortage as the most critical barriers that show the highest potential to elicit other barriers.Research limitations/implicationsBy illustrating the most significant barriers, this study will assist policy makers and industry practitioners in developing strategies in a coordinated and sequential manner to overcome these barriers and thus, achieve socially sustainable supply chains.Originality/valueThis research pioneers the use of IVN-AHP-MULTIMOORA to analyze cyber-risks management barriers in 3PLs for supply chain social sustainability in a developing nation.

Ranking and Challenges of Supply Chain Companies Using MCDM Methodology

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Identifying and modelling the barriers to returns of end-of-life (EOL) heavy-duty machinery in Nigeria: A consumers’ perspective

EOL product returns has become more prevalently practiced in companies in developed nations as a response to government's strict regulations and customers' expectations. Correspondingly, there is a growing body of theoretical literature on the topic within this context as a crucial strategy to accelerate the development of the circular economy (CE). However, consumers' participation in EOL returns of heavy-duty machinery is immature in developing nations due to barriers, and studies are scarce on the analysis of the barriers. To fill this gap, this study aims to identify and analyze the barriers that hinder consumers' participation in EOL returns of heavy-duty machinery within a developing nation-Nigeria. Barriers were identified from published literature and experts' inputs and then, classified under technological, organizational, and environmental (supply chain) categories. Then, analysis was done via a methodology integrating interval-valued neutrosophic analytical hierarchy process (IVN-AHP) with multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA). Findings indicate that the environmental(supply chain) barriers rank the highest, followed by technological barriers. Specifically, consumers participation in EOL returns of heavy-duty machinery in Nigeria is mostly hindered by lack of regulations, lack of financial incentives, lack of skills, undeveloped reverse logistics system and poor access to facilities for recovery operations. Furthermore, we proposed some strategies to support stakeholders to mitigate the barriers. Thus, this study gives valuable guidelines to decision-makers to enable the transition to a CE paradigm in the heavy-duty sector within developing nations for utmost sustainability.

An integrated method for shared power bank supplier selection based on linguistic hesitant fuzzy multi-criteria group decision making

With the increasing number of smartphone users, the potential of the shared power bank industry is increasing. The shared power bank supplier selection can be regarded as a multi-criteria group decision making (MCGDM) problem with linguistic hesitant fuzzy (LHF) sets (LHFSs). This paper proposes a linguistic hesitant fuzzy MCGDM method based on the MULITMOORA (Multi-Objective Optimization on basis of a Ratio Analysis plus the Full Multiplicative form), the BWM (best and worst method), and the prospect theory (PT). Firstly, a new comparison approach of LHFSs is proposed. We propose the LHF Bonferroni mean (BM), the LHF weighted BM and the LHF generalized weighted BM (LHFGWBM) operators. The decision makers’ weights are acquired via a tri-objective optimization model. This paper extends the BWM to obtain the subjective criteria weights and employs the entropy to acquire the objective criteria weights. The comprehensive criteria weights are generated by the Jenson-Shannon divergence. The LHFGWBM operator is used to generate the collective decision matrix. The comprehensive prospect value decision matrix is obtained. Then, we propose the PT-based MULTIMOORA method for LHF MCGDM. An example of shared power bank supplier selection is used to show the superiorities of the proposed LHF MCGDM method.

An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization

Having the capacity to effectively manage multi-criteria decision-making (MCDM) issues with considerable reliability is a crucial prerequisite for any MCDM model. This study introduces an advanced MCDM model that integrates logarithmic percentage change-driven objective weighting (LOPCOW), multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA), and density-based spatial clustering of applications with noise (DBSCAN). The goal is to provide robust decision support. Specifically, the model incorporates a machine learning tool, namely, grid search, to optimize the parameters of neighborhood radius (ε) and the minimum number of points, which significantly influence the clustering quality of DBSCAN. Subsequently, the cluster centers determined by DBSCAN serve as initial points for k-means clustering to negate the impact of outliers. This approach addresses the failures of DBSCAN in nearest neighbor search and parameter selection, and it also resolves the limitations of k-means related to the uncertainty in cluster outcomes due to varying initial centroid selections. Utilizing a real case study from the European Union, comprehensive comparisons of the model results demonstrate the adaptability, reliability, and stability of the proposed model, affirming its effectiveness in providing credible policy recommendations and practical decisions. Overall, this study presents policymakers, professionals, and administrators with a reliable decision-making system for conducting real-world MCDM tasks with enhanced confidence and robustness.

