Multi-attribute Decision Research Articles

An advanced approach to operational quality evaluation for industry-finance integration enterprises based on integrated interval-valued intuitionistic fuzzy multi-attribute decision making

With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique.

A novel TODIM-based multi-attribute decision making method under information described by Z-numbers for selecting online B&B

This study designs a new TODIM-based multi-attribute decision making (MADM) method under information described by Z-numbers for selecting online Bed and Breakfast (B&B), which includes extracting the evaluation attributes and obtaining the best online B&B. To do that, first the fine-tuned Bidirectional Encoder Representation from Transformers (BERT) model is used to analyze the sentiment of all online reviews from nine regions of Airbnb, which these reviews about online B&Bs are divided into positive and negative comments. And then Term Frequency-Inverse Document Frequency (TF-IDF) is used to extract attribute feature words of positive and negative comments. Their intersection is sought to determine the attributes of the tourists' online B&B selection. Next, the ranking technique of Z-number and the distance of Z-number are put forward. Finally, a novel TODIM-based MADM method under information described by Z-numbers is proposed to select the B&Bs by the online review of B&Bs obtained from Ctrip.com. The advantage of this method is that it not only considers the reliability of online reviews, but also reflects the psychological factors of tourists. The feasibility of the proposed method is illustrated by ranking the B&Bs, and the flexibility and superiority of the method are highlighted by the sensitivity analysis and comparative analysis.

Modeling project interactions in multiattribute portfolio decision analysis: Axiomatic foundations and practical implications

A common approach to modeling project interactions in multiattribute project portfolio selection is to augment the additive portfolio utility function, in which portfolio utility is the sum of the included projects’ utilities, with additional terms representing the synergy/cannibalization effects triggered by selecting specific subsets of projects. In this paper we develop a set of sufficient and necessary assumptions for representing preferences among multiattribute project portfolios with a quasi-symmetric multilinear utility function and show how this function gives rise to interpreting interaction effects as additional terms in the additive portfolio utility function. To foster practical applicability of these theoretical contributions, we also develop techniques to elicit such portfolio utility functions as well as optimization models to identify the feasible portfolio that satisfies relevant resource and other constraints with the maximal expected utility. In recognition that incorporating project interactions necessitates increased involvement of decision makers in assessing the interaction effects and results in computationally more challenging portfolio optimization problems, we analyze the importance of modeling interactions through series of simulation studies based on randomly generated and real-world data sets. Specifically, we examine the impact that omitting project interactions has on the project-level decision recommendations and on the expected utility of the recommended portfolio.

A novel group decision-making method for interval-valued q-rung dual hesitant fuzzy information using extended power average operator and Frank operations

This paper advances the field of multi-attribute group decision making (MAGDM) by proposing a novel framework based on interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs). IVq-RDHFSs, which surpass most existing fuzzy sets, effectively represent complex fuzzy information by describing membership and non-membership degrees through interval value sets. However, prior MAGDM methods based on IVq-RDHFSs have been limited by the functions of operation rules and aggregation operators (AOs). This limitation is addressed through the construction of a new MAGDM framework, leveraging the robust Frank t-norm and t-conorm (FTT) operation and the extended power average (EPA) operator. The proposed framework features the interval-valued q-rung dual hesitant fuzzy Frank weighted extended power average (IVq-RDHFFWEPA) operator to obtain comprehensive evaluation values. The paper also introduces novel techniques for determining the weights of decision-makers and attributes. Practical applications of the proposed method are demonstrated through the assessment of desalination technology selection and rural green eco-tourism projects. Sensitivity and comparison analyses validate the superior functionality, accuracy, and flexibility of this method compared to many state-of-the-art methods. The contributions of this paper are two-fold: it develops efficient measurement techniques for IVq-RDHFSs, such as distance and weight calculation, and it introduces a comprehensive MAGDM method by integrating FTT and EPA under IVq-RDHFSs, which improves the efficiency of solving decision-making problems.

A similarity-based three-way multiattribute decision model under constrained Pythagorean fuzzy environment

A variety of fuzzy sets, especially Pythagorean fuzzy sets (PFSs), are commonly utilized to describe the ambiguity of information in three-way decision (3WD) models. PFSs can handle fuzzy information well but ignore the description of stochastic information. To address this issue, in this paper, we attempt to construct a novel three-way multiattribute decision model by combining 3WD with constrained Pythagorean fuzzy sets (CPFSs) which can measure the reliability of PFSs. Firstly, we introduce the concept of constrained Pythagorean fuzzy information systems (CPFISs). Comprehensive positive and negative similarities of objects are given in a CPFIS by introducing normalization, score function, comparison rules, and positive and negative ideal solutions. Secondly, by comprehensive positive and negative similarities, novel formulas for calculating comprehensive relative utility function values are obtained and an objective method for estimating conditional probabilities is presented in the absence of decision attributes. Based on the above knowledge, we establish a similarity-based three-way multiattribute decision model in a CPFIS. Furthermore, we conduct a case analysis of appendicitis to realize the application of our proposed model and several comparative analyses to demonstrate the effectiveness and superiority of our model. Finally, the good performance of the new model is verified through experimental analyses.

Flow performance evaluation and structural optimization of the dividing manifold systems using multi-attribute decision analysis

Dividing manifold systems with parallel pipe arrays (DMS-PPA) are commonly used in building engineering for fluid transmission and distribution. However, it is often difficult to ensure a uniform flow distribution for each branch pipe in engineering applications, and the flow performance of manifold systems has seldom been evaluated considering both flow distribution and resistance characteristics. Therefore, this study aims to improve the flow performance and optimize the manifold configuration of the dividing manifold systems using a multi-attribute evaluation approach. The results show that the flow non-uniformity coefficient (Ф) and the dimensionless pressure drop (ΔPj/Ppref) indicator have an equivalent degree of influence on the flow distribution (minF=0.5×Φ+0.5×ΔPj/Ppref). The optimal design interval of the inlet/outlet area ratio (AR) for DMS-PPA is recommended as [1.4400, 1.7778] under the numbers of branch pipe N = 2, 3, 4, and 5. In addition, a novel spherical dividing manifold system with bifurcated pipe arrays (SDMS-BPA) was proposed for structural optimization, which can effectively improve flow performance under the condition of equal manifold volumes. The flow distribution uniformity has been improved by nearly 10 times compared to DMS-PPA when the branch pipe angle θ = 15°, spherical diameter ratio φmanifold, s/φin = 4.53, and the radius of spherical manifold l/R = 1. Despite introducing additional flow-turning losses, the total pressure drop in the system increased by approximately 25 %. This study provides an innovative solution and scientific guidance for achieving flow performance improvement and design optimization for dividing manifold systems in building engineering.

Approach to Multi-Attribute Decision Making Based on Spherical Fuzzy Einstein Z-Number Aggregation Information

Spherical fuzzy sets are an enhanced framework of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), and picture fuzzy set (PFS) with the restriction that the total square sum of the membership, indeterminacy, and non-membership degrees must be in 0 and 1. In contrast, the Z-number, a revolutionary idea that captures both the restriction and the reliability of evaluation, is more significant than fuzzy numbers in the fields of decision-making (DM), risk assessment, etc. However, there are still few and insufficient discussions of how to effectively deal with the limitations and reliability of the literature currently in existence. To address this, we first introduced the spherical fuzzy Einstein Z-numbers (SFEZNs), those elements are pairwise comparisons of the decision-makers options. It can be used effectively to make truly ambiguous judgments, reflecting the fuzzy nature, flexibility, and applicability of decision-making data. We present the spherical fuzzy Einstein Z-number weighted aggregation operators and the spherical fuzzy Einstein Z-number weighted geometric operators. We develop a model for spherical fuzzy Einstein Z-number aggregation operators. The main focus of this study is on a technique for handling the issue of multi-attribute decision-making (MADM) effectively and based on one's preferences. We also developed the algorithms for ranking the best options. Finally, we developed the relative comparison and discussion analysis to show the practicability and efficacy of the suggested operators and approaches. The study's findings and implications are discussed.

