Hybrid Multi-attribute Decision Research Articles

A Hybrid Multi-Attribute Decision-Making Method Considering Tourists' Behavioral Preferences for Customized Tours

A decision-making analysis method considering tourists' behavioral preferences is proposed to address the problem of hybrid multi-attribute decision making (HMDM) for customized tours in which attribute values are intuitionistic fuzzy numbers, interval numbers and real numbers. This method is presented through step-by-step instructions:1) construct an evaluation index system of the HMDM for customized tours; 2) convert the attribute values of different data types into normalized intuitionistic fuzzy numbers, and obtain their objective weights by calculating their intuitionistic fuzzy entropies; 3) use the intuitionistic fuzzy weighted average operator to aggregate all the converted attribute values of each customized scheme;4) calculate scores of aggregation values for customized schemes and rank these schemes accordingly under the scenarios where tourists express such behavioral preferences as caution, mildness and recklessness; and 5) apply the proposed method to an example case to prove its feasibility.

A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach.

The global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern's unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country's performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape.

Multi-objective optimization, shrinkage and fracture properties of unsaturated polyester resin modified concrete for bridge deck pavement based on system theory

For the purpose of fabricating unsaturated polyester modified concrete (UPMC) with excellent shrinkage and fracture properties for bridge deck pavement, the optimization design of mix proportion and enhancement mechanism analysis were implemented based on system theory. Firstly, for unsaturated polyester resin (UPR) subsystem, the viscosity, mechanical properties and droplet-size distribution were analyzed to determine the types and dosage of auxiliary additives after emulsification treatment; Next, for UPMC mortar slave-system, the optimization design was implemented through the combination of experimental analysis and hybrid multi-attribute ellipsoidal decision of grey-target (HMEDG) model; Subsequently, for UPMC main-system, the optimal proportion was determined utilizing orthogonal design test. Finally, the variation law and enhancement mechanism on fracture properties and shrinkage characteristics of UPMC was analyzed by combining macroscopic test, thermogravimetry (TG) and X-ray diffraction (XRD). The results demonstrated that 2.0 wt%BPO+1.0 wt%DMA and meta-benzene types UPR were utilized as auxiliary additives for UPMC based on the results of HMEDG models. The demulsified and cured WUP might supplement the interior moisture losses based on humidity compensation, and substantially constrain the evaporation loss inside the matrix, upgrading the hydration degree and facilitating the generation of hydration products. The introduction of 3 %-6 % waterborne-UPR (WUP) would improve 28d-fracture toughness within approximately 23.33 %-49.32 %, and reduce the shrinkage deformation by roughly 28–43 % through inducing the crack tip stress redistribution against the initiation and propagation of interior microcracks and enhancing the homogenization and densification.

A Hybrid Multi-Attribute Decision-Making Approach Considering Correlations Among Indicators

Considering the fuzzy and uncertainty of the evaluation information of the evaluation object, the type of indicators are expanded into five types of mixed evaluation information, namely, exact number, interval number, triangular fuzzy number, hesitant fuzzy number, and probabilistic linguistic term sets, and different types of evaluation information are adopted according to the different characteristics and types of indicators, respectively. When the evaluation information is hybrid information, the generalized Shapley function based on fuzzy measurement is used to analyze the interaction between indicators and determine the weights of indicators, considering that the interaction between indicators is more complicated. In view of the fact that the conversion of mixed information into the same kind of information will lead to the problem of complicated calculation and missing information, the VIKOR method is used to comprehensively evaluate the evaluation objects and select the best ones. Finally, the validity and feasibility of the proposed method is verified by taking the assessment of data literacy level and competence of teachers in a university in a western province of China as an example.

Nickel-based super alloy grinding optimisation using a hybrid multi attribute decision making method based on entropy, AHP and TOPSIS

ABSTRACT Nickel-based superalloy grinding, as well as selecting the appropriate operational parameters, is one of the major challenges in machining engineering. The conducted research consists of two sections. The first section presents a systematic experimentation, which has been carried out to qualitatively and quantitatively investigate nickel-based superalloy grinding. Vitrified CBN grinding wheels, along with a rotary diamond cup dresser, were utilised to conduct the experiments with the workpiece of Inconel 738. Specific grinding energy, G-ratio and surface roughness were indicated as three major machining outputs, while the inputs were the dressing conditioning and grinding parameters. In the following, the effects of different operational parameters were relatively ranked according to their weighted impact on the grinding performance using hybrid multi-attribute decision-making (MADM) methods. For this purpose, a combination of three techniques: Analytic Hierarchy Process (AHP), Entropy and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was utilised to select the most appropriate grinding conditions, while considering the conflicting effect of the different dressing and grinding parameters. The results indicate that specific energy can be an important criterion for evaluating the process efficiency. The changes in G-ratio and surface roughness have the same trends, as opposed to specific grinding energy mutation. The grinding parameters are summarised in equivalent chip thickness. Our findings show that dresser cross feed rate, along with equivalent chip thickness, has the greatest impact on the grinding process efficiency.

