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Enterprise resource planning (ERP) adoption literature has a consensus that selecting the right ERP system is one of the most critical success factors in the ERP adoption lifecycle. While choosing a non-fitting ERP system may lead to adoption failures, however very few papers focus solely on this selection phase. Hence, given the criticality of the ERP selection phase, this paper aims to identify and review the different ERP selection methods in extant literature. This research also presents the factors and variables included in each identified selection method in ERP literature. As a result, each method identified was reviewed, analyzed, and summarized. Our main findings suggest that ERP selection is a multi-criteria decision-making (MCDM) problem, with various methods and techniques that can be utilized for such problems. Several MCDM methods have been used in literature, but often complementing more than one method combined at a time. This is since some methods excel in considering factors in uncertain environments, and other methods are best in evaluating qualitative and quantitative factors. Finally, while there are some methods that were used for cloud-ERP selections, there is no clear consensus in extant literature if some methods could best fit specifically cloud-ERP contexts in contrast to on-premises counterparts. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance. • A new information processing mechanism is proposed to transform online reviews while considering missing information. • A new interval weight determination model is defined. • The evidential reasoning algorithm is extended to fuse online reviews and generate the interval-valued expected utilities. [ABSTRACT FROM AUTHOR]

With the constant changes in the decision-making environment, various limitations have emerged in traditional decision-making methods, such as the inability to simultaneously consider multiple factors such as randomness, hesitation, and ambiguity, thus limiting a comprehensive grasp of the decision-maker's cognitive inputs. To address the above problems, we propose a multi-criteria decision-making (MCDM) model based on a new fuzzy set (FS). Firstly, we define a new Z hesitant fuzzy linguistic term set (ZHFLTS) and propose a visualization metric for mapping ZHFLTS into T-spherical fuzzy (T-SF) space to simplify the computation. The relevant properties of ZHFLTS are further investigated. In order to accurately measure the uncertainty of FSs, we propose the probabilistic-hesitation-fuzzy (PHF) entropy and use it as a basis to derive the potential probability distribution using the fruit fly optimization algorithm (FOA). Subsequently, an MCDM model is established based on the proposed ZHFLTS, visualization metric, and PHF entropy. The model can better solve complex decision-making problems such as TCM through example studies and comparative analyses. The research in this paper aims to cope with today's increasingly complex decision-making environments, and the introduced ZHFLTS framework and the corresponding methodology provide new ways to solve complex decision problems. • ZHFLTS is defined and a mapping method to visualize ZHFLTS in T-SF space is studied. • PHF entropy is proposed based on FOA for obtaining potential probability distribution. • A Novel MCDM model based on ZHFLTS is presented. • It proves the effectiveness of the model through instances and comparative analysis. [ABSTRACT FROM AUTHOR]

While uncertainty quantification (UQ) has served a prominent role in ensuring the safety of dynamical engineering systems, the lack of an integrated approach to handle the aleatory nature of ground motion records, a.k.a., record-to-record (RTR) variability, remains a bottleneck in seismic design. This paper presents a novel approach with two key features. First, on the multi-criteria decision-making (MCDM) front, a general-purpose collective decision support system is introduced by integrating Monte Carlo simulation, automated machine learning (AutoML), and a hybrid fuzzy-outranking MCDM technique. This allows for robust uncertainty capture at a group level without compromising computational efficiency—a departure from the traditional trade-off between MCDM competency in uncertainty handling and computational burden. Second, from an Earthquake Engineering perspective, the established AutoML-based community-level MCDM approach is combined with an efficient metamodel-aided reliability-based design optimization technique and a novel probabilistic-fuzzy seismic-design safety index. Conflicting criteria are classified into structural performance, design safety, and construction cost metrics. The results indicate that the developed intelligent seismic design paradigm properly captures uncertainties rooted in the RTR variability, input data, criterion weighting, and decision-makers' preferences from a community-level standpoint. It also offers acceptable error metrics during the ranking procedure in a user-friendly environment, at a significantly reduced computational expenditure. Moreover, it can be concluded that there is no imperative need for developing complex decision tools for robust-to-uncertainty seismic design. The implementation of the proposed MCDM-based UQ framework has reduced the design cost of an existing dam by 17%. [Display omitted] • A general-purpose stochastic-fuzzy multi-criteria decision-making tool is introduced. • An MCDM-based uncertainty quantification tool is suggested for robust seismic design. • A probabilistic-fuzzy seismic-design safety index captures ground motion variability. • An automated machine-learning pipeline reduces computational decision-making costs. • A hybrid stochastic-fuzzy MCDM dataset for earthquake/risk engineers is developed. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new "Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency" (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, "Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method" (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C 21 latency and C 22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C 15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including "virtualized cloud RAN" coming as the optimal one, followed by "fog RAN," "cloud RAN," and finally "heterogeneous cloud RAN." The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications. [ABSTRACT FROM AUTHOR]

