Multi-attribute Decision-making Model Research Articles

Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models

ABSTRACT This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations.. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.

Uncertain interval TOPSIS and potentially regrettable decisions within ICT evaluation environments

It is generally assumed that the rankings provided by Multi-Attribute Decision-Making (MADM) techniques are definitive. Once the ranking is delivered, decision makers (DMs) are expected to choose the first alternative and dismiss the remaining ones, concluding the application of the corresponding model. The MADM literature has incorporated fuzziness and imprecision to its models to deal with evaluation uncertainties but has not accounted for its consequences defined in terms of regrettable choices. That is, MADM models do not consider the possible consequences of having chosen an alternative whose actual characteristics do not correspond to those expected by the DM. This paper aims at designing an integrated MADM framework with interval variables where the DM is allowed to modify the initial alternative chosen after observing the realizations of its characteristics. In order to do so, sequences of alternatives including the initial choice as well as subsequent alternate choices should be ranked in place of single alternatives. We analyze the combinatorial decision environment that arises from defining and evaluating sequences of choices by accounting for the whole set of potential realizations and any subsequent change in the alternatives selected. The TOPSIS method is used to design the integrate evaluation framework producing the final ranking. A case study analyzing the entry decision of a firm within a group of European countries based on their levels of ICT development is presented. We illustrate how the countries selected and their order may differ substantially when accounting for the complementarities existing among them. Moreover, the selection process and any subsequent decision vary with the number of modifications considered relative to the initial country selected. The results obtained are of interest not only to firms facing a similar problem, but also to DMs or managers dealing with strategic selection processes where the wrong choice of alternatives may lead to increasingly complex sequential disruptions.

EV-Station-Grid Coordination Optimization Strategy Considering Psychological Preferences

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.

Optimization design of heat dissipation scheme for submarine data center based on analytic hierarchy process

Aiming at the problem of heat dissipation optimization design of submarine data center, firstly, the placement mode of server is designed, and on this basis, the heat generation power of server is analyzed by Newton cooling method of convective heat transfer to determine its feasibility. Secondly, based on the relationship between fluid velocity and pressure and the relationship between fluid velocity and heat dissipation through the container surface, the container structure design and its placement direction are clarified. Then, the optimal container shell material is determined by using multi-attribute decision-making model and analytic hierarchy process. Finally, the quadratic composite function of shell cost and seawater depth with respect to shell thickness is constructed to calculate the appropriate seawater depth. According to the research, considering the structure, placement method, heat dissipation effect and seawater temperature of the container, on the basis that a single container shell can be placed with up to 609 servers, Maraging 300 Steel is selected as the most suitable container shell material.

A New Hybrid Decision-Making Model for Promoting Sustainable Social Rental Housing

The realization of sustainable social rental housing is regarded as an important policy to solve the housing burden, but social rental housing is often unsustainable. This study assesses the sustainability of social rental housing. However, the decision-making models, such as the classical decision-making hierarchy (AHP) used in the current study, conflict with the resolution of the complex influence of the relationship between evaluation dimensions/criteria. Based on this, we construct a new hybrid multi-attribute decision-making model (MADM) combining the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Fuzzy DEMATEL-based analytic network process (DANP) and the modified VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for the sustainability assessment of government-led social rental housing (SRH) using Taiwanese experts as the subject of the investigation. The Fuzzy DEMATEL found that economic sustainability (D1), as the core dimension, affects environmental (D3) and social sustainability (D2). Furthermore, criteria with the “cause” position, including the reasonable development model (C2), the provision of living facilities and services needed (C5), the adoption of environmentally friendly technology and equipment (C10), guaranteed rent and lease (C1), the acquisition of working skill and employment opportunities (C6) and space planning and design (C3), should receive more attention from decision makers because improving these can further enhance the sustainability of the overall scheme. Combined with the DANP weight and modified VIKOR, we also indicate that these criteria should be optimized to make the evaluation more sustainable for a selected social housing case in Taiwan. The research also further proposes management strategy implications.