An integrated group decision-making framework for assessing S3PRLPs based on MULTIMOORA-WASPAS with q-rung orthopair fuzzy information

Sustainable third-party reverse logistics has gradually risen to prominence as a component of contemporary commercial development as a result of the acceleration of global economic integration and the prominent growth of information technology in the logistics industry. In the procedure of sustainable third-party reverse logistics providers (S3PRLPs) selection, indeterminacy and conflict information bring great challenges to decision experts. In view of the significant superiority of q-rung orthopair fuzzy (q-ROF) set in expressing uncertain and vague assessment information, this essay designs a comprehensive assessment framework through merging the best and worst method (BWM), Multiplicative Multi-objective Optimization by Ratio Analysis with Full Multiplicative Form (MULTIMOORA) and weighted aggregated sum product assessment (WASPAS) method to address the S3PRLPs selection issue with entirely unknown weight information under q-ROF setting. Firstly, we present a novel score function for comparing q-ROF numbers after analyzing the inadequacies of previous works. Secondly, the q-ROF Frank interactive weighted average (q-ROFFIWA) and q-ROF Frank interactive weighted geometric (q-ROFFIWG) operators are advanced based on the constructed operations to take into consideration the interactive impact of information fusion procedure. Thirdly, the q-ROF-MULTIMOORA-WASPAS decision framework is built based on novel score function and the developed operators, in which the synthetic weights of the criterion are determined by the modified BWM and entropy weight method to reflect both the subjectivity of the decision expert and the objectivity of the decision information. Ultimately, an empirical example was used to evaluate S3PRLPs to demonstrate the applicability and feasibility of the developed methodology, and a comparative analysis was conducted with other existing methods to highlight its advantages in dealing with complex decision problems. The discussion from the research indicates that the developed methodology can be used to evaluate S3PRLPs and further improve the quality of logistics services for organizations.

Assessing and Clustering Countries Based on COVID-19 and Related Indicators: Clustering and MULTIMOORA Approaches

The COVID-19 pandemic has been one of humanity's most difficult times. The pandemic spread and impact were not at the same level for all countries. Investigation of the variation of the countries is crucial for policymakers. Therefore, the study proposed to cluster countries according to the number of COVID-19 cases, deaths, vaccinations and related socioeconomic, disease, and health risk factors and rank them by using MULTIMOORA (MOORA plus the full multiplicative form) in an integrated way. The data set consists of 148 countries and 13 indicators. K-Means algorithm was used to cluster countries. Optimal cluster was found as six according to Silhouette Index. The cluster consisted of mostly developed countries ranked as best perform cluster. It had the highest number of COVID-19 vaccinations, GDP per capita, share health expenditure in GDP, life expectancy, elderly population portion, and environmental performance index values, and the least mortality of chronic diseases. Moreover, Norway, Iceland, and Denmark were the best-performing countries in this cluster. In addition to this, Turkey was located in the second-ranked cluster. It was also determined that COVID-19 indicators (cases, deaths, and vaccinations) were related to GDP per capita, environmental index, and life expectancy. As a result, policymakers can develop pandemic policies for country groups separately, and assistance can be provided in this regard according to the priority order of the countries.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Multi-criteria group decision making through full multiplicative form under hesitant Fermatean fuzzy environment

Multi-criteria group decision making through full multiplicative form under hesitant Fermatean fuzzy environment

Incorporating artificial intelligence in detecting crop diseases: Agricultural decision-making based on group consensus model with MULTIMOORA and evidence theory

The continuous advancements in artificial intelligence (AI) technology are facilitating its widespread integration across various sectors, prominently within agriculture. This evolving synergy is reshaping farming practices, specifically by innovating intelligent mechanisms to monitor and combat crop diseases through intelligent decision-making. Wheat assuming particular significance on the global agricultural stage. Addressing the concerning spread of the wheat soil-borne mosaic (WSBM), this paper employs modern methodologies to gauge the severity of virus infection in wheat through quantitative evaluation. Our contribution lies in the exploration of a group consensus model method within the context of interval type-2 fuzzy sets (IT2FSs). Navigating the intricacies of decision makers (DMs) reaching consensus, we address the challenge of selecting a consensus degree threshold by introducing random variables. Experimentally determining an objective consensus degree threshold. Then, we combine the Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form (MULTIMOORA) method with Dempster-Shafer (D-S) evidence fusion theory. This result in an evidence fusion decision-making model rooted in IT2F information, offering enhanced stability. Leveraging the MULTIMOORA method for decision-making, we then fuse the outcomes through D-S evidence fusion theory, yielding stable decision results. Subsequently, we validate the feasibility of our established decision model within the context of WSBM. In conclusion, we subject it to sensitivity and comparative analyses, comparing it with other methods. This thorough evaluation aims to validate the effectiveness and feasibility of our proposed approach, providing insights into its potential applications and contributions in the realm of agricultural decision-making.

Evaluation of business strategies based on the financial performance of the corporation and investors' behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: A real case study

Revealing the complex relationship between corporation-chosen strategies and financial performance is crucial in navigating today's dynamic business landscape. This research delves into the profound impact of business strategies on both corporation performance and investors' behavior, seeking to identify the strategy that yields the highest returns and efficiency for the corporation and its beneficiaries (investors, loan-holders, and stock-holders). This study proposes a multi-criteria decision-making (MCDM) model that integrates the distance correlation-based criteria importance through inter-criteria correlation (D-CRITIC) weighting method with multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTI-MOORA) technique to evaluate the business strategies of corporations (i.e., defenders, analyzers, reactors, and prospectors). Notably, fuzzy MULTI-MOORA methodology is employed to account for uncertainty, addressing vague, imprecise, and ambiguous input and knowledge. This study is implemented on a real case of the Tehran Stock Exchange on a sample of eighty companies from 2017 to 2021. The study's results underscore the medium analyzers strategy as the optimal alternative among various business strategies, assuming the maximization of benefits for all beneficiaries, and simultaneously specify a prevalent preference for analyzer strategies within Iran's economic landscape. To validate the proposed method, the findings are compared to the other MCDM techniques such as technique for order performance by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of simple ratio analysis (MOOSRA), simple additive weighting (SAW), viekriterijumsko kompromisno rangiranje (VIKOR), and Taxonomy. Additionally, applying principal component analysis (PCA) as a visualization approach demonstrates the satisfactory performance of the proposed framework. Beyond its scientific contributions, this research holds practical managerial applicability, providing actionable insights for informed decision-making across various fields.

Analyzing the response distribution of DO concentration and its environmental factors under the influence of typhoon rain events with remote sensing

Typhoon rain events are important factors that trigger changes in dissolved oxygen concentrations in watersheds. The direction of the typhoon driving force is clear, but the mode of action and mechanism are complex. Moreover, quantifying the relationship between these actions and dissolved oxygen is challenging. This study collected measured data from water quality monitoring and remote sensing during the 2022 typhoon rain events. By analyzing the changes in typhoon driving factors and dissolved oxygen (DO) concentrations in water under various typhoon storms, extended MOORA plus the full multiplicative form (MULTIMOORA), Multiscale Geographic Weighted Regression (MGWR), and spatial autocorrelation analysis were used to evaluate the response of DO concentration. Furthermore, the effects of the atmospheric environment under the influence of human activities on the response distribution of the urban water environment were analyzed. The results of the study showed that under the effect of a typhoon with higher rainfall intensity, the response of DO concentration in the water body of the river in the center of the city was better. However, the response of DO concentration in the water body at the mouth of the sea had a tendency to become worse. Under the influence of typhoon rain events with smaller intensity, the scouring effect of rainwater dominated, and the DO concentration response in the water body had a tendency to become worse. The analysis of spatial heterogeneity under the influence of human activities showed that the ranking values of DO concentration response in rivers in the city area of Zhongshan, under the influence of typhoon rain events, were positively correlated with the distribution of ozone (O3) concentration and sulfur dioxide (SO2) concentration in the eastern, central, and western parts of Zhongshan. Conversely, it was negatively correlated with the distribution of O3 concentration and SO2 concentration in the northern and southern parts of Zhongshan. Based on the research results, we constructed a technique to evaluate the response of dissolved oxygen concentration during the typhoon transit period, which can provide an indicator reference for urban managers in water environment management.

A sustainability evaluation framework for the urban energy Internet using the Fermatean fuzzy Aczel-Alsina hybrid MCDM method

The evaluation of urban energy internet (UEI) is crucial for the establishment of low-carbon cities. This paper aims to present a hybrid decision-making framework to address UEI evaluation problems from a sustainability perspective. First, the Fermatean fuzzy Aczel-Alsina power aggregation operators are proposed to aggregate evaluation information, and the remarkable properties of these operators are explored. Then, the combined weight of the criteria is determined through the decision-making trial and evaluation laboratory method and the Shannon entropy-based power weighted method within the Fermatean fuzzy circumstance. Moreover, the classic multiobjective optimization by ratio analysis plus the full multiplicative form method is extended using the proposed aggregation operators. Finally, an index system was constructed from a sustainability perspective, and the level of development of the four UEI projects is assessed using the proposed method. The experimental results demonstrate that the proposed method can provide beneficial guidelines for the construction and development of the UEI. Furthermore, the reliability and effectiveness of the proposed decision-making framework are validated through sensitivity analysis and a comparative study with existing MCDM methods.

Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA

The motivation for sustainable urban mobility is driven by the need to create environmentally friendly, socially inclusive, economically viable, and healthier cities for current and future generations. It requires a comprehensive approach that combines infrastructure development, policy changes, behavioral shifts, and technological advancements to transform urban transportation systems. The importance of creating increasingly sustainable urban transportation is now greater than it has ever been. One of the attempts in this field is the sustainable urban transport index (SUTI) developed by the United Nations (UN). Although the SUTI has a comprehensive pool of criteria for the evaluation of mobility sustainability of cities, considering the criteria weights as equal and disregarding the uncertainties present in expert opinions is viewed as a significant drawback. To satisfy the aforementioned deficiency, a novel integrated approach including the best-worst method (BWM), multi-objective optimization by ratio analysis (MOORA) and MOORA plus full multiplicative form (MULTIMOORA) method is develloped and applied for the SUTI evaluation. In order to manage ambiguous linguistic approximations, the utilization of BWM is applied within the framework of interval type-2 fuzzy (IT2F) sets. While IT2F-BWM is used to prioritize the main and sub-indicators of SUTI, MULTIMOORA approach is applied to rank the Asian cities. The findings imply that the proposed approach not only provides a more scientific perspective but also provides the opportunity to perform sensitivity analysis on criteria weights. Therefore, the results obtained in this study are expected to help cities to create a road map for planning sustainable urban mobility (e.g. Sustainable Urban Mobility Plan).

Evaluation of employment quality of college graduates based on interval MULTIMOORA with combination weights

PurposeThe purpose of this paper is to develop a novel interval Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form(MULTIMOORA) with combination weights to evaluate the employment quality of college graduates, where the criteria are expressed by interval numbers and the weights of criteria are completely unknown.Design/methodology/approachFirstly, considering the subjective uncertainty of the weights of the criteria, the interval best worst method (I-BWM) was present to determine the subjective weights of the criteria. Secondly, by the improved interval number distance measure, an improved interval deviation maximization method (I-MDM) was introduced to detemine the objective weights. In the following, based on the I-BWM and the improved I-MDM, a combination weighting method that takes into account the subjective and objective weights is proposed. Finally, a multi-criteria decision-making method based on the interval MULTIMOORA with combination weights is present to evaluate the employment quality of college graduates, and then a comparative analysis with some of the existing distance measures of interval numberswas conducted to illustrate the flexibility.FindingsAccording to the data of the Report on Employment Quality of Chinese College Graduats released by Mycos Research Institute in 2016–2020 and 2021–2022, the proposed method was used to evaluate the employment quality of college graduates during the period before and after the COVID-19 epidemic. The results verify that the method is more reasonable because the subjective and objective weights of the criteria can be fully considered. Finally, the feasibility and practicability of the proposed method are further verified by varying parameters.Originality/valuePresent an evaluation method on the employment quality of college graduates based on the Interval MULTIMOORA with combination weights considering the subjective and objective weights. And the proposed method is proved that it can provide a more reasonable evaluation results. At the same time, it is verified that the feasibility and the practicability of the proposed method are affected by varying parameters in the paper.

Evaluation of dynamic technological innovation capability in high-tech enterprises based on pythagorean fuzzy LBWA and MULTIMOORA

With the rapid development of science and technology, high-tech enterprises need to constantly carry out technological innovation to adapt to the changes in the external environment, and maintain their competitive advantages. However, the current research on technological innovation of high-tech enterprises is carried out from a static perspective, which is difficult to understand the dynamic evolution process of continuous technological innovation of high-tech enterprises in a turbulent environment. Therefore, this paper studies high-tech enterprises’ dynamic technological innovation ability from a dynamic perspective, through literature reading and the investigation of the technological innovation status of high-tech enterprises, the evaluation index system of 12 indicators under three dimensions is constructed. The multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA) –Level-based weight assessment (LBWA) comprehensive evaluation model based on Pythagorean fuzzy number (PFN) is proposed to evaluate the dynamic technological innovation ability of high-tech enterprises. Finally, the accuracy and reliability of the model are verified by case analysis. The result of this study shows that the ability to identify new technological knowledge and information outside the enterprise, the ability to obtain technological innovation resources, and the ability to strengthen the input of innovation resources are important factors for the dynamic technological innovation capability of enterprises, so enterprises should pay more attention from these aspects. This study provides a new comprehensive evaluation model and evaluation results can help the decision-makers find their strengths and weaknesses in time and improve them, to promote the sustainable development of high-tech enterprises.

A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development

This study explores how to enhance the decision-making processes in the phases of idea generation or alternative selection during the new product development (NPD) process. NPD is acknowledged as the central function of businesses in an increasingly competitive marketplace. In the current era, the highly uncertain and rapidly changing market environment makes the NPD extremely vague and complex. To be able to find a suitable solution to this complexity, the proposed research aims to categorize the decision points in the process of NPD regarding software development and identify the fuzziness elements affecting the process. The goal of a decision-making process is to prioritize several alternatives with respect to some required objectives and to select the best among them. Multi-Criteria Decision-Making (MCDM) may support reaching a consensus judgment with the collective assessment of Decision Makers (DMs). We introduce a novel evaluation approach for this problem. The proposed approach employs a MULTIMOORA (Multi-objective Optimization by Ratio Analysis plus the Full Multiplicative Form) MCDM technique under Pythagorean Fuzzy Sets (PFSs) objective world environment to cope with an ambiguous environment using Group Decision Making (GDM) setting to shape the decisions. The PFSs have demonstrated their advantages in dealing with vagueness and uncertainty over crisp, fuzzy, or intuitionistic fuzzy sets. Therefore, PFSs can represent the DMs’ judgments and preferences in a better structure, ensuring enhanced decision-making in a group consensus. A case study on gaming software and app development is presented to validate the functionality of the proposed method. The results are compared and assessed with the help of a sensitivity analysis. This research makes a real contribution to the literature by proposing a novel evaluation method to rate and select NPD (gaming software and apps) to deal with the inexactness and vagueness associated with the criteria and alternatives.

Collaborative Fuzzy Linguistic Learning to Low-Resource and Robust Decision System Based on Bounded Rationality

Low-resource languages are challenging to process intelligent decision systems due to limited data and resources. As an effective way of processing low-resource languages in intelligent decision systems, fuzzy linguistic approaches excel in transforming original uncertain linguistic information into highly structured data and learning valid decision rules between complex data structures. However, existing fuzzy linguistic methods may not fully capture realistic features of multi-attribute group decision-making (MAGDM), such as incomplete and hesitant linguistic expressions, stable information fusion, and bounded rationality of decision-makers (DMs). Therefore, it is necessary to develop a collaborative fuzzy language learning system based on bounded rationality, low-resource and robust decision-making. Specifically, we present a new multi-granularity (MG) group decision-making (GDM) scheme by using MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and PT (Prospect Theory) for incomplete hesitant fuzzy linguistic information systems (I-HFL-ISs), where MG GDM aims to discover knowledge from complex MAGDM problems with MG features. To achieve the above goal, we first introduce the concept of MG-I-HFL-ISs to represent incomplete, hesitant and imprecise linguistic evaluation information offered by multiple decision-makers (DMs). Then, we apply a valid transformation scheme to convert MG-I-HFL-ISs into MG-HFL-ISs, and use the MG probability rough set (PRS) to develop a series of MG-HFL-PRSs with the support of MULTIMOORA. Afterwards, an HFL MG GDM method is designed by integrating MULTIMOORA and PT for solving MAGDM problems with MG-I-HFL-ISs. The proposed method can effectively synthesize low-resource languages and mine useful decision-making knowledge. At last, a drug selection case and a simulated case are performed for showing the rationality of the designed HFL MG GDM scheme.

Incomplete intuitionistic fuzzy behavioral group decision-making based on multigranulation probabilistic rough sets and MULTIMOORA for water quality inspection

Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs’ psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studies.