Hospital wayfinding systems that are based solely on signage do not provide adequate solutions for wayfinding needs, especially for users with impairments. Moreover, the interaction between user characteristics and the inner space of the building also determines wayfinding efficiency. The aims of this study, therefore, were to identify architectural features that affect spatial orientation and wayfinding behaviors; demonstrate the implementation of a multi-criteria decision-making (MCDM) approach for improving wayfinding in a diverse range of users; and produce a set of quantitative values (i.e., weights) for each selected architectural feature, based on the individual's preferences. Doing so could enable the formulating of practical design guidelines for hospital buildings, tailored to the needs and abilities of the users, to minimize disorientation and confusion – as demonstrated in this paper through a case study. The MCDM approach was chosen as it is based on observations whereby wayfinding resembles a continuous decision-making process, throughout which, users continuously select those architectural features that they perceive as having the greatest wayfinding value. • Summarized the effects of architectural features on wayfinding behaviors. • Demonstrated multicriteria decision-making processes for improved wayfinding. • Designed inclusive wayfinding systems for a diverse range of users. • Illustrated a process for generating routes that minimize users' disorientation. [ABSTRACT FROM AUTHOR]

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves decision makers' (DMs') interaction by incorporating their preferences to arrive at one or more preferred near Pareto-optimal solution(s). The Pareto-Race is an interactive MCDM approach that relies on finding preferred solutions as the DM navigates on the Pareto surface by selecting the preferred reference direction and uniform/varying speed using the concept of Achievement Scalarization Function (ASF) or its augmented version (AASF). Selecting a reference direction or speed is possible, algorithmically, but the effectiveness of the Pareto-Race concept relies on suitable visualization methods that present the series of past solutions and convey the current objective trade-off information to assist the DM's in decision-making task. Traditional multi-dimensional Pareto-optimal front visualization methods like parallel coordinate plots, radial visualization, and heat maps are deemed insufficient for this purpose. Therefore, the paper proposes integrating the concept of the Pareto-Race MCDM approach with a recently developed interpretable self-organizing map (iSOM) based visualization method. The iSOM maps high-dimensional data into lower-dimensional space, facilitating visual convenience for the DM. The proposed method requires a finite-size representation of non-dominated solutions near the complete Pareto Front to generate iSOM plots of objectives. We also propose visualizing the metrics such as closeness to constraint boundaries, trade-off value, and robustness using iSOM to check the quality of the preferred solutions, which is rarely considered in existing MCDM approaches. iSOM plots of objective functions, closeness to constraint, trade-off, and robustness metrics assist the DM in effectively choosing new reference direction and step size in each iteration to arrive at the most preferred solution. The proposed iSOM-enabled Pareto-Race approach is demonstrated on benchmark analytical and real-world engineering examples with 3 to 10 objectives. [Display omitted] • Interactive MCDM using iSOM-aided Pareto-Race technique. • Introducing metrics such as closeness to constraint and robustness in Pareto-Race. • Visual comprehension of goodness of preferred solution and features in each iteration. • Visualization-based selection of step size and direction of search in Pareto-Race. • Demonstration on 3 to 10-objective analytical and real-world engineering examples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main developments on sorting approaches. • The categorization of multiple-criteria decision-making sorting methods. • Applications of the multiple-criteria decision-making sorting methods. • Trends and future directions of MCDM Sorting. Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods' trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research. [ABSTRACT FROM AUTHOR]