Multi-scale decision systems with test cost and applications to three-way multi-attribute decision-making

Multi-scale decision systems with test cost and applications to three-way multi-attribute decision-making

Three-way decisions based multi-attribute decision-making with utility and loss functions

The traditional multi-attribute decision-making (MADM) methods are based on two-way decisions (2WD), i.e., acceptance and rejection. In contrast, three-way decisions (3WD) add a deferred decision to effectively handle MADM problems by reducing decision risks. In the past 3WD models have been broadly explored considering losses and utilities associated with the decisions, but none of the studies considered loss and utility together. Also, the existing studies lack the influence of decision-makers on outcomes. Accordingly, this paper presents a novel 3WD model based on a similarity measure, integrating the hybrid information of MADM matrix, utility and loss values along with the influence of decision-makers. First, the two states are formed by the fuzzy c-mean clustering algorithm. Second, the similarity class for each alternative is constructed based on the Jaccard index. With reference to the equivalence classes formed, conditional probabilities are further calculated and 3WD decision rules are studied. Finally, considering both expected utility and losses associated with the alternatives we propose a novel 3WD model for solving MADM problems. The effectiveness of the proposed approach is demonstrated with the help of an illustrative example. The proposed 3WD-MADM model is further verified from different perceptions through comparative and experimental analysis.

Evaluating Agriculture Robots: Case Study and Analysis

The technical revolution paved the way for the development of robots that can now operate in a variety of areas. The smart farming technique called robotic agrifarming has the potential to alleviate the worldwide labor shortage while increasing agricultural productivity and food safety. From planting to harvesting, agribots perform field operations that enhance soil quality and ensure long-term growth. That's why it's essential to create a decision-support system that helps choose the best robot for an agricultural task. The goal of this study is to create an integrated decision system for assessing agriculture robots using the method based on removal effects of criteria (MEREC). Founded on a lot of alternatives like project viability, ecosystem aptness, community conditions, governmental embracing, and profit, the performance of seven field robots is evaluated. The advantage of the developed ranking technique is that it follows the additive ratio assessment (ARAS) method, which is indeed a specific technique used for multi-attribute decision-making (MADM), particularly in the field of environmental management and evaluation. It's commonly employed when there are multiple factors or criteria to consider in decision-making processes and prioritizing the alternatives.

Complex linear Diophantine fuzzy sets and their applications in multi-attribute decision making

This study utilizes the concept complex linear Diophantine fuzzy set (CLDFS) which is an advanced framework for handling ambiguity in the presence of periodicity for decision-making. Unlike existing techniques, CLDFS offers a larger solution space, incorporating reference parameters α and β. Two real-life scenarios – bridge design selection and AI system choice – are examined to showcase CLDFS’s impact on practical applications. The study presents multi-attribute decision-making (MADM) and multi-attribute group decision-making (MAGDM) algorithms with visualizations, applied to warehouse distributor selection and faculty candidate evaluation. The comparative analysis highlights CLDFS’s advantages over other methods

A novel B&B recommendation method based on improved intuitionistic fuzzy sets

PurposeThis paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.Design/methodology/approachThis paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.FindingsA case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.Originality/valueProposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Threshold optimization of task allocation models in human–machine collaborative scoring of subjective assignments

In the exploration of human–machine collaborative scoring of subjective assignment (HMCSSA) problems, it is crucial to note that the workloads assigned to humans and machines are determined by thresholds at various levels of granularity within the human–machine task allocation model. To address this issue, a three-phase framework for optimization and decision making in HMCSSA problems is constructed by combining multi-objective evolutionary algorithms (MOEAs) with multi-attribute decision making method. Specifically, we present a bi-objective threshold optimization model to achieve a trade-off between human costs and scoring fairness in HMCSSA problems. Moreover, four well-known MOEAs are employed for solving the optimization model to search the Pareto optimal thresholds. Meanwhile, the technique for order of preference by similarity to ideal solution (TOPSIS) method is utilized to determine the best human–machine task allocation schemes based on the preferences of decision makers. The numerical experiments are conducted on eight prompts of the ASAP data set to validate the effectiveness and superiority of the proposed framework. In particular, the best task allocation scheme attains the fairness gain of 53.76% with the human participation rate of 21.77% on average.