Quality of service attributes based hybrid decision-making framework for ranking cloud service providers under Fermatean fuzzy environment

Cloud Computing has gained substantial popularity due to its ability to offer diverse and dependable computing services suited to clients demands. Given the rapid expansion of this technology, an increasing number of IT service providers are competing to deliver cloud services that are both of excellent quality and cost-efficient, in order to best meet the requirements of their clients. With the extensive range of options available, selecting the best Cloud Service Provider (CSP) has become a challenging dilemma for the majority of cloud clients. When evaluating services offered by many CSPs, it is important to consider multiple attributes. Efficiently addressing the selection of the best CSP involves tackling a challenging Multi-Attribute Decision Making (MADM) problem. Several MADM techniques have been proposed in academic literature for evaluating CSPs. However, the persisting problems of inconsistency, uncertainty, and rank reversal remain unresolved. In this paper the authors present a hybrid MADM framework to rank eight CSPs using nine Quality of Service (QoS) attributes. In order to achieve this objective, Fermatean fuzzy sets-full consistency method (FFS-FUCOM) is combined with Grey-Relational-Analysis and the Technique-for-Order-Preference-by-Similarity-to-Ideal-Solution (Grey-TOPSIS) technique. The framework successfully resolved the aforementioned problems. Sensitivity analysis is conducted to assess the stability and robustness of the results produced by the proposed framework. The sensitivity analysis results indicate that the proposed framework offers an accurate and robust solution. A systematic ranking test is undertaken to ensure that the results are ranked in a systematic manner. Additionally, a comparative analysis is carried out with the most relevant study.

Can socially sustainable development be achieved through homestead withdrawal? A hybrid multiple-attributes decision analysis

Achieving sustainable rural development is essential for countries worldwide to balance development between urban and rural areas; especially, sustainable social development is crucial. In the face of rapid urbanization in China, the withdrawal of rural homesteads (WRH) has become the core policy for attaining sustainable rural development. Compared with the literature that focuses on the economic or environmental impacts of the policy, few studies have evaluated how social sustainability is accomplished through such land-reform policies. Given the consensus that exploring sustainability emphasizes complex causal relationships between multiple dimensions, assessment models must further consider interdependencies. Based on Chinese expertise and perspective, this study proposes a hybrid multi-attribute decision analysis model to evaluate the contribution of WRH policies toward social sustainability. First, the Delphi method was used to build evaluation criteria covering four dimensions—the socio-ecological environment, social welfare, social equity, and social inclusion—and 20 criteria were based on the existing literature. Second, influential network relations maps (INRMs) were constructed based on the fuzzy decision-making trial and evaluation laboratory (DEMATEL), considering complex causal relationships between dimensions and criteria to further identify the key evaluation criteria for the social sustainability of the homestead exit policy. The results show that the five subdimensions are key to achieving sustainable social development through WRH. Based on our results, we propose certain policy recommendations.

Preference-based multi-attribute decision-making method with spherical-Z fuzzy sets for green product design

Green product design, i.e., design that harmonizes with the environment, is a crucial component for addressing environmental considerations in the earliest stages of the product life cycle, e.g., new energy vehicles (NEVs), that minimize negative environmental impacts. The designs can encompass material selection, resource use, production requirements, recycling, reuse and the disposal of products. Selecting the optimal design alternative considering multiple attribute indices, e.g., environmental indicators and functional indicators, is a typical multi-attribute decision-making (MADM) problem. This study proposes a hybrid preference-based MADM method with spherical-Z fuzzy numbers (SZFNs) for solving the green product design fuzzy information problem. SZFNs are designed to mine the internal hidden information of traditional Z-numbers, and they combine the characteristics of the reliability constraints of Z-numbers and the advantages of spherical linguistic sets. The operation, aggregation operators and probabilistic measure method of SZFNs are defined in this study. The weight vector of the criteria is obtained by adopting the degree of possibility of spherical-Z (DPSZ). The hybrid MADM method is developed by incorporating the total utility of spherical-Z, which is used to convert the internal meaning of the evaluation information into an additive ratio assessment using gray relation analysis (ARAS-GRA) to obtain the optimal alternative. Finally, a case study, i.e., five green product design schemes for NEVs, is adopted to verify the effectiveness and robustness of this proposed method. A comparative analysis, sensitivity analysis and comprehensive discussion are conducted in this research. The results confirm that this proposed method has an improved performance, and provides some references for designers.