The neutrosophic fuzzy set (NF-set) is a unique hybrid structure that combines the essence of the fuzzy and neutrosophic sets. It is an effective mathematical tool for dealing with indeterminate and inconsistent information in situations where the data are imprecise or vague. This paper defines the concepts of similarity-based information measures, including entropy and cross-entropy, between NF-sets. It is the first time these concepts have been introduced since the NF-sets were defined. We provide their interesting properties through well-proven theorems. In addition, we also propose a novel and efficient algorithm to make multi-criteria decision(s) using these information measures with a clear step-by-step illustration. It overcomes the limitations of the original algorithm when evaluating the criteria in two aspects: qualitative and quantitative. A real-world experiment is then conducted to assist students in choosing the optimal subject group(s) for the Vietnamese national high school graduation examination. Experimental results show that our method outperforms the previous original method when giving recommendations with sixteen correct cases, three acceptable cases, and one noisy case out of twenty real-life cases. Finally, the experimental results are presented visually, analyzed rigorously, and discussed carefully, intending to verify the validity and feasibility of the proposed algorithm. The Python source code for experiments is publicly available on Github. [ABSTRACT FROM AUTHOR]

Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired by the behavior of a colony of real ants and uses their pheromone trials to find optimal solutions. Since the beginning of the ACO algorithm, many researchers have tried to improve the performance and stability of the algorithm by using various methodologies. Resolving the exploitation/exploration dilemma by an efficient procedure is critical in improving the ACO. One of the critical parameters in ACO is selecting the heuristic that can affect the movements of ants. So far, the use of several heuristics in ACO has not been studied. We believe that using multiple heuristics instead of a single heuristic can improve the ACO algorithm. For this matter, we have proposed an ACO algorithm based on the ensemble of heuristics using a Multi-Criteria Decision-Making (MCDM) procedure. It means that the movement of the ants is defined based on the judgment of multiple experts (criteria). The idea is based on the hypothesis that different heuristics give us more information about the subsequent nodes, and the variety of these methods examines the different aspects to achieve better and optimal solutions in ACO. In this paper, we have applied our proposed method to the ensemble feature selection task to evaluate the performance of the proposed method. Blending several feature selection methods is regular to tackle the feature selection problem, and also efficiently combining feature selection methods is still challenging. Some well-known ensemble feature selection and primary feature selection methods have been compared with Ant-MCDM on twelve datasets to evaluate the performance of the proposed method in the feature selection task. • We proposed an improvement of the ACO algorithm based on the ensemble of heuristics. • The ensemble of heuristics is modeled as an MCDM process. • The proposed method is applied in ensemble feature selection problem. • For the ensemble process, a combination of relevancy and redundancy methods is used. • The proposed method outperforms competitive methods. [ABSTRACT FROM AUTHOR]

• A new form of the ranking based on optimal points MCDM method is developed. • A new weighting method is developed called Win, Loss, Draw method. • RBOP imitates the decision maker's behavioral pattern to find the best alternative. Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting method s called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process. [ABSTRACT FROM AUTHOR]

