Multi-attribute Decision-making Model Research Articles

Basic uncertainty information hesitant fuzzy multi-attribute decision-making method with credibility

This study addresses the inadequacy of the current quantitative calculation method for decision-maker credibility in hesitant fuzzy multi-attribute decision-making, where credibility is considered. To overcome this limitation, a novel quantitative calculation method for decision-maker credibility is proposed based on the principles of basic uncertainty information theory under a hesitant fuzzy environment. Furthermore, a credible-based hesitant fuzzy multi-attribute decision model is developed. Initially, the paper introduces the concept of a basic uncertainty hesitant fuzzy set by combining basic uncertainty information theory with hesitant fuzzy set theory, thereby enhancing the understanding of basic uncertainty information theory within the realm of non-interval fuzzy information. Building on this foundation, the method for determining the hesitant degree of each element in the basic uncertainty hesitant fuzzy set is provided, followed by the proposed quantitative calculation method for decision-maker’s credibility under the hesitant fuzzy environment, which addresses the lack of a quantitative approach for assessing expert credibility under such circumstances. Subsequently, an attribute weight assignment method is introduced, considering the decision-maker’s credibility, leading to the formulation of a basic uncertainty hesitant fuzzy multi-attribute decision model based on credibility. This model enhances existing hesitant fuzzy multi-attribute decision-making methods that take credibility into account. To validate the proposed approach, the study applies it to the selection of new energy vehicle battery suppliers. The results of the analysis using actual data and sensitivity analysis demonstrate that decision-maker credibility can be quantitatively determined using the proposed method. Additionally, the basic uncertainty hesitant fuzzy multi-attribute decision-making model based on credibility effectively aids in supplier selection. The feasibility and stability of this method are verified through the examination of risk appetite coefficient and hesitancy coefficient.

A novel cross-entropy framework for pattern design quality evaluation based on interval-valued intuitionistic fuzzy multi-attribute decision making

Paper packaging is favored by manufacturers in various industries due to its advantages such as economy and environmental friendliness, and the application of paper packaging in various types of products is also becoming increasingly popular. However, with the growth of the national economy and the increasing purchasing power of the people, consumers not only have higher requirements for the functionality of products, but also have higher requirements for the visual effect of product packaging. Based on the potential consumption trend of “looking for things”, enterprises need to further strengthen the artistic design effect when using paper packaging, promoting the unity of practicality and aesthetics of paper packaging. Pattern design art, as a component of graphic design art, is mainly based on pattern modeling to convey artistic concepts and aesthetic temperament. Integrating pattern design art into the process of paper packaging design can effectively enhance the artistic value of paper packaging, thereby adding color to products. The pattern design quality evaluation is a classical multi-attribute decision making (MADM). In this paper, the MADM methodology is investigated based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for pattern design quality evaluation. First of all, this paper extends the CE method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number cross-entropy (IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for pattern design quality evaluation based on IVIFN-CE algorithm is proposed. In this algorithm, attribute weight is calculated by CRITIC method which is a well-known objective method. At the end of this study, we noticed to a comparison between the proposed IVIFN-CE approach with other existing methods to verify the effectiveness of the algorithm.

Multi-attribute decision-making model based on regret theory and its application in selecting human resource service companies in the post-epidemic era

Multi-attribute decision-making model based on regret theory and its application in selecting human resource service companies in the post-epidemic era