A Multi-Attribute Decision-Making Model for Selecting Centralized or Decentralized Municipal Solid Waste Management Facilities: a Study from the Indian Perspective

A Multi-Attribute Decision-Making Model for Selecting Centralized or Decentralized Municipal Solid Waste Management Facilities: a Study from the Indian Perspective

Prioritizing forest conservation strategies using a multi-attribute decision model to address concerns with the survival of the endangered dragon tree (Dracaena ombet Kotschy and Peyr.)

The globally endangered Dracaena ombet is one of the ten dragon multipurpose tree species in arid ecosystems. Anthropogenic and natural factors are now impacting the sustainability of the species. This study was conducted to prioritize criteria and alternative strategies for conservation of the species using the Analytical Hierarchy Process (AHP) model by involving all relevant stakeholders in the Desa'a dry Afromontane forest, northern Ethiopia. Information about the potential alternative strategies and the criteria for their evaluation were first collected from experts, personal experiences and literature reviews. Afterwards, they were validated using stakeholders' focus group discussions. Five candidate strategies with three evaluation criteria were considered for prioritization using the AHP techniques. The overall priority ranking value of the stakeholders showed that the ecological criterion was deemed as the most essential factor for the choice of the alternative strategies, followed by the economic and social criteria. The minimum cut-off strategy, combining exclosures with collection of only 5% of plant parts from the species, soil and water conservation and habitat protection management options, was selected as the best alternative strategy for sustainable D. ombet conservation. The livelihood losses due to the selected strategy should be compensated by the collection of non-timber forest products, poultry farming, home gardens, rearing small ruminants, beekeeping and agroforestry. This approach may be extended to study other dragon tree species and explore strategies for the conservation of other arid ecosystems.

Pythagorean probabilistic hesitant triangular fuzzy aggregation operators with applications in multiple attribute decision making

As a generalization of fuzzy set, hesitant probabilistic fuzzy set and pythagorean triangular fuzzy set have their own unique advantages in describing decision information. As modern socioeconomic decision-making problems are becoming more and more complex, it also becomes more and more difficult to appropriately depict decision makers' cognitive information in decision-making process. In order to describe the decision information more comprehensively, we define a pythagorean probabilistic hesitant triangular fuzzy set (PPHTFS) by combining the pythagorean triangular fuzzy set and the probabilistic hesitant fuzzy set. Firstly, the basic operation and scoring function of the pythagorean probabilistic hesitant triangular fuzzy element (PPHTFE) are proposed, and the comparison rule of two PPHTFEs is given. Then, some pythagorean probabilistic hesitant triangular fuzzy aggregation operators are developed, and their properties are also studied. Finally, a multi-attribute decision-making (MADM) model is constructed based on the proposed operators under the pythagorean probabilistic hesitant triangular fuzzy information, and an illustration example is given to demonstrate the practicability and validity of the proposed decision-making method.

Role of the e-exhibition industry in the green growth of businesses and recovery

In this paper, a survey and two multi-attribute decision-making (MADM) models have been employed to explore critical success factors of e-exhibition in 30 Chinese provinces that is divided into 8 different regions. The research findings showed that in China, the most important success factors of e-exhibition to have green economic recovery are the presence of International collaboration (0.592), green culture (0.490), and visitor’s attitude (0.439). Furthermore, “Beijing and Tianjin” is the most ideal region to promote e-exhibition in China. South Coast region ranked in second place as the most appropriate region for e-exhibition. The least ideal region of China for e-exhibition is the Southwest region that is less developed compared to other regions of China. The major practical policies are the enhancement of international cooperation to hold an e-exhibition, use of electronic exhibition capacities (synchronous and asynchronous) and creating social sustainability awareness through the media and social network.

Probabilistic double hierarchy linguistic MADM for location selection of new energy electric vehicle charging stations based on the MSM operators

The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper.