• Define Choquet integral based intuitionistic fuzzy arithmetic aggregation operator. • Combine the defined operator with ordered weighted aggregation operator. • Propose a decision-making method based on the combined operator. Intuitionistic fuzzy set (IFS), a classical extension of fuzzy set, is a powerful tool to describe fuzzy information in multi-criteria decision-making (MCDM) because of its membership and non-membership functions. Intuitionistic fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to criteria into one unique output. The traditional intuitionistic fuzzy aggregation operator-based methods have been criticized because of reasons that include disregard for comprehensive correlative relationships of criteria and ignorance for combination of fuzzy measures of criteria and weights of positions. In this paper, the effectiveness of traditional aggregation operator-based MCDM techniques is improved by a novel method to select the best alternative(s) under the intuitionistic fuzzy environment. We begin by defining the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator, the remarkable properties of which are proved in details. Further, the Choquet integral based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator is defined as well as the proofs of its properties. The CIIFHAA operator can not only capture the comprehensive correlative relationships of criteria in a simpler manner, but also combine the weights of positions. Then, we put forward a MCDM method based on CIIFHAA operator with intuitionistic fuzzy evaluations. An illustrative example is solved, the best alternative and the ranking of alternatives are obtained, which can verify the feasibility of the proposed method. Finally, two comparisons are conducted to illustrate the stability and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Internal audit is an independent and objective assurance and consulting activity that aims to improve the operations of an organization and add value to them. Planning internal audit by prioritizing the units to be audit is critical in terms of effective use of available audit and financial resources. In this paper, a new ELimination and Choice Translating Reality (ELECTRE) based decision support model is developed for addressing an internal audit prioritization problem. Spherical fuzzy sets are used for modeling the uncertainty in the nature of the problem and three different approaches are proposed within the study. The first approach is constructed with gradual concordance and discordance sets by comparing spherical fuzzy membership, non-membership, and hesitancy degrees of alternatives; the second approach is developed based on a single type of outranking relation obtained by utilizing score and accuracy functions of spherical fuzzy sets, and the third approach provides an increased fuzziness modeling capacity by using interval-valued spherical fuzzy sets. In the application part of the study, the units of an organization are prioritized for internal audit activity based on five components of the internationally recognized Committee of Sponsoring Organizations (COSO) framework. Sensitivity analyses for decision-maker and criterion weights and a comparative analysis with six other state-of-the-art multi-criteria decision making (MCDM) models are also presented to analyze the consistency and validity of the proposed spherical fuzzy ELECTRE model. [Display omitted] • ELECTRE method is extended with single and interval-valued spherical fuzzy sets. • Score and accuracy functions are used to construct outranking relations. • The proposed model is applied for prioritizing units for internal audit activity. • COSO internal control framework is used to obtain criteria set. • Extensive sensitivity and comparative analyses are provided. [ABSTRACT FROM AUTHOR]

As a result of the increased demand for energy combined with the limitations of traditional energy sources, it is necessary to turn to renewable energy sources such as solar energy, which is one of the most promising technologies to assist the transition towards sustainable development. With the increasing interest in renewable energies in general and solar energy in particular, attempts have been made to ensure that solar energy technologies provide an amount of energy equal to or close to the amount of energy currently consumed. Solar photovoltaic farm applications have become so widespread that they are now relied upon to produce massive amounts of energy for use in factories and homes. In this regard, identifying and prioritizing convenient areas for the construction of expensive solar photovoltaic farms is an important issue. Additionally, determining the suitable geographical locations for photovoltaic farms is not only related to the amount of solar radiation received, but many dimensions must be taken into accounts such as technical, economic, environmental, socio-political dimensions, risks, and their sub-indicators. To overcome the problem of prioritizing and selecting the best location for solar photovoltaic farms to achieve sustainability factors resulting from the presence of many different indicators, this paper applies a hybrid multi-criteria decision-making approach under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators and available alternatives. The proportional importance of the designated dimensions and their sub-indicators was evaluated by the Decision-Making Trial and Evaluation Laboratory. Then, Vlse Kriterijumska Optimizacija Kompromisno Resenje is adopted to rank seven solar photovoltaic farm locations in Egypt. The Benban solar photovoltaic farm is shown to be the most sustainable alternative among the solar photovoltaic farm locations followed by Darb Al Arbaeen solar photovoltaic farm and so has priority for increased installation investments in solar energy production. To validate the proposed approach, results comparisons were implemented to indicate the advantages of the hybrid multi-criteria decision-making methods. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order. [ABSTRACT FROM AUTHOR]