Climate-change-induced risk mapping of the Indian Himalayan districts using the latest IPCC framework

Mountain regions worldwide, notably the Hindu Kush Himalayas (HKH) encompassing the Indian Himalayan Region (IHR), face considerable vulnerability to various hazards resulting in human and livestock casualties, financial losses, and damage. The primary objective of this study is to construct a climate-change-induced risk index specifically for the districts within the IHR. The concept of risk herein refers to the composite of hazard, vulnerability, and exposure, aligned with the current framework of the Intergovernmental Panel on Climate Change (IPCC). Utilising the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a multi-attribute decision-making (MADM) approach, the research integrates indicators to generate indices pertaining to hazard, vulnerability, and exposure across 109 districts in both the Western Indian Himalayan Region (WIHR) and the Eastern Indian Himalayan Region (EIHR). The study relies on secondary data sourced from various openly accessible government reports and websites. Key findings include: (1) The WIHR exhibits a higher overall risk compared to the EIHR. (2) The WIHR demonstrates elevated rankings in hazards and exposure indices. (3) The EIHR exhibits a relatively higher vulnerability index. This paper presents a pioneering contribution to the limited body of literature on risk assessment by offering one of the initial attempts to rank administrative districts pan-IHR based on the latest IPCC criteria. Moreover, the assessment carries significant implications for policymakers, as it is critical to identify the districts vulnerable to climate change and the extent of their vulnerability before addressing climate-induced risk.

A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea

To effectively address the increase in maritime accidents and the challenges posed by the trend toward larger ships for maritime safety, it is crucial to rationally allocate the limited maritime search and rescue (MSAR) resources and enhance accident response capabilities. We present a comprehensive method for allocating MSAR resources, aiming to improve the overall efficiency of MSAR operations. First, we use long short-term memory to predict the number of future accidents and employ the K-medoids algorithm to identify the accident black spots in the studied area. Next, we analyze the multi-constraint conditions in the MSAR resource allocation process. A multi-objective integer programming model is constructed to minimize the response time and allocation cost. Finally, we use the non-dominated sorting genetic algorithm II (DNSGA-II) with Deb’s rules to solve the model, and we propose a multi-attribute decision optimization-based method for MSAR resource allocation. We found that the DNSGA-II exhibits better convergence and generates higher-quality solutions compared to the NSGA-II, particle swarm optimization (PSO), and enhanced particle swarm optimization (EPSO) algorithms. Compared with the existing MSAR resource emergency response system, the optimized scheme reduces the response time and allocation cost by 11.32% and 6.15%, respectively. The proposed method can offer decision makers new insights when formulating MSAR resource allocation plans.

A new decision analysis framework for multi-attribute decision-making under interval uncertainty

In decision-making analysis, the description on decision makers' risk attitude under uncertainty is a focus topic. This paper presents a novel decision analysis framework that considers uncertainty and risk using the cumulative prospect theory. The proposed approach describes uncertain preference information using interval-valued fuzzy sets, maintaining this representation without converting intervals into crisp numbers throughout the entire process. To quantify the distance between interval-valued fuzzy sets, we introduce a comprehensive interval-valued distance model that accounts for multiple situations determined based on the relative positions between intervals. Afterwards, some related theorems about the proposed interval-valued distance model are explored mathematically. Then, with the aid of different reference points and interval-valued distance model, the overall interval-valued prospect value of each alternative is investigated. Finally, an Enhanced Minimax Regret-based (EMR) approach is developed to compare and rank the obtained overall interval-valued prospect values. An illustrative example, accompanied by detailed discussions, is provided to demonstrate the flexibility and superiority of the proposed decision analysis framework.

An augmented fuzzy decision support system to analyse compatible cosmetic face masks for various complexions

AbstractBeauty face masks (BFM) are becoming increasingly popular among both men and women since they provide quick refreshment and nurture the skin. Given the wide range of skin types and the chemicals used in their formulation, it can be difficult to find a product that not only complements the skin type but is also free of potentially harmful ingredients that could endanger the consumer's health. When dealing with ambiguous situations, the multi‐attribute decision making (MADM) approach combined with fuzzy set theory is more effective. Type‐2 fuzzy sets (T2FS) provide greater flexibility in dealing with uncertainty in real‐world issues since they are characterised by a main and secondary membership function. In this research, we present the innovative idea of type‐2 linear diophantine fuzzy set (T2LDFS) as an intriguing tool for capturing expert reluctance about an issue. For analysing the discussed problem, a hybrid fuzzy VIKOR enhanced with the proposed fuzzy logic is suggested. A sensitivity and comparative analysis is carried out to establish the validity of the recommended approach.

A sustainable modelling for solid waste management using analytical hierarchy process, Monte Carlo simulation and NSGA-III

The issue of solid waste management is one of the greatest challenges of any society. Instead of leveraging solid waste's potential to produce energy using waste-to-energy (WTE) technologies, different countries especially developing countries stick to their ineffective and inferior methods to dispose of solid wastes. To fill this gap between energy production and waste management, this paper develops a novel methodology that integrates three efficacious tools, including Monte Carlo simulation, multi-attribute decision-making, and mathematical optimisation modelling to solve the problem from the perspective of economy, energy recovery, environment (3E), and social sustainability. In this paper, a multi-attribute decision-making method, namely AHP, is investigated to select the most suitable emerging WTE technological options as well as the optimal waste collection routes, to capture solid waste as a potential renewable energy source. Monte Carlo simulation and beta-PERT distribution are also applied to reduce the uncertainty intrinsic to decision-making. After validating the proposed methodology in a real-world case study of Tehran city, we apply a novel meta-heuristic algorithm namely NSGA-III to solve the problem on large scales with a reasonable computational time. The performance of this algorithm in solving waste location-routing problems is explored by conducting several trials. Highlights Addressing an integrated waste-to-energy and transportation network for solid waste management. Developing a novel methodology for finding the best waste-to-energy technological alternatives with respect to the sustainable criteria. Finding the high-quality set of waste collection routes, in addition to the location of facilities with the consideration of waste collection in a separated manner and vehicle's fuel consumption and emission in the MIP model. Monte Carlo simulation and beta-PERT distribution are proposed to stochastically model the absence of accurate data. Application of NSGA-III algorithm to handle the solid waste location routing problem.

Graph model for conflict resolution based on the combination of probabilistic uncertain linguistic and EDAS method

The ranking of decision makers (DMs)’ preferences for feasible states in the graph model for conflict resolution (GMCR) is crucial for accurately determining stability results. This paper addresses the issue of subjective ranking methods lacking theoretical foundation and causing ambiguity when the number of feasible states is high by proposing the implementation of the multi-attribute decision-making (MADM) method in the GMCR. The paper utilizes the average level to choose evaluation based on distance from average solution (EDAS) method for determining the DM’s preference ranking, which can effectively reduce the impact of anomalous evaluations. Further, the PUL-EDAS method based on probabilistic uncertainty linguistics (PUL) is developed, which overcomes the shortcomings of the traditional EDAS method, which only applies to the simple evaluation of information. The PUL aligns with DMs’ daily evaluation practice by providing an interval for the quality of qualitative linguistic evaluations. Furthermore, it utilizes an objective aggregation method to calculate comprehensive evaluation information from all DMs. In addition, the four fundamental stability definitions, applicable solely under crisp preferences, are extended to the PUL context, providing related extended definitions. Finally, to ensure the scientific validity and practicality of the proposed theory, this paper selects digital rural governance as the research context for conflict calculus analysis, comparing it with other MADM methods in the preference ranking section.

QNN-MAGDM strategy for E-commerce site selection using quadripartition neutrosophic neutrality aggregative operators

In this paper, we define the Quadripartition Neutrosophic Weighted Neutrality Aggregative (QNWNA) operator and Quadripartition Neutrosophic Ordered Weighted Neutrality Aggregative (QNOWNA) operator for solving Multi-Attribute Group Decision Making (MAGDM) problems. The basic properties of both operators are discussed thoroughly. A new MAGDM strategy is developed using these developed operators. A case study of e-commerce site selection in India is discussed to show the applicability of the proposed MADM strategy. Moreover, the performance of the QNWNA and QNOWNA operators are compared with Quadripartition Neutrosophic Weighted Arithmetic Aggregation (QNWAA) operator and Quadripartition Neutrosophic Weighted Geometric Aggregation (QNWGA), Quadripartition Single valued Neutrosophic Dombi Weighted Arithmetic Aggregation (QSVNDWAA) and QSVN Dombi Weighted Geometric Aggregation (QSVNDWAA) operator.