Switzerland? The Best Choice for Accommodation in Europe for Skiing in the 2023 Season

The study investigates the connections between tourists‘ hotel preferences and distance from resort expressed in meters, distance from ski lift expressed in meters, booking score, number of reviews, room type, feature of free cancellation, price expressed in Euro, type (private host/hotel), destination (ski-to-door access/travel sustainable property) from 10 highly appreciated European ski locations offered in from February 2023 by Booking, using a sustainable, electronic instrument for collecting and analyzing a large amount of data, Octoparse 8 and a multi-attribute decision model. Previous studies concerning tourist preferences and online behavior used traditional methods, such as questionnaires and surveys, being limited to a certain number of questions and respondents; thus, this study covers a research gap in the literature with regard to the use of a large amount of data, electronic instruments and multi-attribute models to rank the hotel locations, which derives from the difficulty in obtaining the necessary data to carry out an in-depth analysis. The results show that when selecting a hotel location from an exclusive ski resort, the tourists are interested in the number of reviews, the price and the distances from the resorts to the ski slopes, while the booking score is less important. This is translated into practical implications for marketers and hotel managers, presented at the end of the paper.

Associated Statistical Parameters’ Aggregations in Interactive MADM

From recent studies, the concept of “monotone expectation” (ME) of Interactive Multi-Attribute Decision Making (MADM) is well known, which was developed for the case of different fuzzy sets. This article develops the concept of “monotone expectation” for such statistical parameters as variance, k-order moment and covariance. We investigate the problem of the definition of some statistical parameters, when the uncertainty is represented by a monotone measure—a fuzzy measure—instead of an additive measure. The study presents the concept of the definition of monotone statistical parameters based on the Choquet finite integral for the definition of monotone expectation, monotone variance, monotone k-order moment and monotone covariance. Associated statistical parameters are also presented—expectation, variance, k-order moment and covariance—which are defined in relation to associated probabilities of a fuzzy measure. It is shown that the monotone statistical parameters defined in the study are defined by one particular relevant associated statistical parameter out of the total number n! of such parameters. It is also shown that the aggregations with monotone statistical parameters used in interactive MADM models take into account interactions of the focal elements of only one consonant structure from the n! consonant structures of attributes. In order to take into account the interactions of the focal elements of all n! consonant structures of attributes, the monotone statistical parameters were expanded into the F-associated statistical parameters. Expansion correctness implies that if dual second-order Choquet capacities are taken as the fuzzy measures of aggregation of the F-associated statistical parameters, then the F-associated statistical parameters coincide with the corresponding monotone statistical parameters. A scheme for embedding new aggregation operators, monotone statistical parameters and F-associated statistical parameters into the interactive MADM model has been developed. Specific numerical examples are presented to illustrate the obtained results.

Endogenous Performance of Rail Sections in Brazil: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach

This paper investigates the endogenous source of railway performance in Brazil by focusing on several rail sections. A novel Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach is proposed including a multi-attribute decision-making (MADM) model based on type-2 fuzzy sets (T2FS) and a Stochastic Structural Relationship Programming (SSRP) Model based on neural networks. Results suggest that the level of inclination of the rail operators to use the bottlenecked rail sections influences the measurement of idleness, in a chained effect. It is suggested that the balance and coordination among bottleneck, minimum curve radius, and installed capacity are prerequisites to improve railway performance.

Multi-attribute decision-making method based on q-rung orthopair probabilistic hesitant fuzzy schweizer-sklar power weighted hamy mean operator.

In order to further improve the computing power of the information aggregation operator in the q-rung orthopair probabilistic hesitant fuzzy environment, this paper proposes a multi-attribute decision-making method based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator. Firstly, the algorithm of q-rung orthopair probabilistic hesitant fuzzy set is improved based on the Schweizer-Sklar T-norm. In order to better reflect the degree of hesitation of decision-making experts, a new q-rung orthopair probabilistic hesitant fuzzy distance measure is proposed, which provides a basis for subsequent power weighted calculations. Furthermore, considering the correlation between attributes and the influence of data extremes, some information aggregation operators and their power weighted forms are proposed. Finally, a multi-attribute decision-making model based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator is established, and the reliability and validity of the research content in this paper are verified through decision-making examples and comparative analysis.