A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theory

Background:The supplier selection problem is a sophisticated decision-making process that involves evaluating multiple factors. While previous research has primarily focused on objective attributes, such as supplier qualifications, product quality, and price, the subjective opinions of users have often been overlooked. However, with the growing importance of user reviews and sentiment analysis in e-commerce, incorporating users’ opinions on supplier products can provide valuable insights. Purpose:This study aims to address the limitations of existing supplier selection approaches by proposing a comprehensive framework that integrates aspect-level sentiment analysis and a fuzzy multi-attribute decision model. The goal is to enhance the decision-making process by considering both objective attributes and subjective opinions. Methods:To achieve this, we develop a novel convolutional neural network (CNN) model with a gating mechanism to perform aspect-level sentiment analysis. Furthermore, we propose a fuzzy multi-attribute decision model that combines the predefined sentiment aspects with traditional evaluation criteria. The model is applied to a dataset specifically designed for automotive component supplier selection. Results:Experimental results demonstrate the superior performance of our approach compared to existing methods and datasets. A case study demonstrates the combination of aspect-level sentiment analysis and the fuzzy decision model allows for a more comprehensive evaluation of suppliers. Conclusion:By integrating aspect-level sentiment analysis and the fuzzy multi-attribute decision model, our proposed framework offers a novel perspective on supplier selection problems. The results highlight the feasibility and superiority of our approach, providing valuable insights for management in making informed decisions. This research contributes to the fields of supplier selection, sentiment analysis, and decision-making, with potential applications in various industries beyond the automotive sector.

Multi-attribute decision-making based on comprehensive hesitant fuzzy entropy

It is unquestionable that hesitant fuzzy set has developed into an important tool for handling uncertain data. As a measure between data, hesitant fuzzy entropy is significant in the decision-making process. However, existing entropy measures have a series of problems such as anti intuition. In the decision-making problem under hesitant fuzzy frameworks, traditional decision-making methods such as TOPSIS method assume that the decision-maker is absolutely rational, which also makes the calculation results unreasonable. Therefore, the present study provides a comprehensive hesitant fuzzy entropy. Additionally, the traditional TOPSIS method is enhanced based on the cumulative prospect theory and the entropy method, thereby generating a multi-attribute decision-making model with unknown attribute weights. First, a new axiomatic definition of hesitant fuzzy entropy is given, then the comprehensive hesitant fuzzy entropy is defined based on the two features of fuzziness and unclarity. Besides, we prove that our method meets the new axiomatic definition. Finally, the improved TOPSIS method is applied to evaluate the comprehensive entropy measure, while simulations verify the effectiveness of the proposed method. The results showed that compared to existing entropy measures, the comprehensive hesitant fuzzy entropy more accurately reflects the uncertainty of hesitant fuzzy elements, and also has the advantages of simple calculation and easy understanding. Meanwhile, the improved TOPSIS method takes into account the psychological preferences of decision-makers, which is more reasonable than traditional methods.

An integrated cross entropy methodology for planning scheme evaluation of highway transportation hub with interval-valued intuitionistic fuzzy information

After entering the 21st century, China’s national economy has shown a rapid growth momentum, the comprehensive transportation system has been continuously improved, the road traffic infrastructure has made remarkable achievements, and the modern logistics industry has also risen rapidly and grown rapidly, which has greatly changed the market demand for road transport hubs. The road transport hub is the main node of the road transport network, the hub of passenger and freight distribution of road transport, and the organizational center for the interconnection of road transport and other transport modes and the development of comprehensive transport. Highway transportation hub is an important part of highway transportation infrastructure and plays an important role in highway transportation. The planning scheme evaluation of highway transportation hub is a multi-attribute decision making (MADM). This paper intends to propose a MADM methodology based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for planning scheme evaluation of highway transportation hub. First of all, this paper extends the cross entropy method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number CE(IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for planning scheme evaluation of highway transportation hub based on IVIFN-CE algorithm is proposed.

Multi-attribute decision-making model based on triangular fuzzy numbers and prospect theory

This article combines the theory of triangular vague numbers and prospects to build a multi -attribute decision -making optimization model, and the application of the decision -making problem of the project construction plan has achieved the choice of the construction plan of the project. Among them, the fuzzy treatment of the scheme attribute value through the vague number of the triangle reflects the uncertainty and risk in the actual project. Through the comprehensive weights composed of the objective weight determined by attribute differences and the subjective weight of Delphi -based, the error of attribute weights is effectively reduced. On the basis of the optimization model of this decision, the BIM technology -related application platform and expert assessment method are applied to determine the reference point of the plan attributes and build attribute expectations. The value function of the prospect theory is combined with the comprehensive right vector calculation of the comprehensive prospective value matrix to achieve multi -plan multi -attribute decision -making optimization.

A multi-attribute decision-making fusion model for stock trading with customizable investor personality traits in a picture fuzzy environment

In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors’ personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors’ personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts.

An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

Evaluation of Sustainability for Coal Consumption Using a Multiattribute Decision-Making Model

The study of coal consumption has become a focus of academic attention due to ecological and environmental concerns. This study proposed a method to evaluate sustainability for coal consumption by using a multiple-attribute decision-making (MADM) model. The data used are from the China Statistical Yearbook and the China Energy Statistical Yearbook over the years. The MADM model consists of attributes (standards), their importance weights, and alternatives. The comprehensive evaluation system for the sustainability of coal consumption, which was composed of four first-grade indicators including coal strength (CS), energy dependency (ED), social development level (SL), and population size (PS) and their corresponding 20 second-grade indicators, was included in the MADM model as attributes (standards). The importance weight of the attributes was determined by the entropy weight method (EWM) and the analytic hierarchy process (AHP), while the comprehensive evaluation of the sustainability of coal consumption in China over the years was considered as an alternative. The evaluation indices of four first-grade indicators were obtained through multiobjective optimization, and then through the weights and evaluation indices of the four first-grade indicators, multiobjective optimization was carried out for the overall objective of the comprehensive evaluation of the sustainability of coal consumption over the years, and the comprehensive evaluation indices of the sustainable development of coal consumption over the years were obtained. The results show that China’s coal consumption is sustainable, with the composite assessment index rising from 0.058 in 2011 to 0.837 in 2021, an increase of 13.4 times.

A study of Global Equity based on AHP & EW Multi-attribute Decision Model

With the shortage of Earth's resources, asteroid mining has received much attention. However, there is no specific policy to address the impact of asteroid mining on global equity, so this paper proposes a targeted mathematical modeling solution-AHP&EM multi-attribute decision model. First, this paper calculates the weights of these different aspects in the model using entropy weights (EW) and hierarchical analysis (AHP), successfully constructs a global stock valuation model, and finds that the global stock index is gradually improving. In addition, this paper designs a scoring model and evaluates the optimal solution using the Pythagorean hesitant fuzzy operator (PFHS) and concludes that the agency model is most beneficial to the growth of the global equity index, while the equity system and tax planning are most suitable for the current model and most beneficial to the global equity.

A novel probabilistic linguistic decision-making model based on discrete evidence fusion and attribute weight optimization

Probabilistic linguistic term set (PLTS) is an effective tool to describe qualitative evaluation, where decision-makers’ hesitation is expressed by multiple linguistic terms and decision-makers’ preferences for possible linguistic evaluations are obtained through probabilistic information. Recently, many multi-attribute decision models based on PLTSs have been developed greatly. However, the existing models fail to consider the interactions among multiple attributes under the framework of intuitionistic fuzzy sets for a decision-making problem given in the form of PLTSs. To solve this problem, a novel probabilistic linguistic decision-making model of discrete evidence is proposed in this paper. Specifically, an optimization model of evidence entropy is established to obtain non-additive attribute weights. Then by using these weights as fuzzy measures, a Choquet intuitionistic fuzzy logit (CIFL) model is defined to acquire decision results. The proposed CIFL model fully takes interactive attributes into account and extends the intuitionistic fuzzy logit (IFL) model. Finally, in a case study of an emergency plan selection, the optimal alternative is chosen correctly with the highest probability by the proposed model. A sensitivity analysis under two entropy functions and four scores, a comparative analysis with seven existing methods and a theoretical analysis are utilized to prove the rationality and validity of the developed model.

The Optimal Path for China to Achieve the “Dual Carbon” Target from the Perspective of Energy Structure Optimization

Exploring the path of energy structure optimization to reduce carbon emissions and achieve a carbon peak has important policy implications for achieving the “Dual Carbon” target. To this end, this paper explores the optimal path for China to achieve the “dual carbon” target from the perspective of energy structure optimization in three steps: (1) we forecast China’s carbon emissions and carbon intensity during 2024–2035 based on a combined forecasting model; (2) we simulate the development of energy consumption and carbon emissions under the “economic development scenario-energy structure scenario” with the help of Markov chain forecasting model; (3) we construct a multi-attribute decision model to account for the above elements as variables to calculate a composite index to analyze the optimal path for China to achieve “Dual Carbon” target under different decision preferences. It is found that (1) potential negative effects caused by COVID-19 are not as serious as reported; (2) only the scenario with low-speed economic growth and effective policies guiding, which doesn’t follow laws of social development, can contribute to reaching carbon peaking by 2030 while maintaining a high-quality carbon intensity; (3) the optimal path that scenario with middle-speed economic growth and strict cost control is a sub-optimal choice subject to realities; (4) technologies innovations in green or low-carbon fields are needed to accelerate energy consumption structure optimization.

Spatial Decision-Making under Uncertainties for Supporting the Prospection of Sites for Hybrid Renewable Energy Generation Systems

This research aims at developing methodological tools for decision makers (DMs) to determine the locations for generating sites for hybrid renewable energy systems, considering complementarity characteristics of the corresponding sources and their seasonal availability. The decision process on new sites starts using Geographic Information Systems for modeling relevant spatial criteria. The problem solution is associated with using the Slide-OWA operator, which permits one to control the inter-criteria compensation levels by adjusting pessimism and optimism parameters. After constructing multicriteria estimates for generating sites, the general scheme of multi-objective decision-making is applied in conditions of uncertainty since these estimates are subject to significant levels of uncertainty. Such a scheme is based on the possibilistic approach, which involves the construction of payoff matrices. At this point, it is possible that some alternatives cannot be distinguished based only on the criteria considered so far, or the DM may want to evaluate the alternatives from the point of view of additional considerations. For this purpose, the framework of multi-attribute decision-making models is applied by employing methods for preference modeling in a fuzzy environment. The present work results are applied to a case study of the Minas Gerais State in Brazil. These results play a strategic role for governments and investors in their decisions while considering the ability to meet a wide range of criteria.

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.

Exponential information measures-driven Pythagorean fuzzy MADM method and its application to new energy battery supplier evaluation problem

Under the Pythagorean fuzzy environment, this paper presents a multi-attribute decision-making (MADM) model based on exponential entropy measure and exponential similarity measure to evaluate new energy battery supplier’s performance. In this method, the notion of Pythagorean fuzzy linguistic sets (PFLSs) is first introduced by combining the linguistic fuzzy sets (LFSs) and the Pythagorean fuzzy sets (PFSs). Then, the axiomatic definitions of Pythagorean fuzzy entropy and Pythagorean fuzzy similarity measure are developed to measure the degree of uncertainty and similarity between two Pythagorean fuzzy linguistic values (PFLVs). The PFLVs can be expressed by the linguistic membership degree (LMD) and linguistic non-membership degree (LNMD). In addition, we construct two new information measure formulas based on exponential function. Through a series of proofs, we verify that they satisfy the axiomatic conditions of entropy and similarity measure of Pythagorean fuzzy language respectively. On this basis, we research the relationship between the two information measures. Finally, we present a novel Pythagorean fuzzy linguistic MADM model. An example for evaluating performance of new energy battery supplier is given to explain the effectiveness of the newly-developed approach. The stability and validity of the newly-developed approach is performed by sensitivity analysis and comparative analysis.

A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem

This study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multi-objective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.

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.