A hybrid multi-attribute decision-making and data envelopment analysis model with heterogeneous attributes: The case of sustainable development goals

This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes’ weights and the alternatives’ scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments.

Using a Hybrid Multiattribute Decision-Making Model for Evaluating the Sustainable Development Potential of Characteristic Towns and Exploring Development Planning Strategies.

In recent years, with the development of 'China's new urbanization, the "characteristic town movement" with the development of industrial economy first has brought problems to a large number of rural settlements, such as no cultural planning, no consumption of industry, and no soul. Then, in reality, there are still a large number of rural settlements under the planning of the upper-level local government, with the goal of developing into a characteristic town in the future. Therefore, this study believes that there is an urgent need to build a framework for evaluating the construction potential of rural settlements with sustainable characteristic towns. Not only that but also a decision analysis model should be provided for real-world empirical cases. This model needs to cover the assessment of the sustainable development potential of characteristic towns as the goal and the formulation of improvement strategies. This study combines the data collection of current characteristic town development rating reports, applies data exploration technology to extract core impact elements and obtain hierarchical decision rules, integrates expert domain knowledge with DEMATEL technology, and establishes an impact network relationship diagram between core impact elements. At the same time, the representative characteristic town cases are assessed for their sustainable development potential, and the modified VIKOR technique is applied to clarify the actual problems of the empirical cases, in an attempt to determine whether the development potential and development plan of the characteristic town meet the sustainable development needs from the pre-evaluation mechanism.

How, When, &Where temporary hospitals fit in turbulent times: A hybrid MADM optimization in the middle east

Governments have been challenged to provide temporary hospitals and other types of facilities to face the COVID-19 pandemic. This research proposes a novel multi-attribute decision-making (MADM) model to help determine how, when, and where these temporary facilities should be installed based on a set of critical success factors (CSFs) mapped in an uncertain environment. We portray the available facilities for temporary hospitals based on the CSFs that must be considered to make critical decisions regarding the optimal position based on the government’s strategic decision-making process, thus indirectly providing better services and maximizing resources. In relation to earlier work, this research builds upon hybrid Pythagorean fuzzy numbers to find weights in Best-Worst Methods and rank temporary facilities based on evaluation by an area-based method for ranking. Policy implications and future directions are derived.

A TWO-STAGE MATHEMATICAL PROGRAMMING MODEL FOR DISTRIBUTED PHOTOVOLTAIC PROJECT PORTFOLIO SELECTION WITH INCOMPLETE PREFERENCE INFORMATION

With the rapid growth of the solar photovoltaic (PV) market, many distributed PV power projects are introduced to the market. Selecting a rational project investment portfolio is a complex and challenging task for energy enterprises as both financial and non-financial factors of projects are needed to be considered under limited information and resources. This study presents a two-stage hybrid multi-attribute decision-making and integer programming model for distributed PV project portfolio selection. In Stage I, a multiple attribute group decision-making method based on mathematical programming is used to evaluate the non-financial value of projects under incomplete preference information. Compensative weighted averaging operators with an adjustable parameter are utilized to capture the subjective attitudinal character of an expert in the aggregation process. Then, a rank acceptability index is developed to measure each project’s group support degree in non-financial dimension. In Stage II, a bi-objective integer programming model is constructed to optimize project portfolios, which considers both financial and non-financial values of projects under resource, carbon emission and other strategic constraints. The applicability and effectivity of the proposed approach are demonstrated by a case study of a distributed PV project portfolio selection.

Making Informed Decisions to Improve Restaurant Image Using a Hybrid MADM Approach: A Case of Fast-Food Restaurants in an Island of East Malaysia

Restaurant image refers to an immediate perception that pops up in a customer’s mind when the name of a restaurant is mentioned. Therefore, it is crucial for restaurants, including fast-food restaurants (FFRs), to evaluate and sustain a positive restaurant image. However, evaluating and improving a restaurant’s image is challenging, since it counts in multiple service attributes associated with various degrees of unknown priority. Even so, the existing literature is yet to outspread the usage of an appropriate multi-attribute decision-making (MADM)-based approach to specifically evaluate the image of FFRs. Therefore, this research aimed at employing such an approach to evaluate the image of four FFRs on an island in East Malaysia, using various people, processes, and physical evidence attributes. Firstly, an initial list of FFR image attributes was elicited from the available literature. This initial list was then further validated through a two-round Delphi survey involving a panel of ten experts. A questionnaire was then designed based on the finalized attributes, and data collected from a sample of 251 respondents were analyzed using the compromised-analytical hierarchy process (C-AHP) method. The C-AHP results suggest that the strategies to improve an FFR’s image should primarily incorporate the following six attributes: hospitality, employees’ problem-solving skills, employees’ knowledge, food taste, physical cleanliness, and service response time. The FFR at the top of the ranking has the highest performance scores over these same six attributes. Surprisingly, employees’ appearance and restaurant exterior were reported as the two least important image attributes. This research is the first to demonstrate the application of a hybrid MADM-based approach to uncover the weights of FFR image attributes and rank those FFRs by computing their aggregated image scores.

Assessing the Adaptation of Internet of Things (IoT) Barriers for Smart Cities’ Waste Management Using Fermatean Fuzzy Combined Compromise Solution Approach

This study proposes a systematic methodology of the adoption of Internet of Things (IoT) barriers (IoTBs) that exist in the waste management structures of smart cities (SCs) in growing economies likely India. A hybrid multi-attribute decision-making (MADM) is applied and has recognized 15 IoTBs from literature and experts opinions obstructing the IoT adoption in SCs of India. The different IoTBs are studied using the similarity measure-based new weighting approach, and the combined compromise solution (CoCoSo) method. Considering that Fermatean fuzzy sets (FFSs) can represent this uncertainty, this paper proposes a decision-making framework for waste management system solutions based on the FFSs and builds a complete evaluation index system. Herein, we first combine the Archimedean Copula operations and Archimedean operations and term them as ‘generalized Archimedean Copula operations’ for FFSs. Based on these new operations, we develop the Fermatean fuzzy generalized Archimedean copula weighted averaging (FFGACWA) and Fermatean fuzzy generalized Archimedean copula weighted geometric (FFGACWG) operators. Then, we construct a decision algorithm based on FFWGAAC and FFWGGAC operators. Second, we propose new weighting procedure based on similarity measure to discuss the significance degree of IoTBs. Further, we apply this method to the evaluation and selection of a methodology of IoTBs in smart cities’ waste management (SCWM) assignments, and prove the effectiveness of this method. The algorithm can represent the Fermatean fuzzy information in a complex environment. It cannot only consider the uncertainty of the decision-makers (DMEs) when giving the evaluation value but also synthesize the relationship between any numbers of evaluation criteria. Finally, the superiority of the methodology is discussed by sensitivity analysis and comparative study. The results show that the method can effectively handle the decision-making problems in complex environments. This paper will assist the representatives, stakeholders and government to know the importance of IoTBs affecting waste management processes, and it will certainly help them to take judgments for exterminating the IoTBs for an effective IoT employment in SCWM assignments.

Selecting a green supplier utilizing the new fuzzy voting model and the fuzzy combinative distance-based assessment method

• In this paper, the selection of a Green Supplier is performed using a new hybrid Multi Attribute Decision Making-Data Envelopment Analysis method. • The fuzzy version of the Preferential Voting model is designed in this paper. • A new secondary goal model using the Combinative Distance-based Assessment method is proposed in the Cross Efficiency ranking method. • The proposed new method for Selecting a Green Supplier in an Automotive Group has been implemented. Significant increase in environmental concerns has led organizations to consider the necessary measures to apply green supply chain management to improve their environmental and economic performance. An important way to implement green supply chain management is to revise the methods of purchasing and selecting green suppliers. In this paper, a new fuzzy Data Envelopment Analysis (DEA) model for green supplier selection by collecting expert votes is presented. Then a way to improve the cross-efficiency method to achieve a complete ranking is suggested. In the improved method, a new secondary goal model based on the concepts of the Fuzzy COmbinative Distance-based ASsessment (CODAS) method will be presented. The proposed models were then used to select a green supplier in an automotive group. In the present case study, a complete ranking for green suppliers is obtained.

Evaluating smart office buildings from a sustainability perspective: A model of hybrid multi-attribute decision-making

This study proposes a framework that contains four dimensions (environment, economy, socio-cultural, and technology) and nine relevant criteria for evaluating smart office buildings through a hybrid multi-attribute decision-making (MADM) model. A Taiwanese case study of the silver-level label office building is evaluated within this study. Using the Decision making trial and evaluation laboratory (DEMATEL) (di + ri = central degree, di-ri = cause degree) this study reveals that the core dimension for evaluating smart office buildings more sustainable is the technological dimension (D4) (34.007, 0.967), followed by the economic dimension (D2) (38.725, 0.175) and the socio-cultural dimension (D3) (37.743, 0.047); improvements to these three dimensions drives the environmental dimension (D1) (48.694, −1.190). In terms of the criteria, automation (C8) (19.328, 0.587), interactivity (C9) (14.679, 0.380), waste management (C3) (13.884, 0.300), architectural innovation (C7) (18.882, 0.226) and affordability (C4) (19.070, 0.201) are five critical criteria. Although the performance of the Taiwanese case study is, in terms of its sustainability, acceptable (Total performance = 9.530 and the gap ratio to aspiration level = 0.046) based on the modified VlseKriterjumska Optimizacija i Kompromisno Resenje (VIKOR), there is still room for further improvements to be made in terms of sustainability.

An integrated approach to joint production planning and reliability-based multi-level preventive maintenance scheduling optimisation for a deteriorating system considering due-date satisfaction

This paper presents a new bi-objective model to deal with an integrated production planning and reliability-based multi-level preventive maintenance (PM) scheduling problem. The production system includes a set of parallel deteriorating machines. The model aims to find the production lot sizes and the sequence and interval of PM activities over a multi-shift planning horizon. The PM activities are classified as the adjustment and replacement ones, each of which has a specific cost and effect on machines. The model serves to minimise the total cost while maximising customer satisfaction. Also, the most profitable customers are identified by applying a hybrid multi-attribute decision making (MADM) approach (BWM-TOPSIS) in order to fulfil their orders in their desirable time windows. Three efficient meta-heuristic algorithms are developed to solve the proposed model for large-scale problems. Besides, TOPSIS method is employed to select the most desirable solution among the obtained Pareto solutions. Finally, a case study is provided to show the applicability of the proposed approach.

A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem

This article intends to study the dynamic hybrid multi-attribute decision making (DHMADM) process, where the choice information is given in two-scale intuitionistic fuzzy numbers (IFNs) as well as interval-valued intuitionistic fuzzy numbers (IVIFNs) by the decision-makers (DMs) at distinct periods. We have contributed some new dynamic weighted aggregation operators (AOs), namely dynamic intuitionistic fuzzy Dombi weighted average (DIFDWA), weighted geometric (DIFDWG) operators, and uncertain dynamic intuitionistic fuzzy Dombi weighted average (UDIFDWA), weighted geometric (UDIFDWG) operators for interval uncertainty. Next, we have obtained the aggregated data by using these dynamic Dombi operators. Then, we apply two grey relational analysis (IF-GRA and IVIF-GRA) approach on the aggregated matrix to obtain the overall grey relational degree for each option from positive and negative ideal (PIA and NIA) alternative. Then, we evaluate each choice’s relative relational degree from the positive to get the final rank. Finally, we demonstrate the proposed model with a numerical example to compare the proposed model for applicability and validity.

A New Approach for Ranking Shadowed Fuzzy Numbers and its Application

In many decision situations, decision-makers face a kind of complex problems. In these decision-making problems, different types of fuzzy numbers are defined and, have multiple types of membership functions. So, we need a standard form to formulate uncertain numbers in the problem. Shadowed fuzzy numbers are considered granule numbers which approximate different types and different forms of fuzzy numbers. In this paper, a new ranking approach for shadowed fuzzy numbers is developed using value, ambiguity and fuzziness for shadowed fuzzy numbers. The new ranking method has been compared with other existing approaches through numerical examples. Also, the new method is applied to a hybrid multi-attribute decision making problem in which the evaluations of alternatives are expressed with different types of uncertain numbers. The comparative study for the results of different examples illustrates the reliability of the new method.

A New Approach for Ranking Shadowed Fuzzy Numbers and its Application

In many decision situations, decision-makers face a kind of complex problems. In these decision-making problems, different types of fuzzy numbers are defined and, have multiple types of membership functions. So, we need a standard form to formulate uncertain numbers in the problem. Shadowed fuzzy numbers are considered granule numbers which approximate different types and different forms of fuzzy numbers. In this paper, a new ranking approach for shadowed fuzzy numbers is developed using value, ambiguity and fuzziness for shadowed fuzzy numbers. The new ranking method has been compared with other existing approaches through numerical examples. Also, the new method is applied to a hybrid multi-attribute decision making problem in which the evaluations of alternatives are expressed with different types of uncertain numbers. The comparative study for the results of different examples illustrates the reliability of the new method.