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.

A web-based integrated Bayesian decision support system for rangeland management in semi-arid watersheds

A Bayesian web-based Decision Support System (DSS) was designed and developed to assist various stakeholders in improving biophysical and socio-economic conditions in a stressed and fragile watershed by systematically evaluating current watershed characteristics, introducing feasible solutions as well as choosing the best ones. The need to deliver a flexible DSS has been the motivation to look for a decision toolbox accommodating the concerns and priorities of a wide range of stakeholders as the potential users. This assists the users to make informed decisions based on quantified potential outcomes. The DSS main components include a database management system, a model base management system, multi-attribute decision-making models, and a graphical user interface. This prototype DSS provides a brief introduction of a pilot watershed characterized with semi-arid conditions in the Mid-north of Iran and then presents the problem structure using the DPSIR approach. A set of vegetation-based solutions and their spatial distribution considering some scenario rules are provided to the DSS users. The various impacts of the proposed solutions are predicted using several models in the Bayesian network modeling context integrating qualitative and quantitative knowledge and data. The DSS developed in this study facilitates knowledge transfer and involvement of stakeholders including practitioners and local communities in the decision making process. This DSS has been designed into a web-based and open-source framework for easier and inclusive access. The DSS can be tailored to fit a broader class of environmental problems.

Some uncertainty measures for probabilistic hesitant fuzzy information

The probabilistic hesitant fuzzy sets (PHFSs), which have received a lot of attention and have been widely used in many domains, are effective in describing hesitant evaluations. Despite significant progress, entropy and cross-entropy, as the famous uncertainty measures of probabilistic hesitant fuzzy information, have not yet been thoroughly investigated. However, these uncertainty measures are quite important and useful in the context of multi-attribute decision-making (MADM). In this contribution, we revisit the concept of uncertainty measures for probabilistic hesitant fuzzy information by comprehensively considering their fuzziness and hesitancy, and proposed some novel entropy and cross-entropy measures for them. Specifically, the hybrid entropy measures are designed as convex combinations of the fuzzy entropy factor and the hesitant entropy factor, and in the same line of thought, the hybrid cross-entropy measures are also designed as convex combinations of the fuzzy cross-entropy factor and the hesitant cross-entropy factor. It is shown that the hybrid entropy measures are simple in structure, clear in physical meaning, and strong in discriminating capability, while the hybrid cross-entropy measures can avoid the design flaws of existing cross-entropy measures and employ natural symmetry. It is further shown that the hybrid entropy and cross-entropy measures can be designed in pairs and are mutually supportive, which suggests that they are internally unified as the uncertainty measures of probabilistic hesitant fuzzy information. In the end, based on the proposed uncertainty measures, an MADM model is developed within the TOPSIS framework to evaluate a company’s strategic initiatives under probabilistic hesitant fuzzy environment. Numerical results show that these uncertainty measures can support group decision-making effectively and efficiently with probabilistic hesitant fuzzy information.

Retracted: Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

Retracted: Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

Application of AHP and MABAC methods in the framework of multi-criteria decision-making in the selection of investment projects

A multi-attribute decision-making process and ranking of investment projects is almost inevitable today in order to realize and achieve an investment project. We can observe the given process from the aspect of decision theory. Today, it is unthinkable to make a decision on the acceptance of an investment project, without looking at different criteria, the only option is to set the choice of an investment project as a multi-attributive decision-making problem. The goal of this paper is to present a multi-attribute decision-making model, when we have several proposed investment projects and when I want to evaluate the given projects and just rank them for choosing the best method. Based on its wide application, the AHP method will be used to determine the weights of the criteria themselves during the decision-making process, while the MABAC method will be used for the further process of ranking investment projects in order to obtain a single-investment ranking of proposed alternatives, more precisely, proposed investment projects.

A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies

A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies